Open_SLM_Leaderboard / index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Open SLM Leaderboard</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,300;0,9..40,400;0,9..40,500;0,9..40,600;0,9..40,700;1,9..40,400&family=Space+Mono:wght@400;700&display=swap" rel="stylesheet">
<script src="https://cdn.jsdelivr.net/npm/chart.js@4"></script>
<style>
:root {
--leader-gold: #d7bd83;
--leader-gold-hover: #f0dca8;
--bg: #0a0a0b;
--bg-alt: #111113;
--surface: #141416;
--surface-hover: #1a1a1d;
--border: #232326;
--border-subtle: #19191c;
--border-strong: #2e2e32;
--text: #e8e8e8;
--text-secondary: #6b6b6e;
--text-muted: #454548;
--accent: #c2b6ff;
--accent-muted: #7a6fb0;
--accent-soft: rgba(194, 182, 255, 0.08);
--chart-tooltip-background: rgba(20, 20, 22, 0.96);
--chart-tooltip-border: rgba(194, 182, 255, 0.25);
--best: #4ade80;
--worst: #454548;
}
* { margin: 0; padding: 0; box-sizing: border-box; }
html { scroll-behavior: smooth; }
body {
font-family: 'DM Sans', system-ui, -apple-system, sans-serif;
background: var(--bg);
color: var(--text);
line-height: 1.6;
-webkit-font-smoothing: antialiased;
}
.container { max-width: 1100px; margin: 0 auto; padding: 0 40px; }
/* ─── Hero ─── */
.hero { padding: 80px 0 60px; text-align: center; }
.hero h1 {
font-size: clamp(44px, 7vw, 80px);
font-weight: 300;
letter-spacing: -3px;
line-height: 1.0;
}
.hero h1 span { color: var(--accent); }
.hero-sub {
font-size: 17px;
color: var(--text-secondary);
margin-top: 18px;
max-width: 520px;
margin-left: auto;
margin-right: auto;
font-weight: 400;
letter-spacing: -0.2px;
}
.hero-sub a {
color: var(--accent);
text-decoration: none;
border-bottom: 1px solid transparent;
transition: border-color 0.15s;
}
.hero-sub a:hover {
border-bottom-color: var(--accent);
}
.hero-note {
margin-top: 40px;
background: var(--surface);
border: 1px solid var(--border-subtle);
border-left: 3px solid var(--accent);
border-radius: 0 10px 10px 0;
padding: 20px 28px;
text-align: left;
font-size: 14px;
color: var(--text-secondary);
line-height: 1.7;
}
.hero-note strong { color: var(--text); font-weight: 600; }
.hero-note a {
color: var(--accent);
text-decoration: none;
border-bottom: 1px solid transparent;
transition: border-color 0.15s;
}
.hero-note a:hover { border-bottom-color: var(--accent); }
/* ─── Section Headings ─── */
.section-title {
font-size: clamp(36px, 5vw, 56px);
font-weight: 300;
letter-spacing: -2px;
line-height: 1.05;
margin-bottom: 8px;
}
.section-sub {
font-size: 14px;
color: var(--text-secondary);
margin-bottom: 24px;
font-weight: 400;
}
/* ─── Highlights ─── */
.overview-strip {
display: flex;
align-items: center;
justify-content: space-between;
flex-wrap: wrap;
gap: 20px;
padding: 0;
margin-top: 36px;
}
.overview-counts { display: flex; flex-wrap: wrap; gap: 18px 32px; }
.overview-stat { display: flex; align-items: baseline; gap: 10px; }
.overview-stat strong {
color: var(--text);
font: 500 26px/1 'DM Sans', sans-serif;
letter-spacing: -.8px;
}
.overview-stat > span, .overview-scope { color: #91919b; font-size: 12px; }
.overview-scope { display: flex; gap: 10px; }
.size-leaders-heading { display: flex; align-items: baseline; justify-content: space-between; gap: 20px; margin-bottom: 18px; }
.size-leaders-title { color: var(--text); font-size: 24px; font-weight: 500; letter-spacing: -.6px; }
.size-leaders-heading .section-sub { margin-bottom: 0; color: #91919b; }
/* Notable Releases */
.release-head {
display: flex;
justify-content: space-between;
gap: 20px;
align-items: flex-end;
margin-bottom: 14px;
}
.release-grid {
display: grid;
grid-template-columns: repeat(4, minmax(0, 1fr));
gap: 12px;
}
.release-card {
min-height: 136px;
display: flex;
flex-direction: column;
background: var(--surface);
border: 1px solid var(--border-subtle);
border-top: 2px solid var(--accent);
border-radius: 8px;
padding: 16px;
transition: background 0.15s, border-color 0.15s;
}
.release-card:hover {
background: var(--surface-hover);
border-color: var(--border);
}
.release-kicker {
display: flex;
justify-content: space-between;
gap: 8px;
align-items: center;
margin-bottom: 12px;
font-family: 'Space Mono', monospace;
font-size: 10px;
letter-spacing: 0.08em;
text-transform: uppercase;
color: var(--text-muted);
}
.release-pill {
flex: 0 0 auto;
border: 1px solid rgba(194,182,255,0.30);
border-radius: 4px;
padding: 1px 6px;
color: var(--accent);
background: var(--accent-soft);
letter-spacing: 0.04em;
}
.release-title {
color: var(--text);
text-decoration: none;
font-size: 19px;
font-weight: 500;
line-height: 1.15;
letter-spacing: -0.35px;
overflow-wrap: anywhere;
transition: color 0.15s;
}
.release-title:hover { color: var(--accent); }
.release-blurb {
margin-top: 10px;
color: var(--text-secondary);
font-size: 13px;
line-height: 1.55;
}
.release-org {
display: block;
margin-top: 6px;
color: var(--text-muted);
font-size: 11px;
}
.release-meta {
display: block;
margin-top: auto;
padding-top: 16px;
color: var(--text-muted);
font-family: 'Space Mono', monospace;
font-size: 10px;
letter-spacing: 0.04em;
text-transform: uppercase;
}
.release-meta a {
color: var(--text-secondary);
text-decoration: none;
border-bottom: 1px solid transparent;
transition: color 0.15s, border-color 0.15s;
}
.release-meta a:hover {
color: var(--accent);
border-bottom-color: var(--accent);
}
.release-stats {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 8px;
margin-top: auto;
padding-top: 16px;
}
.release-stat {
border-top: 1px solid var(--border-subtle);
padding-top: 8px;
}
.release-stat-label {
display: block;
color: var(--text-muted);
font-family: 'Space Mono', monospace;
font-size: 9px;
letter-spacing: 0.08em;
text-transform: uppercase;
}
.release-stat-value {
display: block;
margin-top: 2px;
color: var(--text-secondary);
font-family: 'Space Mono', monospace;
font-size: 12px;
}
.release-stat-value.best {
color: var(--best);
font-weight: 700;
}
/* ─── Param Filters ─── */
.filter-bar {
display: flex;
gap: 8px;
margin-bottom: 12px;
flex-wrap: wrap;
}
.filter-group {
margin-bottom: 18px;
}
.leaderboard-filter-row {
display: flex;
align-items: flex-end;
justify-content: space-between;
gap: 24px;
margin-bottom: 18px;
}
.leaderboard-filter-row .filter-group {
flex: 1 1 auto;
min-width: 0;
margin-bottom: 0;
}
.leaderboard-search-controls {
flex: 0 1 300px;
min-width: 220px;
}
.leaderboard-search { display: block; }
.size-mode-toggle {
display: flex;
gap: 4px;
padding: 3px;
margin-bottom: 12px;
border: 1px solid var(--border);
border-radius: 8px;
background: var(--surface);
}
.size-mode-toggle .filter-btn { flex: 1; }
.size-mode-toggle .filter-btn:focus-visible {
outline: 2px solid var(--accent);
outline-offset: 2px;
}
.leaderboard-search-input {
display: block;
width: 100%;
height: 32px;
padding: 6px 11px;
border: 1px solid var(--border);
border-radius: 6px;
outline: none;
background: var(--surface);
color: var(--text);
font: 11px/1.2 'Space Mono', monospace;
transition: border-color .15s, background .15s, box-shadow .15s;
}
.leaderboard-search-input::placeholder { color: var(--text-muted); }
.leaderboard-search-input:hover { border-color: var(--border-strong); }
.leaderboard-search-input:focus {
border-color: var(--accent-muted);
background: var(--surface-hover);
box-shadow: 0 0 0 3px var(--accent-soft);
}
@media (max-width: 620px) {
.leaderboard-filter-row {
display: block;
}
.leaderboard-search-controls {
display: block;
min-width: 0;
margin-top: 14px;
}
}
.filter-label {
display: block;
font-family: 'Space Mono', monospace;
font-size: 10px;
letter-spacing: 0.08em;
text-transform: uppercase;
color: var(--text-muted);
margin-bottom: 8px;
}
.filter-btn {
font-family: 'Space Mono', monospace;
font-size: 11px;
font-weight: 400;
letter-spacing: 0.02em;
padding: 5px 12px;
border-radius: 6px;
border: 1px solid var(--border);
background: transparent;
color: var(--text-secondary);
cursor: pointer;
transition: all 0.15s;
}
.filter-btn:hover {
border-color: var(--accent-muted);
color: var(--text);
}
.filter-btn.active {
border-color: var(--accent);
background: var(--accent-soft);
color: var(--accent);
}
/* ─── Table ─── */
.table-wrap {
overflow-x: auto;
border: 1px solid var(--border-subtle);
border-radius: 10px;
background: var(--surface);
}
.leaderboard-table-shell {
position: relative;
}
.leaderboard-table-shell > .table-wrap {
border-top-right-radius: 0;
}
.extra-columns-tab {
position: absolute;
z-index: 2;
top: 0;
right: -28px;
width: 29px;
height: 42px;
display: inline-flex;
align-items: center;
justify-content: center;
padding: 0;
border: 1px solid var(--border-subtle);
border-left: 0;
border-radius: 0 9px 9px 0;
background: var(--surface);
color: var(--text-muted);
cursor: pointer;
box-shadow: 5px 0 14px rgba(0,0,0,0.10);
transition: color 0.15s, border-color 0.15s, background 0.15s, transform 0.15s;
}
.extra-columns-tab:hover,
.leaderboard-table-shell.extras-open .extra-columns-tab {
color: var(--accent);
border-color: var(--accent-muted);
background: var(--accent-soft);
}
.extra-columns-tab:hover { transform: translateX(2px); }
.extra-columns-glyph {
display: inline-block;
font-family: 'Space Mono', monospace;
font-size: 18px;
line-height: 1;
transition: transform 0.18s;
}
.leaderboard-table-shell.extras-open .extra-columns-glyph {
transform: rotate(180deg);
}
#leaderboard-table .extra-col { display: none; }
.leaderboard-table-shell.extras-open #leaderboard-table .extra-col { display: table-cell; }
.leaderboard-table-shell.extras-open #leaderboard-table .extra-col-start {
border-left: 1px solid var(--border-strong);
}
#leaderboard-table thead th {
height: 41px;
box-sizing: border-box;
}
table { width: 100%; border-collapse: collapse; font-size: 14px; }
thead th {
padding: 12px 12px;
text-align: center;
font-size: 10px;
font-weight: 600;
letter-spacing: 0.08em;
text-transform: uppercase;
color: var(--text-muted);
border-bottom: 1px solid var(--border);
cursor: pointer;
user-select: none;
white-space: nowrap;
transition: color 0.15s;
font-family: 'Space Mono', monospace;
}
thead th:hover { color: var(--accent); }
thead th:first-child, tbody td:first-child { text-align: left; }
thead th .sort-indicator {
margin-left: 3px;
opacity: 0;
font-size: 9px;
color: var(--accent);
}
thead th.sorted { color: var(--accent); }
thead th.sorted .sort-indicator { opacity: 1; }
tbody tr {
border-bottom: 1px solid var(--border-subtle);
transition: background 0.12s;
}
tbody tr:last-child { border-bottom: none; }
tbody tr:hover { background: var(--accent-soft); }
tbody td {
padding: 12px 12px;
text-align: center;
vertical-align: middle;
}
.td-rank {
font-weight: 700;
font-size: 12px;
color: var(--accent-muted);
width: 32px;
font-family: 'Space Mono', monospace;
}
.org-title-line {
display: inline-flex;
align-items: center;
gap: 8px;
}
.rank-move {
display: inline-flex;
align-items: center;
justify-content: center;
min-width: 30px;
height: 17px;
padding: 0 5px;
border-radius: 4px;
border: 1px solid var(--border-subtle);
color: var(--text-muted);
background: rgba(255,255,255,0.03);
font-size: 9px;
line-height: 1;
}
.rank-move.up {
color: var(--best);
border-color: rgba(74,222,128,0.28);
background: rgba(74,222,128,0.08);
}
.rank-move.down {
color: #f87171;
border-color: rgba(248,113,113,0.26);
background: rgba(248,113,113,0.07);
}
.rank-move.new {
color: var(--accent-muted);
border-color: rgba(194,182,255,0.30);
background: rgba(194,182,255,0.08);
}
.td-model {
text-align: left;
font-weight: 500;
font-size: 14px;
color: var(--text);
}
.td-model a {
color: #d6d6da;
text-decoration: none;
transition: color 0.15s;
}
.td-model a:hover { color: var(--accent); }
.td-model .rank-move {
margin-left: 7px;
vertical-align: 1px;
}
.td-model .model-org {
display: block;
font-size: 11px;
color: var(--text-muted);
font-weight: 400;
margin-top: 1px;
}
.td-model .model-org a {
color: var(--text-muted);
text-decoration: none;
transition: color 0.15s, border-color 0.15s;
border-bottom: 1px solid transparent;
}
.td-model .model-org a:hover {
color: var(--accent-muted);
border-bottom-color: var(--accent-muted);
}
.td-score {
font-family: 'Space Mono', monospace;
font-size: 12px;
letter-spacing: -0.2px;
}
.td-score.best {
color: var(--best);
font-weight: 700;
}
.td-score.na {
color: var(--text-muted);
font-style: normal;
}
.td-params {
font-family: 'Space Mono', monospace;
font-size: 11px;
color: var(--text-secondary);
}
.org-badge {
display: inline-block;
font-size: 10px;
font-weight: 600;
padding: 2px 7px;
border-radius: 4px;
letter-spacing: 0.03em;
font-family: 'Space Mono', monospace;
}
/* ─── Scatter Plot ─── */
.legend-bar {
display: flex;
gap: 20px;
margin-bottom: 24px;
flex-wrap: wrap;
}
.legend-item {
display: flex;
align-items: center;
gap: 6px;
font-size: 12px;
color: var(--text-secondary);
}
.legend-dot { width: 8px; height: 8px; border-radius: 50%; }
.chart-card {
background: var(--surface);
border: 1px solid var(--border-subtle);
border-radius: 10px;
padding: 20px;
}
.chart-card h3 {
font-size: 12px;
font-weight: 500;
color: var(--text-secondary);
margin-bottom: 14px;
letter-spacing: 0.02em;
}
.chart-card canvas {
max-height: 460px;
}
.index-method-card {
border-left-color: var(--best);
}
.index-formula {
margin: 18px 0;
padding: 14px 16px;
border: 1px solid var(--border-subtle);
border-radius: 7px;
background: rgba(255,255,255,0.018);
color: var(--text);
font-family: 'Space Mono', monospace;
font-size: 12px;
line-height: 1.7;
overflow-x: auto;
}
.index-weight-grid {
display: grid;
grid-template-columns: repeat(4, minmax(0, 1fr));
gap: 10px;
margin: 18px 0;
}
.index-weight {
padding: 13px 14px;
border: 1px solid var(--border-subtle);
border-radius: 7px;
background: rgba(255,255,255,0.012);
}
.index-weight strong,
.index-weight span {
display: block;
}
.index-weight strong {
color: var(--text);
font-size: 13px;
font-weight: 500;
margin-bottom: 5px;
}
.index-weight span {
color: var(--text-muted);
font-family: 'Space Mono', monospace;
font-size: 10px;
line-height: 1.5;
}
.index-method-note {
color: var(--text-muted) !important;
font-size: 12px !important;
}
/* ─── About ─── */
.about-box {
background: var(--surface);
border: 1px solid var(--border-subtle);
border-left: 3px solid var(--accent);
border-radius: 0 10px 10px 0;
padding: 28px 32px;
}
.about-box h3 {
font-size: 20px;
font-weight: 500;
margin-bottom: 10px;
color: var(--text);
letter-spacing: -0.5px;
}
.about-box p {
font-size: 14px;
color: var(--text-secondary);
line-height: 1.7;
}
.about-box a {
color: var(--accent);
text-decoration: none;
border-bottom: 1px solid transparent;
transition: border-color 0.15s;
}
.about-box a:hover { border-bottom-color: var(--accent); }
/* ─── Footer ─── */
footer {
margin-top: 100px;
margin-bottom: 90px;
padding: 32px 0;
border-top: 1px solid var(--border-subtle);
display: flex;
justify-content: space-between;
align-items: center;
font-size: 11px;
color: var(--text-muted);
font-family: 'Space Mono', monospace;
}
footer a {
color: var(--text-secondary);
text-decoration: none;
margin-left: 16px;
transition: color 0.15s;
}
footer a:hover { color: var(--accent); }
/* ─── Side Nav Rail ─── */
.side-nav {
position: fixed;
left: 28px;
top: 50%;
transform: translateY(-50%);
z-index: 50;
display: flex;
flex-direction: column;
gap: 2px;
}
.side-nav a {
display: flex;
align-items: center;
gap: 11px;
padding: 5px 0;
font-family: 'Space Mono', monospace;
font-size: 11px;
letter-spacing: 0.04em;
color: var(--text-muted);
text-decoration: none;
transition: color 0.15s;
}
.side-nav-dot {
width: 7px;
height: 7px;
border-radius: 50%;
border: 1px solid var(--border-strong);
flex: 0 0 auto;
transition: background 0.15s, border-color 0.15s, box-shadow 0.15s;
}
.side-nav-label {
transition: color 0.15s;
}
.side-nav a:hover { color: var(--text-secondary); }
.side-nav a:hover .side-nav-dot { border-color: var(--accent-muted); }
.side-nav a.active { color: var(--accent); }
.side-nav a.active .side-nav-dot {
background: var(--accent);
border-color: var(--accent);
box-shadow: 0 0 0 3px var(--accent-soft);
}
@media (max-width: 1279px) {
.side-nav { display: none; }
}
/* ─── Compare ─── */
.th-compare {
cursor: default !important;
width: 60px;
}
.th-compare:hover { color: var(--text-muted) !important; }
.td-compare { width: 60px; text-align: center !important; }
.compare-toggle {
width: 26px;
height: 26px;
border-radius: 6px;
border: 1px solid var(--border);
background: transparent;
color: var(--text-secondary);
cursor: pointer;
font-family: 'Space Mono', monospace;
font-size: 14px;
line-height: 1;
display: inline-flex;
align-items: center;
justify-content: center;
transition: all 0.15s;
}
.compare-toggle:hover {
border-color: var(--accent-muted);
color: var(--text);
}
.compare-toggle.active {
border-color: var(--accent);
background: var(--accent-soft);
color: var(--accent);
}
.compare-head {
display: flex;
justify-content: space-between;
align-items: flex-end;
gap: 20px;
flex-wrap: wrap;
}
.compare-head-actions {
display: flex;
align-items: center;
gap: 12px;
margin-bottom: 24px;
}
.compare-count {
font-family: 'Space Mono', monospace;
font-size: 11px;
color: var(--accent-muted);
margin-left: 4px;
}
.compare-limit-note {
font-family: 'Space Mono', monospace;
font-size: 10px;
letter-spacing: 0.04em;
color: var(--text-muted);
opacity: 0;
transition: opacity 0.2s, color 0.2s;
}
.compare-limit-note.show {
opacity: 1;
color: var(--accent);
}
.compare-empty {
background: var(--surface);
border: 1px dashed var(--border);
border-radius: 10px;
padding: 44px 28px;
text-align: center;
color: var(--text-secondary);
}
.compare-empty p { font-size: 15px; }
.compare-empty-hint { font-size: 13px; color: var(--text-muted); margin-top: 8px; }
.compare-empty-plus {
display: inline-flex;
align-items: center;
justify-content: center;
width: 18px; height: 18px;
border-radius: 4px;
border: 1px solid var(--accent);
color: var(--accent);
font-family: 'Space Mono', monospace;
font-size: 12px;
vertical-align: middle;
}
.compare-table-wrap { overflow-x: auto; }
.compare-table { width: 100%; border-collapse: collapse; }
.compare-table th, .compare-table td {
padding: 14px 16px;
text-align: center;
border-bottom: 1px solid var(--border-subtle);
}
.compare-table thead th { border-bottom: 1px solid var(--border); vertical-align: bottom; }
.compare-metric-head {
text-align: left !important;
font-family: 'Space Mono', monospace;
font-size: 10px;
letter-spacing: 0.08em;
text-transform: uppercase;
color: var(--text-muted);
font-weight: 600;
}
.compare-model-head {
position: relative;
min-width: 140px;
}
.compare-remove {
position: absolute;
top: 6px; right: 6px;
width: 18px; height: 18px;
border-radius: 4px;
border: 1px solid var(--border);
background: transparent;
color: var(--text-muted);
cursor: pointer;
font-size: 12px;
line-height: 1;
display: inline-flex;
align-items: center;
justify-content: center;
transition: all 0.15s;
}
.compare-remove:hover { color: #f87171; border-color: rgba(248,113,113,0.4); }
.compare-model-name {
display: block;
color: var(--text);
text-decoration: none;
font-size: 14px;
font-weight: 500;
letter-spacing: -0.2px;
overflow-wrap: anywhere;
transition: color 0.15s;
}
.compare-model-name:hover { color: var(--accent); }
.compare-model-org {
display: block;
margin-top: 3px;
font-size: 11px;
color: var(--text-muted);
}
.compare-model-org a { color: var(--text-muted); text-decoration: none; border-bottom: 1px solid transparent; }
.compare-model-org a:hover { color: var(--accent-muted); border-bottom-color: var(--accent-muted); }
.compare-metric {
text-align: left !important;
font-size: 13px;
color: var(--text-secondary);
font-weight: 500;
}
.compare-cell {
font-family: 'Space Mono', monospace;
font-size: 13px;
color: var(--text-secondary);
}
.compare-cell.params { color: var(--text); }
.compare-cell.best { color: var(--best); font-weight: 700; }
.compare-cell.na { color: var(--text-muted); }
.compare-table tbody tr:hover { background: var(--accent-soft); }
.compare-table tbody tr:last-child td { border-bottom: none; }
/* Sticky compare bar */
.compare-bar {
position: fixed;
left: 0; right: 0; bottom: 0;
z-index: 60;
padding: 0 20px 18px;
pointer-events: none;
transform: translateY(140%);
transition: transform 0.25s ease;
}
.compare-bar.show { transform: translateY(0); }
.compare-bar-inner {
pointer-events: auto;
max-width: 1020px;
margin: 0 auto;
display: flex;
align-items: center;
gap: 14px;
flex-wrap: wrap;
background: rgba(20,20,22,0.92);
backdrop-filter: blur(12px);
border: 1px solid var(--border-strong);
border-radius: 12px;
padding: 12px 16px;
box-shadow: 0 10px 40px rgba(0,0,0,0.45);
}
.compare-bar-label {
font-family: 'Space Mono', monospace;
font-size: 11px;
letter-spacing: 0.04em;
color: var(--accent);
white-space: nowrap;
}
.compare-bar-chips {
display: flex;
gap: 6px;
flex-wrap: wrap;
flex: 1 1 auto;
}
.compare-chip {
display: inline-flex;
align-items: center;
gap: 6px;
font-size: 12px;
color: var(--text);
background: rgba(255,255,255,0.03);
border: 1px solid var(--border);
border-radius: 6px;
padding: 3px 6px 3px 9px;
}
.compare-chip-dot { width: 7px; height: 7px; border-radius: 50%; flex: 0 0 auto; }
.compare-chip-x {
border: none;
background: transparent;
color: var(--text-muted);
cursor: pointer;
font-size: 14px;
line-height: 1;
padding: 0 2px;
transition: color 0.15s;
}
.compare-chip-x:hover { color: #f87171; }
.compare-bar-actions {
display: flex;
align-items: center;
gap: 14px;
white-space: nowrap;
}
.compare-bar-view {
font-family: 'Space Mono', monospace;
font-size: 12px;
color: var(--accent);
text-decoration: none;
background: none;
border: 0;
border-bottom: 1px solid transparent;
padding: 0;
cursor: pointer;
transition: border-color 0.15s;
}
.compare-bar-view:hover { border-bottom-color: var(--accent); }
.compare-bar-clear {
font-family: 'Space Mono', monospace;
font-size: 11px;
color: var(--text-secondary);
background: transparent;
border: 1px solid var(--border);
border-radius: 6px;
padding: 4px 10px;
cursor: pointer;
transition: all 0.15s;
}
.compare-bar-clear:hover { color: var(--text); border-color: var(--accent-muted); }
/* ─── Mobile ─── */
@media (max-width: 768px) {
.container { padding: 0 20px; }
.hero { padding: 60px 0 40px; }
.release-head { display: block; }
.release-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
.chart-card canvas { max-height: 360px; }
.index-weight-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
footer { flex-direction: column; gap: 12px; text-align: center; }
footer a { margin: 0 8px; }
.compare-head { display: block; }
.compare-head-actions { margin-top: 12px; }
.compare-bar-inner { gap: 10px; padding: 10px 12px; }
.compare-bar-actions { width: 100%; justify-content: space-between; }
.extra-columns-tab {
top: 0;
right: -20px;
width: 29px;
height: 42px;
border: 1px solid var(--border-subtle);
border-left: 0;
border-radius: 0 9px 9px 0;
box-shadow: 5px 0 14px rgba(0,0,0,0.10);
}
.extra-columns-tab:hover { transform: none; }
}
@media (max-width: 520px) {
.release-grid { grid-template-columns: 1fr; }
.index-weight-grid { grid-template-columns: 1fr; }
}
</style>
<style>
.filter-leader-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
gap: 8px;
width: 100%;
margin: 10px 0 22px;
}
#benchmark-leader-grid { grid-template-columns: repeat(6, minmax(0, 1fr)); }
.filter-leader-card {
min-width: 0;
position: relative;
padding: 13px 14px 14px;
background: var(--surface);
border: 1px solid var(--border-subtle);
border-top: 2px solid var(--border-strong);
border-radius: 8px;
cursor: pointer;
transition: background .15s ease, border-color .15s ease, transform .15s ease;
}
.filter-leader-card:hover,
.filter-leader-card:focus-visible {
background: var(--surface-hover);
border-color: var(--accent-muted);
outline: none;
transform: translateY(-2px);
}
.filter-leader-card.active {
background: var(--accent-soft);
border-color: var(--accent-muted);
border-top-color: var(--accent);
}
.filter-leader-kicker {
display: flex;
justify-content: space-between;
gap: 8px;
color: var(--text-muted);
font: 700 9px/1.2 'Space Mono', monospace;
letter-spacing: .08em;
text-transform: uppercase;
}
.filter-leader-kicker b { color: var(--best); }
.filter-leader-name {
display: block;
margin-top: 9px;
color: var(--text);
font-size: 13px;
font-weight: 600;
line-height: 1.25;
text-decoration: none;
overflow-wrap: anywhere;
}
.filter-leader-name:hover { color: var(--accent); }
.filter-leader-score {
display: block;
margin-top: 8px;
color: var(--best);
font: 700 16px/1 'Space Mono', monospace;
}
.filter-leader-meta {
display: block;
margin-top: 7px;
color: var(--text-muted);
font-size: 10px;
line-height: 1.35;
}
@media (max-width: 1100px) {
.filter-leader-grid,
#benchmark-leader-grid { grid-template-columns: repeat(3, minmax(0, 1fr)); }
}
@media (max-width: 620px) {
.filter-leader-grid,
#benchmark-leader-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
}
body.variant-seven {
background:
radial-gradient(circle at 50% 0, rgba(194,182,255,.10), transparent 36rem),
var(--bg);
}
body.variant-seven .container {
max-width: 1360px;
padding: 0 58px;
display: grid;
grid-template-columns: repeat(12, minmax(0, 1fr));
gap: 22px;
}
body.variant-seven .container > * { min-width: 0; }
body.variant-seven .hero {
grid-column: 1 / -1;
padding: 112px 0 32px;
border-bottom: 1px solid var(--accent-muted);
}
body.variant-seven .hero::before {
content: 'Axiomic Labs';
display: block;
margin-bottom: 28px;
color: var(--accent);
font: 700 10px/1 'Space Mono', monospace;
letter-spacing: .2em;
}
body.variant-seven .hero h1 { font-size: clamp(64px, 9vw, 126px); }
body.variant-seven #about {
grid-column: 1 / span 4;
padding-top: 48px !important;
}
body.variant-seven #int-index-method {
grid-column: 5 / -1;
padding-top: 48px !important;
}
body.variant-seven #about .about-box,
body.variant-seven #int-index-method .about-box {
height: 100%;
border-radius: 0;
border-top: 2px solid var(--accent);
}
body.variant-seven #highlights,
body.variant-seven #new-releases,
body.variant-seven #leaderboard,
body.variant-seven #compare,
body.variant-seven #score-chart,
body.variant-seven #charts,
body.variant-seven #org-performance,
body.variant-seven footer { grid-column: 1 / -1; }
body.variant-seven #highlights { padding-top: 32px !important; }
body.variant-seven #new-releases { padding-top: 88px !important; }
body.variant-seven #new-releases .release-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
body.variant-seven #new-releases .release-card {
min-height: 180px;
padding: 24px;
border-radius: 0;
border-left: 3px solid var(--accent);
border-top-color: var(--border);
}
body.variant-seven #leaderboard,
body.variant-seven #compare,
body.variant-seven #score-chart,
body.variant-seven #charts,
body.variant-seven #org-performance { padding-top: 96px !important; }
body.variant-seven .section-title {
font-size: clamp(48px, 6vw, 76px);
letter-spacing: -.06em;
}
body.variant-seven #leaderboard .leaderboard-table-shell { border-radius: 0 0 18px 0; }
body.variant-seven #score-chart,
body.variant-seven #charts {
padding: 36px !important;
background: var(--bg-alt);
border: 1px solid var(--border);
border-radius: 0 0 28px 0;
}
body.variant-seven #score-chart .chart-card,
body.variant-seven #charts .chart-card,
body.variant-seven #org-performance .chart-card { background: transparent; border: 0; padding: 18px 0 0; }
body.variant-seven footer { margin-top: 88px; }
@media (max-width: 900px) {
body.variant-seven .container { display: block; padding: 0 22px; }
body.variant-seven .hero { padding: 68px 0 50px; }
body.variant-seven #new-releases .release-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
}
@media (max-width: 560px) {
body.variant-seven #new-releases .release-grid { grid-template-columns: 1fr; }
body.variant-seven #score-chart,
body.variant-seven #charts { padding: 22px !important; }
}
</style>
<base target="_blank">
<style>
/* index7.1 refinements */
#leaderboard-body .td-model > a.category-leader {
color: var(--leader-gold);
text-shadow: 0 0 7px rgba(215, 189, 131, .23), 0 0 12px rgba(215, 189, 131, .07);
}
#leaderboard-body .td-model > a.category-leader:hover { color: var(--leader-gold-hover); }
.category-leader-key { color: var(--leader-gold); }
#highlight-leader-grid { grid-template-columns: repeat(4, minmax(0, 1fr)); gap: 12px; margin: 0; }
#highlight-leader-grid .filter-leader-card {
display: flex;
flex-direction: column;
min-height: 174px;
padding: 18px;
border-radius: 4px;
border: 1px solid var(--border-subtle);
border-left: 3px solid var(--leader-org-color);
background: var(--surface);
box-shadow: none;
}
#highlight-leader-grid .filter-leader-card.active {
border-color: var(--border);
border-left-color: var(--leader-org-color);
}
#highlight-leader-grid .filter-leader-card:hover,
#highlight-leader-grid .filter-leader-card:focus-visible {
border-color: var(--leader-org-color);
background: var(--surface-hover);
transform: none;
}
#highlight-leader-grid .filter-leader-card:focus-visible { outline: 2px solid var(--leader-org-color); outline-offset: 3px; }
#highlight-leader-grid .filter-leader-kicker { color: #91919b; font-size: 10px; letter-spacing: .08em; }
#highlight-leader-grid .filter-leader-name { min-height: 44px; margin-top: 14px; color: var(--text); font-size: 18px; font-weight: 500; letter-spacing: -.3px; }
#highlight-leader-grid .filter-leader-name:hover { color: var(--text); }
.highlight-leader-footer { display: grid; grid-template-columns: minmax(0, 1fr) auto; align-items: end; gap: 10px; margin-top: auto; padding-top: 14px; }
.highlight-leader-score { text-align: right; }
#highlight-leader-grid .filter-leader-score { margin-top: 0; font-size: 22px; }
#highlight-leader-grid .size-score-label { display: block; margin-top: 5px; color: #91919b; font-size: 10px; }
#highlight-leader-grid .filter-leader-meta { margin-top: 0; color: #91919b; font-size: 11px; overflow-wrap: anywhere; }
.highlight-leader-org, .highlight-leader-params { display: block; }
.highlight-leader-params { margin-top: 4px; color: var(--text-secondary); }
@media (max-width: 900px) {
#highlight-leader-grid { grid-template-columns: repeat(3, minmax(0, 1fr)); }
}
@media (max-width: 620px) {
#highlight-leader-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
#highlight-leader-grid .filter-leader-card { padding: 14px; }
#highlight-leader-grid .filter-leader-name { font-size: 16px; }
#highlight-leader-grid .filter-leader-score { font-size: 20px; }
#highlight-leader-grid .filter-leader-meta { font-size: 10px; }
.size-leaders-heading { align-items: flex-start; flex-direction: column; gap: 12px; }
.overview-counts { gap: 18px 24px; }
}
body.variant-seven-one #new-releases .release-card {
border-left: 1px solid var(--border-subtle);
}
body.variant-seven-one #new-releases .release-card:hover {
border-left-color: var(--border);
}
body.variant-seven-one .release-carousel-nav {
display: flex;
align-items: center;
gap: 8px;
padding-bottom: 24px;
}
body.variant-seven-one .release-carousel-button {
width: 38px;
height: 38px;
display: inline-flex;
align-items: center;
justify-content: center;
border: 1px solid var(--border-strong);
border-radius: 50%;
background: var(--surface);
color: var(--text);
font: 400 17px/1 'Space Mono', monospace;
cursor: pointer;
transition: color .15s ease, border-color .15s ease, background .15s ease, transform .15s ease;
}
body.variant-seven-one .release-carousel-button:hover:not(:disabled) {
color: var(--accent);
border-color: var(--accent-muted);
background: var(--accent-soft);
transform: translateY(-2px);
}
body.variant-seven-one .release-carousel-button:disabled {
color: var(--text-muted);
cursor: default;
opacity: .32;
}
body.variant-seven-one .release-carousel-status {
min-width: 54px;
color: var(--text-muted);
text-align: center;
font: 700 9px/1 'Space Mono', monospace;
letter-spacing: .1em;
}
body.variant-seven-one #new-releases .release-grid {
display: block;
overflow: hidden;
transition: height .38s cubic-bezier(.2,.8,.2,1);
touch-action: pan-y;
}
body.variant-seven-one .release-carousel-track {
display: flex;
align-items: flex-start;
width: 100%;
transition: transform .5s cubic-bezier(.2,.8,.2,1);
will-change: transform;
}
body.variant-seven-one .release-carousel-page {
flex: 0 0 100%;
min-width: 0;
display: grid;
grid-template-columns: repeat(2, minmax(0, 1fr));
gap: 12px;
}
body.variant-seven-one .release-carousel-page .release-card {
height: 100%;
}
body.variant-seven-one #score-chart,
body.variant-seven-one #charts {
padding: 96px 0 0 !important;
background: transparent;
border: 0;
border-radius: 0;
}
body.variant-seven-one .filter-leader-card.active {
background: var(--accent-soft);
background: color-mix(in srgb, var(--leader-org-color, var(--accent)) 10%, var(--surface));
border-color: var(--leader-org-color, var(--accent));
box-shadow:
inset 0 0 0 1px color-mix(in srgb, var(--leader-org-color, var(--accent)) 24%, transparent),
0 12px 30px color-mix(in srgb, var(--leader-org-color, var(--accent)) 9%, transparent);
}
body.variant-seven-one footer > div:nth-child(2) {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: center;
gap: 8px 16px;
}
body.variant-seven-one footer > div:nth-child(2) a {
flex: 0 0 auto;
margin-left: 0;
white-space: nowrap;
}
body.variant-seven-one #rules {
grid-column: 1 / -1;
padding-top: 96px !important;
}
body.variant-seven-one .rules-grid {
display: grid;
grid-template-columns: repeat(3, minmax(0, 1fr));
gap: 12px;
}
body.variant-seven-one .rule-card {
min-height: 170px;
padding: 22px;
background: var(--surface);
border: 1px solid var(--border-subtle);
border-top: 2px solid var(--border-strong);
border-radius: 0 0 18px 0;
}
body.variant-seven-one .rule-number {
display: block;
margin-bottom: 28px;
color: var(--accent-muted);
font: 700 10px/1 'Space Mono', monospace;
letter-spacing: .14em;
}
body.variant-seven-one .rule-card h3 {
margin-bottom: 8px;
color: var(--text);
font-size: 18px;
font-weight: 500;
letter-spacing: -.3px;
}
body.variant-seven-one .rule-card p {
color: var(--text-secondary);
font-size: 13px;
line-height: 1.6;
}
body.variant-seven-one .rule-card a {
color: var(--accent);
text-decoration: none;
}
body.variant-seven-one .rule-card a:hover {
text-decoration: underline;
}
body.variant-seven-one .filter-leader-kicker b {
display: none;
}
body.variant-seven-one .extra-columns-tab {
overflow: hidden;
}
body.variant-seven-one .extra-columns-glyph {
transform: rotate(0deg) scale(1);
transform-origin: 50% 50%;
transition: transform .42s cubic-bezier(.2,.8,.2,1), color .2s ease;
will-change: transform;
}
body.variant-seven-one .leaderboard-table-shell.extras-open .extra-columns-glyph {
transform: rotate(180deg) scale(1.12);
}
body.variant-seven-one .leaderboard-table-shell.extras-open > .table-wrap {
animation: leaderboard-columns-open .48s cubic-bezier(.2,.8,.2,1) both;
}
body.variant-seven-one .leaderboard-table-shell.extras-closing > .table-wrap {
animation: leaderboard-columns-close .42s cubic-bezier(.4,0,.2,1) both;
}
body.variant-seven-one .leaderboard-table-shell.extras-open #leaderboard-table .extra-col {
animation: leaderboard-cell-open .42s cubic-bezier(.2,.8,.2,1) both;
}
body.variant-seven-one .leaderboard-table-shell.extras-open #leaderboard-table .extra-col:nth-child(11) {
animation-delay: .04s;
}
body.variant-seven-one .leaderboard-table-shell.extras-open #leaderboard-table .extra-col:nth-child(12) {
animation-delay: .09s;
}
body.variant-seven-one .leaderboard-table-shell.extras-open #leaderboard-table .extra-col:nth-child(13) {
animation-delay: .14s;
}
body.variant-seven-one .leaderboard-table-shell.extras-closing #leaderboard-table .extra-col {
display: table-cell;
animation: leaderboard-cell-close .3s cubic-bezier(.4,0,1,1) both;
}
body.variant-seven-one .leaderboard-table-shell.extras-closing #leaderboard-table .extra-col-start {
border-left: 1px solid var(--border-strong);
}
@keyframes leaderboard-columns-open {
from {
clip-path: inset(0 24% 0 0);
opacity: .82;
}
to {
clip-path: inset(0 0 0 0);
opacity: 1;
}
}
@keyframes leaderboard-columns-close {
from {
clip-path: inset(0 0 0 0);
opacity: 1;
}
to {
clip-path: inset(0 24% 0 0);
opacity: .82;
}
}
@keyframes leaderboard-cell-open {
from {
opacity: 0;
transform: translateX(-18px);
}
to {
opacity: 1;
transform: translateX(0);
}
}
@keyframes leaderboard-cell-close {
from {
opacity: 1;
transform: translateX(0);
}
to {
opacity: 0;
transform: translateX(14px);
}
}
@media (prefers-reduced-motion: reduce) {
body.variant-seven-one .extra-columns-glyph,
body.variant-seven-one .leaderboard-table-shell > .table-wrap,
body.variant-seven-one .leaderboard-table-shell #leaderboard-table .extra-col {
animation-duration: .01ms !important;
transition-duration: .01ms;
}
body.variant-seven-one #new-releases .release-grid,
body.variant-seven-one .release-carousel-track {
transition-duration: .01ms;
}
}
@media (max-width: 900px) {
body.variant-seven-one .rules-grid {
grid-template-columns: repeat(2, minmax(0, 1fr));
}
}
@media (max-width: 1600px) {
body.variant-seven-one .side-nav {
display: none;
}
}
@media (max-width: 560px) {
body.variant-seven-one .rules-grid {
grid-template-columns: 1fr;
}
body.variant-seven-one .release-head {
display: flex;
align-items: flex-end;
}
body.variant-seven-one .release-carousel-page {
grid-template-columns: 1fr;
}
body.variant-seven-one .release-carousel-nav {
padding-bottom: 24px;
}
}
</style>
<link rel="stylesheet" href="leaderboard-history.css">
</head>
<body class="variant-seven variant-seven-one">
<!-- SIDE NAV RAIL -->
<nav id="side-nav" class="side-nav" aria-label="Section navigation"></nav>
<div class="container">
<!-- HERO -->
<section class="hero">
<h1>Open <span>SLM Leaderboard</span></h1>
<p class="hero-sub">
A leaderboard for sub-400M parameter language models, evaluated using LM-eval harness
or a custom benchmark script available here
<a href="https://huggingface.co/datasets/AxiomicLabs/ArithMark-3.0" target="_blank" rel="noopener noreferrer">ArithMark-3.0</a>.
</p>
<p class="hero-sub">
To support our work and help us keep this leaderboard up to date, please consider giving the space a like and follow!
</p>
<div class="overview-strip" id="overview-strip" aria-label="Leaderboard overview"></div>
</section>
<!-- HIGHLIGHTS -->
<section id="highlights" style="padding-top:20px;">
<div class="size-leaders-heading">
<h2 class="size-leaders-title">Leaders by size</h2>
<p class="section-sub">Ranked by Intelligence Index</p>
</div>
<div class="filter-leader-grid" id="highlight-leader-grid" aria-label="Intelligence Index leaders by model size"></div>
</section>
<!-- NOTABLE NEW RELEASES -->
<section id="new-releases" style="padding-top:40px;">
<div class="release-head">
<div>
<h2 class="section-title">Notable New Releases</h2>
<p class="section-sub">Short notes on new models and leaderboard shifts.</p>
</div>
<div class="release-carousel-nav" aria-label="Release pages">
<button class="release-carousel-button" id="release-prev" type="button" aria-label="Previous four releases">&larr;</button>
<span class="release-carousel-status" id="release-page-status" aria-live="polite">01 / 01</span>
<button class="release-carousel-button" id="release-next" type="button" aria-label="Next four releases">&rarr;</button>
</div>
</div>
<div class="release-grid">
<article class="release-card" data-org="axiomiclabs">
<div class="release-kicker">
<span>14/10/2026</span>
<span class="release-pill">update</span>
</div>
<a class="release-title" href="#leaderboard">Open SLM expands to sub-400M models</a>
<p class="release-blurb">The leaderboard now welcomes models below 400M parameters, expanding the previous 150M limit. Switch to Extended above Search models to use the &lt;250M filter or select All to explore the full leaderboard.</p>
<span class="release-meta">Leaderboard Update &middot; Eligibility</span>
</article>
<article class="release-card" data-org="axiomiclabs">
<div class="release-kicker">
<span>10/10/2026</span>
<span class="release-pill">new</span>
</div>
<a class="release-title" href="https://huggingface.co/AxiomicLabs/GPT-X3-150M" target="_blank">New Outright #1</a>
<p class="release-blurb">GPT-X3-150M has taken the #1 spot from cagliostro, becoming the first ever model to surpass 30 on the Intelligence Index</p>
<span class="release-meta">Axiomic Labs &middot; 149M params</span>
</article>
<article class="release-card" data-org="benchlabs">
<div class="release-kicker">
<span>05/10/2026</span>
<span class="release-pill">new</span>
</div>
<a class="release-title" href="https://huggingface.co/bench-labs/cagliostro-v3.5" target="_blank">We have a NEW #1</a>
<p class="release-blurb">Bench Labs' cagliostro-v3.5 has taken the #1 spot, becoming the first model to defeat Huggingface's SmolLM2. </p>
<span class="release-meta">Bench Labs &middot; 146M params</span>
</article>
<article class="release-card" data-org="benchlabs">
<div class="release-kicker">
<span>20/09/2026</span>
<span class="release-pill">new</span>
</div>
<a class="release-title" href="https://huggingface.co/bench-labs/cagliostro-v3" target="_blank">Bench Labs advances the SLM frontier</a>
<p class="release-blurb">Bench Labs' cagliostro-v3 hits Intelligence Index 26.13, a 146M-param model trained on 75b tokens, from scratch on a single consumer GPU. It places 2nd on the leaderboard, making it the most capable SLM we've seen trained on consumer hardware. </p>
<span class="release-meta">Bench Labs &middot; 146M params</span>
</article>
<article class="release-card" data-org="axiomiclabs">
<div class="release-kicker">
<span>26/08/2026</span>
<span class="release-pill">update</span>
</div>
<a class="release-title" href="https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard" target="_blank">Open SLM UI gets a catalogue refresh</a>
<p class="release-blurb">The leaderboard UI now brings filter leaders, animated benchmark expansion, Intelligence Index fit scores, and paged release notes into one clearer workspace.</p>
<span class="release-meta">Leaderboard Update &middot; UI</span>
</article>
<article class="release-card" data-org="axiomiclabs">
<div class="release-kicker">
<span>12/07/2026</span>
<span class="release-pill">change</span>
</div>
<a class="release-title" href="https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard" target="_blank">Leaderboard Math Changes</a>
<p class="release-blurb">ArithMark-3 is now the primary math benchmark for ranking, weighting subject to change. ArithMark-2 remains available in the expanded columns.</p>
<span class="release-meta">Leaderboard Change Β· Math</span>
</article>
<article class="release-card" data-org="ucr">
<div class="release-kicker">
<span>01/07/2026</span>
<span class="release-pill">new</span>
</div>
<a class="release-title" href="https://huggingface.co/UniversalComputingResearch/Atom2.7m" target="_blank">Atom2.7m posts standout ArithMark 2.0 result</a>
<p class="release-blurb">UCR's 2.74M SLM uses custom tokenization and digit features to reach 69.4% ArithMark accuracy, setting the leaderboard's #1 score</p>
<span class="release-meta">UCR &middot; 2.7M params</span>
</article>
<article class="release-card" data-org="axiomiclabs">
<div class="release-kicker">
<span>19/06/2026</span>
<span class="release-pill">new</span>
</div>
<a class="release-title" href="https://huggingface.co/AxiomicLabs/GPT-S2-5M" target="_blank">Axiomic Labs GPT-S2 takes &lt;10M #1 spot</a>
<p class="release-blurb">Built on Axiomic Labs' T-X4 refresh-gated XSA stack, GPT-S2-5M pushes into the sub-10M bracket and lands at the top, dethroning SLM-10M.</p>
<span class="release-meta">Axiomic Labs Β· 5.4M params</span>
</article>
<article class="release-card" data-org="veyraai">
<div class="release-kicker">
<span>16/06/2026</span>
<span class="release-pill">new</span>
</div>
<a class="release-title" href="https://huggingface.co/veyra-ai/Veyra2-Apricot-50M-Base" target="_blank">Veyra2-Apricot-50M-Base takes the &lt;50M lead</a>
<p class="release-blurb">Veyra AI's 49M Apricot checkpoint posts a 37.63 average, setting the strongest score in the sub-50M bracket.</p>
<span class="release-meta">veyra-ai Β· 49M params</span>
</article>
</div>
</section>
<!-- LEADERBOARD TABLE -->
<section id="leaderboard" style="padding-top:40px;">
<h2 class="section-title">Leaderboard</h2>
<p class="section-sub">Zero-shot evaluation. Higher is better for all columns. <span class="category-leader-key">Gold names</span> mark size category leaders. Click any header to sort.</p>
<div class="leaderboard-filter-row">
<div class="filter-group">
<span class="filter-label">Model size</span>
<div class="filter-bar" id="filter-bar"></div>
</div>
<div class="leaderboard-search-controls">
<div class="size-mode-toggle" role="group" aria-label="Model size categories">
<button class="filter-btn active" id="size-mode-default" type="button" onclick="setSizeMode('default')" aria-pressed="true" aria-controls="filter-bar size-leader-grid highlight-leader-grid" title="Under 150M and smaller categories">Default</button>
<button class="filter-btn" id="size-mode-extended" type="button" onclick="setSizeMode('extended')" aria-pressed="false" aria-controls="filter-bar size-leader-grid highlight-leader-grid" title="Default categories plus All sizes and under 250M">Extended</button>
</div>
<label class="leaderboard-search">
<span class="filter-label">Search models</span>
<input class="leaderboard-search-input" id="leaderboard-search" type="search" placeholder="Name or organization…" autocomplete="off" spellcheck="false">
</label>
</div>
</div>
<div class="filter-leader-grid" id="size-leader-grid" aria-label="Best model in every size filter"></div>
<div class="leaderboard-table-shell" id="leaderboard-table-shell">
<button class="extra-columns-tab" id="extra-columns-toggle" type="button" onclick="toggleExtraColumns()" aria-label="Show more columns" aria-expanded="false" aria-controls="leaderboard-table" title="Show more columns">
<span class="extra-columns-glyph" aria-hidden="true">&rsaquo;</span>
</button>
<div class="table-wrap">
<table id="leaderboard-table">
<thead>
<tr>
<th class="th-compare">Compare</th>
<th onclick="sortTable('rank')"># <span class="sort-indicator">β–Ό</span></th>
<th onclick="sortTable('name')">Model <span class="sort-indicator"></span></th>
<th onclick="sortTable('params')">Params <span class="sort-indicator"></span></th>
<th onclick="sortTable('intelligenceIndex')" title="Intelligence Index: chance-normalized; ArithMark-3 weight 0.65, other components weight 1">Int Index <span class="sort-indicator"></span></th>
<th onclick="sortTable('hellaswag')">HellaSwag <span class="sort-indicator"></span></th>
<th onclick="sortTable('arc')">ARC-Easy <span class="sort-indicator"></span></th>
<th onclick="sortTable('arcChall')">ARC-Challenge <span class="sort-indicator"></span></th>
<th onclick="sortTable('piqa')">PIQA <span class="sort-indicator"></span></th>
<th onclick="sortTable('arithmark3')">ArithMark-3 <span class="sort-indicator"></span></th>
<th class="extra-col extra-col-start" onclick="sortTable('avg')">Avg <span class="sort-indicator"></span></th>
<th class="extra-col" onclick="sortTable('fitZ')">Fit Std Devs <span class="sort-indicator"></span></th>
<th class="extra-col" onclick="sortTable('arithmark2')">ArithMark-2 <span class="sort-indicator"></span></th>
</tr>
</thead>
<tbody id="leaderboard-body"></tbody>
</table>
</div>
</div>
</section>
<!-- COMPARE -->
<section id="compare" style="padding-top:80px;">
<div class="compare-head">
<div>
<h2 class="section-title">Compare</h2>
<p class="section-sub">Models you add from the leaderboard line up side by side here. <span id="compare-count-label" class="compare-count">None selected</span></p>
</div>
<div class="compare-head-actions">
<span id="compare-limit-note" class="compare-limit-note">Up to 6 models at a time</span>
<button class="filter-btn" onclick="clearCompare()">Clear all</button>
</div>
</div>
<div id="compare-content"></div>
</section>
<!-- BAR CHART -->
<section id="score-chart" style="padding-top:80px;">
<h2 class="section-title">Scores</h2>
<p class="section-sub" id="score-chart-sub">Top scores for the active size and benchmark filters.</p>
<div class="filter-group">
<span class="filter-label">Benchmark</span>
<div class="filter-bar" id="benchmark-filter-bar"></div>
<div class="filter-leader-grid" id="benchmark-leader-grid" aria-label="Best model in every benchmark filter"></div>
</div>
<div class="chart-card">
<h3>Top Intelligence Index Scores</h3>
<canvas id="barChart"></canvas>
</div>
</section>
<!-- SCATTER PLOT -->
<section id="charts" style="padding-top:80px;">
<h2 class="section-title">Efficiency</h2>
<p class="section-sub" id="efficiency-sub">Score vs parameter count (log scale). Shaded zone = above regression line.</p>
<div class="legend-bar" id="legend-bar"></div>
<div class="chart-card">
<h3>Intelligence Index vs Log Parameters</h3>
<canvas id="scatterChart"></canvas>
</div>
</section>
<!-- LEADERBOARD HISTORY -->
<section id="org-performance" style="padding-top:80px;">
<h2 class="section-title">Leaderboard History</h2>
<p class="section-sub" id="history-period">Indie model leadership across the &lt;150M, &lt;100M, &lt;50M, &lt;10M and &lt;3M categories.</p>
<div class="chart-card history-chart">
<h3>Intelligence frontier over time</h3>
<div id="history-frontier"></div>
<p class="history-note" id="history-frontier-note">Each step marks a new category leader, using addition dates and current II. Colours identify the lab. Hover a dot for model details; select a category to show or hide its line.</p>
</div>
<div class="chart-card history-chart">
<h3>Category dominance over time</h3>
<div id="history-dominance"></div>
</div>
<div class="chart-card history-chart">
<h3>Labs with the most #1 class-days</h3>
<div id="history-labs"></div>
<p class="history-note">Size categories overlap. A lab leading two categories for one day earns two class-days.</p>
</div>
<div class="chart-card history-chart">
<h3>Most dominant models</h3>
<div id="history-models"></div>
<p class="history-note">Total #1 class-days per model, summed across the same five size categories.</p>
</div>
<details class="history-method">
<summary>How leadership is counted</summary>
<p>Each currently listed, uncommented model joins the timeline on its stored addition date. Rankings throughout the timeline use the model's current Intelligence Index, parameter count and organization. For each category, the highest-scoring eligible model receives each calendar day from its lead's start through the day before the next leader arrives. Exact ties follow the leaderboard's model order. Hugging Face, Meta, OpenAI, Google and Microsoft entries are excluded from these indie lab charts.</p>
<p>The timeline is reconstructed from addition dates and current scores, rather than past score revisions. Totals include today in Australia/Sydney and are recalculated when the page loads.</p>
</details>
</section>
<!-- LEADERBOARD RULES -->
<section id="rules">
<h2 class="section-title">Rules</h2>
<p class="section-sub">Eligibility, evaluation, and submission requirements for the Open SLM Leaderboard.</p>
<div class="rules-grid">
<article class="rule-card">
<span class="rule-number">RULE 01</span>
<h3>Stay below 400M parameters</h3>
<p>The leaderboard is for language models with fewer than 400 million parameters.</p>
</article>
<article class="rule-card">
<span class="rule-number">RULE 02</span>
<h3>Train it from scratch</h3>
<p>The submitted model must be pretrained from scratch by you or your organization.</p>
</article>
<article class="rule-card">
<span class="rule-number">RULE 03</span>
<h3>Publish open weights</h3>
<p>Model weights must be openly available and point to a genuine training checkpoint, not a merged model.</p>
</article>
<article class="rule-card">
<span class="rule-number">RULE 04</span>
<h3>Use the listed evaluations</h3>
<p>Provide zero-shot results for the listed benchmarks using LM-eval harness or the linked custom ArithMark script.</p>
</article>
<article class="rule-card">
<span class="rule-number">RULE 05</span>
<h3>Results are verified</h3>
<p>Submitted results are independently checked by the leaderboard team before they are merged.</p>
</article>
<article class="rule-card">
<span class="rule-number">RULE 06</span>
<h3>Submit through the Space</h3>
<p>Open a PR or discussion with the benchmark results in the <a href="https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard/discussions" target="_blank">leaderboard Space</a>.</p>
</article>
</div>
</section>
<!-- ABOUT / ADD MODEL -->
<section id="about" style="padding-top:80px;">
<div class="about-box">
<h3>Add your model </h3>
<p>Open a PR or discussion on this Space with your model's results for the given benchmarks. They will be independently verified by our team and then your PR will be merged. Your model must be pretrained by you from scratch, open weights and be a training checkpoint (not merged) to qualify <a href="https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard/discussions" target="_blank">Open a PR β†’</a></p>
</div>
</section>
<!-- INTELLIGENCE INDEX METHODOLOGY -->
<section id="int-index-method" style="padding-top:32px;">
<div class="about-box index-method-card">
<h3>How the Intelligence Index is calculated</h3>
<p>Each benchmark is first adjusted for its random-chance floor, so chance performance maps to 0 and perfect performance maps to 100.</p>
<div class="index-formula">
N(score, chance) = 100 &times; (score &minus; chance) &divide; (100 &minus; chance)<br>
Int Index = (HellaSwag + ARC + PIQA + 0.65 &times; ArithMark-3) &divide; 3.65
</div>
<div class="index-weight-grid">
<div class="index-weight"><strong>HellaSwag</strong><span>weight 1.00<br>chance 25</span></div>
<div class="index-weight"><strong>Combined ARC</strong><span>weight 1.00<br>chance 25</span></div>
<div class="index-weight"><strong>PIQA</strong><span>weight 1.00<br>chance 50</span></div>
<div class="index-weight"><strong>ArithMark-3</strong><span>weight 0.65<br>chance 25</span></div>
</div>
<p class="index-method-note">Combined ARC is the mean of ARC-Easy and ARC-Challenge before normalization. When a component is unavailable, its weight is removed from both the numerator and denominator; at least two components are required. Scores below chance may be negative. ArithMark-2 is displayed separately and is not included in the index.</p>
</div>
</section>
<!-- FOOTER -->
<footer>
<div>Open SLM Leaderboard by <a href="https://huggingface.co/AxiomicLabs" style="margin:0;">Axiomic Labs</a></div>
<div>
<a href="#leaderboard" onclick="return scrollToSection('leaderboard')">Leaderboard</a>
<a href="#compare" onclick="return scrollToSection('compare')">Compare</a>
<a href="#score-chart" onclick="return scrollToSection('score-chart')">Scores</a>
<a href="#charts" onclick="return scrollToSection('charts')">Efficiency</a>
<a href="#org-performance" onclick="return scrollToSection('org-performance')">History</a>
<a href="#rules" onclick="return scrollToSection('rules')">Rules</a>
<a href="#int-index-method" onclick="return scrollToSection('int-index-method')">Int Index</a>
</div>
<div style="font-size:10px;">All results independently verified using our internal verification process.</div>
</footer>
</div>
<!-- STICKY COMPARE BAR -->
<div id="compare-bar" class="compare-bar"></div>
<script>
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benchlabs: { name: 'Bench Labs', chartColor: 'rgba(192, 38, 211, 0.70)', chartBorder: '#c026d3', url: 'https://huggingface.co/bench-labs' },
surjolabs: { name: 'SurjoLabs', chartColor: 'rgba(255, 153, 51, 0.70)', chartBorder: '#ff9933', url: 'https://huggingface.co/SurjoLabs' },
codesoft: { name: 'CodeSoft', chartColor: 'rgba(80, 162, 255, 0.70)', chartBorder: '#50a2ff', url: 'https://huggingface.co/CodeSoft' },
sz14: { name:'sz14', chartColor: 'rgba(90, 125, 192, 0.70)', chartBorder:'#5A7DC0', url: 'https://huggingface.co/sz14' },
cnwplayer: { name:'CNWPlayer', chartColor: 'rgba(37, 99, 235, 0.70)', chartBorder: '#2563eb', url: 'https://huggingface.co/CNWPlayer/'},
specklabs: { name:'SpeckLabs', chartColor: 'rgba(30, 58, 95, 0.70)', chartBorder: '#1e3a5f', url:'https://huggingface.co/specklabs/'},
qikp: { name: 'Charles', chartColor: 'rgba(22, 159, 111, 0.70)', chartBorder: '#169f6f', url:'https://huggingface.co/qikp/'},
dalab: { name: 'DALab', chartColor: 'rgba(139, 94, 60, 0.70)', chartBorder: '#8b5e3c', url: 'https://huggingface.co/DALabCommunity/' },
fwkv: { name: 'FWKV', chartColor: 'rgba(148, 163, 184, 0.70)', chartBorder: '#94a3b8', url: 'https://huggingface.co/FWKV'},
'basically-ai': { name: 'basically AI', chartColor: 'rgba(163, 166, 0, 0.70)', chartBorder: '#a3a600', url: 'https://huggingface.co/basically-ai' },
dedeprogames: { name: 'DedeProGames', chartColor: 'rgba(20, 184, 166, 0.70)', chartBorder: '#14b8a6', url: 'https://huggingface.co/DedeProGames' },
slmarchive: { name: 'SLM-Archive', chartColor: 'rgba(132, 169, 140, 0.70)', chartBorder: '#84a98c', url: 'https://huggingface.co/SLM-Archive' },
nikity: { name: 'Nikity', chartColor: 'rgba(251, 146, 60, 0.70)', chartBorder: '#fb923c', url: 'https://huggingface.co/Nikity' },
opengcm: { name: 'OpenGCM', chartColor: 'rgba(34, 211, 238, 0.70)', chartBorder: '#22d3ee', url: 'https://huggingface.co/OPENGCM' },
mystery: { name: 'Mystery', chartColor: 'rgba(128, 128, 128, 0.7)', chartBorder: '#808080', url: 'https://huggingface.co/' },
ivmelabs: { name: 'IvmeLabs', chartColor: 'rgba(255, 0, 0, 0.70)', chartBorder: '#ff0000', url: 'https://huggingface.co/IvmeLabs' },
aurora: { name: 'AuroraAI Research', chartColor: 'rgba(130, 52, 168, 0.70)', chartBorder: '#8234A8', url: 'https://huggingface.co/AuroraAI-Research' },
alexliap: { name: 'alexliap', chartColor: 'rgba(219, 39, 119, 0.70)', chartBorder: '#db2777', url: 'https://huggingface.co/alexliap' },
slayerlab: { name: 'Fabryka AI', chartColor: 'rgba(2, 132, 199, 0.70)', chartBorder: '#0284c7', url: 'https://huggingface.co/SlayerLab' },
forgeworks: { name: 'ForgeWorks', chartColor: 'rgba(249, 124, 33, 0.70)', chartBorder: '#f97c21', url: 'https://huggingface.co/ForgeWorks' },
saicr: { name: 'saicr', chartColor: 'rgba(6, 78, 59, 0.70)', chartBorder: '#064e3b', url: 'https://huggingface.co/saicr' },
hoglet33: { name: 'Hoglet', chartColor: 'rgba(45, 140, 210, 0.70)', chartBorder: '#2d8cd2', url: 'https://huggingface.co/Hoglet-33' },
techdotus: { name: 'Tech.us', chartColor: 'rgba(67, 136, 255, 0.70)', chartBorder: '#4388ff', url: 'https://huggingface.co/techdotus' },
glossia: { name: 'Glossia', chartColor: 'rgba(255, 175, 204, 0.70)', chartBorder: '#ffafcc', url: 'https://huggingface.co/Glossia' },
jonnester: { name: 'Jonnester', chartColor: 'rgba(59, 130, 246, 0.70)', chartBorder: '#3b82f6', url: 'https://huggingface.co/Jonnester' },
unluckyexe: { name: 'Unluckyexe', chartColor: 'rgba(16, 185, 129, 0.70)', chartBorder: '#10b981', url: 'https://huggingface.co/Unluckyexe' },
dotlabs: { name: 'dotlabs', chartColor: 'rgba(225, 225, 225, 0.70)', chartBorder: '#e1e1e1', url: 'https://huggingface.co/dotlabs' },
algodriveai: { name: 'AlgoDriveAI', chartColor: 'rgba(232, 69, 44, 0.70)', chartBorder: '#e8452c', url: 'https://huggingface.co/AlgoDriveAI' },
heronetic: { name: 'Heronetic', chartColor: 'rgba(100, 84, 0, 0.70)', chartBorder: '#645400', url: 'https://huggingface.co/Heronetic' },
tinybrainbot: { name: 'TinyBrainBot', chartColor: 'rgba(14, 165, 164, 0.70)', chartBorder: '#0ea5a4', url: 'https://huggingface.co/nkthebass' },
liquid: { name: 'LiquidAI', chartColor: 'rgba(255, 175, 204, 0.69)', chartBorder: '#ffafcc', url: 'https://huggingface.co/LiquidAI' },
mica: { name: 'MICA', chartColor: 'rgba(249, 115, 22, 0.70)', chartBorder: '#f97316', url: 'https://huggingface.co/vynly' },
RuVi: { name: 'RuVI AI', chartColor: 'rgba(132, 204, 22, 0.70)', chartBorder: '#84cc16', url: 'https://huggingface.co/RuVI-AI' },
murzik344: { name: 'Murzik344', chartColor: 'rgba(216, 27, 43, 0.70)', chartBorder: '#d81b2b', url: 'https://huggingface.co/Murzik344' },
littlekedi: { name: 'LittleKedi', chartColor: 'rgba(214, 140, 69, 0.70)', chartBorder: '#d68c45', url: 'https://huggingface.co/edededdy' },
ls: { name: 'LS', chartColor: 'rgba(232, 144, 144, 0.70)', chartBorder: '#e89090', url: 'https://huggingface.co/Dsg2' },
meridianmrm: { name: 'Meridian-MRM', chartColor: 'rgba(163, 230, 53, 0.70)', chartBorder: '#a3e635', url: 'https://huggingface.co/Meridian-MRM' },
};
const MODELS = [ // ADD NEW MODELS TO BOTTOM OF LIST
// addedDate records the first leaderboard appearance; it is stored metadata only.
// Backfill missing dates with python scripts/update_history.py; charts calculate from MODELS on page load.
// -- Models with full benchmark data --
{ name: 'Nexus-Erebus-3M', org: 'ideoalabs', addedDate: '2026-07-13', params: 3000384, paramsDisplay: '3.0M', arc: 27.02, hellaswag: 26.59, piqa: 51.41, arcChall: 20.65, arithmark3: 29.20, arithmark2: 70.72, links: { card: 'https://huggingface.co/MaliosDark/Nexus-Erebus-3M' } },
{ name: 'Nexus-Erebus-50M', org: 'ideoalabs', addedDate: '2026-07-10', params: 54100000, paramsDisplay: '54M', arc: 44.40, hellaswag: 27.05, piqa: 58.27, arcChall: 22.70, arithmark3: 33.10, arithmark2: 52.48, links: { card: 'https://huggingface.co/MaliosDark/Nexus-Erebus-50M' } },
{ name: 'SmolLM2-135M', org: 'huggingface', addedDate: '2026-05-27', params: 135000000, paramsDisplay: '135M', arc: 58.63, hellaswag: 43.22, piqa: 68.44, arcChall: 29.69, arithmark3: 39.20, arithmark2: 32.68, links: { card: 'https://huggingface.co/HuggingFaceTB/SmolLM2-135M' } },
{ name: 'GPT-X2-125M', org: 'axiomiclabs', addedDate: '2026-05-27', params: 125000000, paramsDisplay: '125M', arc: 51.47, hellaswag: 40.41, piqa: 67.30, arcChall: 27.82, arithmark3: 37.20, arithmark2: 30.68, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X2-125M' } },
{ name: 'GPT-X-125M', org: 'axiomiclabs', addedDate: '2026-05-27', params: 125000000, paramsDisplay: '125M', arc: 50.76, hellaswag: 36.57, piqa: 64.96, arcChall: 26.62, arithmark3: 35.60, arithmark2: 30.24, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X-125M' } },
{ name: 'MobileLLM-R1-140M-base', org: 'facebook', addedDate: '2026-05-27', params: 140000000, paramsDisplay: '140M', arc: 49.92, hellaswag: 33.84, piqa: 63.22, arcChall: 24.74, arithmark3: 65.70, arithmark2: 53.56, links: { card: 'https://huggingface.co/facebook/MobileLLM-R1-140M-base' } },
{ name: 'Supra-50M-Base', org: 'supralabs', addedDate: '2026-05-27', params: 51786240, paramsDisplay: '52M', arc: 45.88, hellaswag: 31.83, piqa: 62.51, arcChall: 25.00, arithmark3: 36.30, arithmark2: 27.04, links: { card: 'https://huggingface.co/SupraLabs/Supra-50M-Base' } },
{ name: 'Shard-1', org: 'compactai', addedDate: '2026-05-27', params: 54500000, paramsDisplay: '54.5M', arc: 41.12, hellaswag: 29.20, piqa: 58.22, arcChall: 20.99, arithmark2: 26.80, links: { card: 'https://huggingface.co/CompactAI-O/Shard-1' } },
{ name: 'Escarda-86M', org: 'quazim0t0', addedDate: '2026-06-16', params: 85656068, paramsDisplay: '85.7M', arc: 35.98, hellaswag: 30.32, piqa: 58.49, arcChall: 22.95, arithmark2: 29.36, links: { card: 'https://huggingface.co/Quazim0t0/Escarda-86M' } },
{ name: 'Escarda-86M-Base', org: 'quazim0t0', addedDate: '2026-06-16', params: 85656068, paramsDisplay: '85.7M', arc: 37.92, hellaswag: 28.02, piqa: 58.16, arcChall: 18.86, arithmark2: 25.36, links: { card: 'https://huggingface.co/Quazim0t0/Escarda-86M-Base' } },
{ name: 'Supra-50M-Instruct', org: 'supralabs', addedDate: '2026-05-27', params: 51786240, paramsDisplay: '52M', arc: 44.40, hellaswag: 29.09, piqa: 59.47, arcChall: 27.30, arithmark3: 33.90, arithmark2: 29.12, links: { card: 'https://huggingface.co/SupraLabs/Supra-50M-Instruct' } },
{ name: 'Supra-50M-Reasoning', org: 'supralabs', addedDate: '2026-06-04', params: 51786240, paramsDisplay: '52M', arc: 44.44, hellaswag: 29.10, piqa: 59.30, arcChall: 27.39, arithmark3: 35.00, arithmark2: 28.96, links: { card: 'https://huggingface.co/SupraLabs/Supra-50M-Reasoning' } },
{ name: 'SmolLM-135M', org: 'huggingface', addedDate: '2026-05-27', params: 135000000, paramsDisplay: '135M', arc: 56.31, hellaswag: 42.70, piqa: 68.28, arcChall: 29.01, arithmark3: 36.80, arithmark2: 28.84, links: { card: 'https://huggingface.co/HuggingFaceTB/SmolLM-135M' } },
{ name: 'OPT-125M', org: 'facebook', addedDate: '2026-05-27', params: 125000000, paramsDisplay: '125M', arc: 40.28, hellaswag: 31.31, piqa: 62.24, arcChall: 22.70, arithmark3: 35.20, arithmark2: 24.40, links: { card: 'https://huggingface.co/facebook/opt-125m' } },
{ name: 'GPT-S-5M', org: 'axiomiclabs', addedDate: '2026-05-27', params: 5160000, paramsDisplay: '5.2M', arc: 33.21, hellaswag: 27.46, piqa: 57.24, arcChall: 21.16, arithmark3: 30.20, arithmark2: 27.12, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-S-5M' } },
{ name: 'GPT-2', org: 'openai', addedDate: '2026-05-27', params: 124000000, paramsDisplay: '124M', arc: 39.35, hellaswag: 31.26, piqa: 62.08, arcChall: 22.35, arithmark3: 35.70, arithmark2: 26.48, links: { card: 'https://huggingface.co/openai-community/gpt2' } },
{ name: 'Spark-5M-Base-v4', org: 'lhtechai', addedDate: '2026-05-27', params: 5000000, paramsDisplay: '5M', arc: 33.16, hellaswag: 27.03, piqa: 53.32, arcChall: 21.50, arithmark3: 29.30, arithmark2: 25.00, links: { card: 'https://huggingface.co/LH-Tech-AI/Spark-5M-Base-v4' } },
{ name: 'Supra-Mini-v5-8M', org: 'supralabs', addedDate: '2026-05-27', params: 7870000, paramsDisplay: '7.87M', arc: 33.21, hellaswag: 26.37, piqa: 54.03, arcChall: 21.16, arithmark3: 31.50, arithmark2: 24.28, links: { card: 'https://huggingface.co/SupraLabs/Supra-Mini-v5-8M' } },
{ name: 'Pythia-70M', org: 'eleutherai', addedDate: '2026-05-27', params: 70000000, paramsDisplay: '70M', arc: 31.65, hellaswag: 27.49, piqa: 53.48, arcChall: 23.63, arithmark3: 27.00, arithmark2: 25.32, links: { card: 'https://huggingface.co/EleutherAI/pythia-70m' } },
{ name: 'Supra-Mini-v4-2M', org: 'supralabs', addedDate: '2026-05-27', params: 2620000, paramsDisplay: '2.6M', arc: 30.98, hellaswag: 25.52, piqa: 51.90, arcChall: 21.50, arithmark3: 28.30, arithmark2: 24.08, links: { card: 'https://huggingface.co/SupraLabs/Supra-Mini-v4-2M' } },
{ name: 'Pythia-31M', org: 'eleutherai', addedDate: '2026-05-27', params: 31000000, paramsDisplay: '31M', arc: 33.88, hellaswag: 27.14, piqa: 56.26, arcChall: 21.67, arithmark3: 34.90, arithmark2: 27.20, links: { card: 'https://huggingface.co/EleutherAI/pythia-31m' } },
{ name: 'Stentor3-50M', org: 'stentor', addedDate: '2026-05-27', params: 50000000, paramsDisplay: '50M', arc: 29.67, hellaswag: 27.10, piqa: 53.75, arcChall: 21.67, arithmark3: 33.30, arithmark2: 29.48, links: { card: 'https://huggingface.co/StentorLabs/Stentor3-50M' } },
{ name: 'Stentor3-20M', org: 'stentor', addedDate: '2026-05-27', params: 20000000, paramsDisplay: '20M', arc: 29.50, hellaswag: 27.06, piqa: 55.06, arcChall: 23.12, arithmark3: 32.60, arithmark2: 26.72, links: { card: 'https://huggingface.co/StentorLabs/Stentor3-20M' } },
{ name: 'Portimbria-150M', org: 'stentor', addedDate: '2026-05-29', params: 151026432, paramsDisplay: '151M', arc: 35.82, hellaswag: 27.09, piqa: 58.27, arcChall: 18.77, arithmark3: 34.80, arithmark2: 28.04, links: { card: 'https://huggingface.co/StentorLabs/Portimbria-150M' } },
{ name: 'nanowhale-100m-base', org: 'huggingface', addedDate: '2026-05-27', params: 100000000, paramsDisplay: '100M', arc: 28.79, hellaswag: 26.31, piqa: 51.80, arcChall: 24.83, arithmark3: 26.60, arithmark2: 25.20, links: { card: 'https://huggingface.co/HuggingFaceTB/nanowhale-100m-base' } },
{ name: 'Pythia-14M', org: 'eleutherai', addedDate: '2026-05-27', params: 14000000, paramsDisplay: '14M', arc: 32.28, hellaswag: 26.20, piqa: 55.88, arcChall: 20.99, arithmark3: 33.40, arithmark2: 27.04, links: { card: 'https://huggingface.co/EleutherAI/pythia-14m' } },
{ name: 'Tenete-8M', org: 'harleyml', addedDate: '2026-05-27', params: 8000000, paramsDisplay: '8M', arc: 31.69, hellaswag: 26.75, piqa: 55.66, arcChall: 21.84, arithmark3: 26.20, arithmark2: 26.72, links: { card: 'https://huggingface.co/Harley-ml/Tenete-8M' } },
{ name: 'Dillion-1.2M', org: 'harleyml', addedDate: '2026-05-27', params: 1281384, paramsDisplay: '1.3M', arc: 31.19, hellaswag: 26.65, piqa: 53.05, arcChall: 22.78, arithmark3: 28.00, arithmark2: 24.80, links: { card: 'https://huggingface.co/Harley-ml/Dillion-1.2M' } },
{ name: 'CinnabarLM-1.4M-Base', org: 'mihaipopa', addedDate: '2026-05-27', params: 1510000, paramsDisplay: '1.5M', arc: 28.54, hellaswag: 27.08, piqa: 52.50, arcChall: 23.38, arithmark3: 29.20, arithmark2: 24.96, links: { card: 'https://huggingface.co/MihaiPopa-1/CinnabarLM-1.4M-Base' } },
{ name: 'CinnabarLM-4M-Base', org: 'mihaipopa', addedDate: '2026-05-27', params: 4230000, paramsDisplay: '4.2M', arc: 28.28, hellaswag: 27.71, piqa: 52.29, arcChall: 22.70, arithmark3: 29.40, arithmark2: 24.96, links: { card: 'https://huggingface.co/MihaiPopa-1/CinnabarLM-4M-Base' } },
{ name: 'CinnabarLM-1.5M-Base', org: 'mihaipopa', addedDate: '2026-05-27', params: 1710000, paramsDisplay: '1.7M', arc: 28.11, hellaswag: 27.08, piqa: 52.94, arcChall: 21.93, arithmark3: 26.40, arithmark2: 25.20, links: { card: 'https://huggingface.co/MihaiPopa-1/CinnabarLM-1.5M-Base' } },
{ name: 'Dillionv2-1.3M', org:'harleyml', addedDate: '2026-05-29', params: 1285200, paramsDisplay: '1.3M', arc: 29.71, hellaswag: 27.27, piqa: 53.05, arcChall: 22.44, arithmark3: 31.50, arithmark2: 27.00, links: { card: 'https://huggingface.co/Harley-ml/Dillionv2-1.3M' } },
{ name: 'KeyLM-75M', org:'eclipsesenpai', addedDate: '2026-05-30',params: 75251200, paramsDisplay: '75M', arc: 35.73, hellaswag: 29.66, piqa: 60.50, arcChall: 23.98, arithmark3: 30.10, arithmark2: 25.80, links: { card: 'https://huggingface.co/Eclipse-Senpai/KeyLM-75M' } },
{ name: 'Supra-Mini-v6-1M', org: 'supralabs', addedDate: '2026-05-30', params: 1410688, paramsDisplay: '1.4M', arc: 30.68, hellaswag: 27.23, piqa: 53.70, arcChall: 20.48, arithmark3: 26.40, arithmark2: 26.48, links: { card: 'https://huggingface.co/SupraLabs/Supra-Mini-v6-1M' } },
{ name: 'GPT-S-1.4M', org: 'axiomiclabs', addedDate: '2026-06-01', params: 1426000, paramsDisplay: '1.4M', arc: 31.57, hellaswag: 26.89, piqa: 55.17, arcChall: 21.93, arithmark3: 30.20, arithmark2: 25.16, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-S-1.4M' } },
{ name: 'Archaea-74M', org: 'godelev', addedDate: '2026-06-02', params: 74016256, paramsDisplay: '74M', arc: 39.06, hellaswag: 27.27, piqa: 58.27, arcChall: 22.70, arithmark3: 35.50, arithmark2: 29.20, links: { card: 'https://huggingface.co/GODELEV/Archaea-74M' } },
{ name: 'Cali-0.1B', org: 'sandroeth', addedDate: '2026-06-02', params: 123782400, paramsDisplay: '124M', arc: 27.53, hellaswag: 26.84, piqa: 52.12, arcChall: 24.49, arithmark3: 32.90, arithmark2: 24.72, links: { card: 'https://huggingface.co/Sandroeth/cali-0.1B' } },
{ name: 'Quark-50M', org: 'thingai', addedDate: '2026-06-04', params: 56666496, paramsDisplay: '57M', arc: 36.78, hellaswag: 28.48, piqa: 57.83, arcChall: 25.00, arithmark3: 34.90, arithmark2: 28.20, links: { card: 'https://huggingface.co/ThingAI/Quark-50m' } },
{ name: 'Quark-135M', org: 'thingai', addedDate: '2026-06-04', params: 134561088, paramsDisplay: '135M', arc: 47.73, hellaswag: 31.33, piqa: 58.32, arcChall: 28.24, arithmark3: 39.70, arithmark2: 40.32, links: { card: 'https://huggingface.co/ThingAI/Quark-135m' } },
{ name: 'Veyra-30M-Base', org: 'veyraai', addedDate: '2026-06-03', params: 34611712, paramsDisplay: '35M', arc: 35.90, hellaswag: 27.92, piqa: 58.92, arcChall: 24.15, arithmark3: 28.90, arithmark2: 26.76, links: { card: 'https://huggingface.co/veyra-ai/Veyra-30M-Base' } },
{ name: 'Syn-2.6M', org: 'fromzero', addedDate: '2026-06-07', params: 2604210, paramsDisplay: '2.6M', arc: 32.03, hellaswag: 26.96, piqa: 53.65, arcChall: 20.39, arithmark3: 27.60, arithmark2: 26.68, links: { card: 'https://huggingface.co/fromziro/Syn-2.6M' } },
{ name: 'Er-Medium-12.5M', org: 'fromzero', addedDate: '2026-06-07', params: 12497520, paramsDisplay: '12.5M', arc: 35.10, hellaswag: 28.50, piqa: 57.51, arcChall: 20.73, arithmark3: 32.50, arithmark2: 30.88, links: { card: 'https://huggingface.co/fromziro/Er-Medium-12.5M' } },
{ name: 'michel-tiny', org: 'finnianx', addedDate: '2026-06-09', params: 55719040, paramsDisplay: '56M', arc: 37.37, hellaswag: 28.15, piqa: 57.34, arcChall: 21.76, arithmark3: 28.70, arithmark2: 25.28, links: { card: 'https://huggingface.co/finnianx/michel-tiny' } },
{ name: 'OdinNext-138M-Base', org: 'joelhenwang', addedDate: '2026-06-09', params: 138449696, paramsDisplay: '138M', arc: 45.08, hellaswag: 28.09, piqa: 59.52, arcChall: 23.81, arithmark3: 41.80, arithmark2: 36.84, links: { card: 'https://huggingface.co/joelhenwang/OdinNext-138M-Base' } },
{ name: 'OdinNext-138M-Instruct', org: 'joelhenwang', addedDate: '2026-06-09', params: 138451232, paramsDisplay: '138M', arc: 44.40, hellaswag: 28.86, piqa: 58.65, arcChall: 23.12, arithmark3: 40.90, arithmark2: 36.56, links: { card: 'https://huggingface.co/joelhenwang/OdinNext-138M-Instruct' } },
{ name: 'michel-micro', org: 'finnianx', addedDate: '2026-06-10', params: 28355072, paramsDisplay: '28M', arc: 38.51, hellaswag: 28.16, piqa: 57.62, arcChall: 23.29, arithmark3: 29.20, arithmark2: 26.04, links: { card: 'https://huggingface.co/finnianx/michel-micro' } },
{ name: 'kirk-tung', org: 'rtc2022', addedDate: '2026-06-10', params: 53111296, paramsDisplay: '53M', arc: 30.43, hellaswag: 26.32, piqa: 52.61, arcChall: 22.01, arithmark3: 29.40, arithmark2: 24.88, links: { card: 'https://huggingface.co/rtc2022/kirk-tung' } },
{ name: 'ettin-decoder-32m', org: 'jhuclsp', addedDate: '2026-06-11', params: 32081344, paramsDisplay: '32M', arc: 31.73, hellaswag: 27.79, piqa: 52.29, arcChall: 22.01, arithmark3: 32.70, arithmark2: 26.80, links: { card: 'https://huggingface.co/jhu-clsp/ettin-decoder-32m' } },
{ name: 'ettin-decoder-17m', org: 'jhuclsp', addedDate: '2026-06-11', params: 16913600, paramsDisplay: '17M', arc: 31.65, hellaswag: 27.39, piqa: 53.37, arcChall: 22.27, arithmark3: 31.70, arithmark2: 28.48, links: { card: 'https://huggingface.co/jhu-clsp/ettin-decoder-17m' } },
{ name: 'ettin-decoder-68m', org: 'jhuclsp', addedDate: '2026-06-11', params: 68457664, paramsDisplay: '68M', arc: 32.74, hellaswag: 27.81, piqa: 52.18, arcChall: 23.04, arithmark3: 30.30, arithmark2: 30.00, links: { card: 'https://huggingface.co/jhu-clsp/ettin-decoder-68m' } },
{ name: 'ettin-decoder-150m', org: 'jhuclsp', addedDate: '2026-06-11', params: 149655232, paramsDisplay: '150M', arc: 29.97, hellaswag: 25.95, piqa: 51.25, arcChall: 24.23, arithmark3: 28.00, arithmark2: 26.60, links: { card: 'https://huggingface.co/jhu-clsp/ettin-decoder-150m' } },
{ name: 'Er-Tiny-1.3M', org: 'fromzero', addedDate: '2026-06-11', params: 1332744, paramsDisplay: '1.3M', arc: 30.09, hellaswag: 27.67, piqa: 52.72, arcChall: 22.35, arithmark3: 28.60, arithmark2: 26.80, links: { card: 'https://huggingface.co/fromziro/Er-Tiny-1.3M' } },
{ name: 'michel-nano', org: 'finnianx', addedDate: '2026-06-11', params: 5963008, paramsDisplay: '6M', arc: 33.29, hellaswag: 27.12, piqa: 54.79, arcChall: 22.70, arithmark3: 30.90, arithmark2: 26.00, links: { card: 'https://huggingface.co/finnianx/michel-nano' } },
{ name: 'Supra-1.5-50M-base-exp', org: 'supralabs', addedDate: '2026-06-11', params: 51786240, paramsDisplay: '52M', arc: 48.40, hellaswag: 29.78, piqa: 60.01, arcChall: 25.51, arithmark3: 34.40, arithmark2: 31.32, links: { card: 'https://huggingface.co/SupraLabs/Supra-1.5-50M-base-exp' } },
{ name: 'Ant-10M', org: 'godelev', addedDate: '2026-06-12', params: 9902336, paramsDisplay: '10M', arc: 27.53, hellaswag: 26.96, piqa: 50.65, arcChall: 28.24, arithmark3: 28.00, arithmark2: 24.48, links: { card: 'https://huggingface.co/GODELEV/Ant-19m' } },
{ name: 'Supra-1.5-50M-Instruct-exp', org: 'supralabs', addedDate: '2026-06-13', params: 51786240, paramsDisplay: '52M', arc: 43.94, hellaswag: 29.26, piqa: 59.41, arcChall: 26.11, arithmark3: 34.90, arithmark2: 29.80, links: { card: 'https://huggingface.co/SupraLabs/Supra-1.5-50M-Instruct-exp' } },
{ name: 'SLM-10M', org: 'liodonai', addedDate: '2026-06-13', params: 9968640, paramsDisplay: '10M', arc: 35.52, hellaswag: 27.40, piqa: 57.07, arcChall: 23.46, arithmark3: 27.60, arithmark2: 26.40, links: { card: 'https://huggingface.co/liodon-ai/slm-10m' } },
{ name: 'Doge-20M', org: 'smalldoge', addedDate: '2026-06-14', params: 13118728, paramsDisplay: '13.1M', arc: 33.38, hellaswag: 26.61, piqa: 54.30, arcChall: 20.73, arithmark3: 29.70, arithmark2: 29.08, links: { card: 'https://huggingface.co/SmallDoge/Doge-20M' } },
{ name: 'Veyra2-Apricot-50M-Base', org: 'veyraai', addedDate: '2026-06-16', params: 49303040, paramsDisplay: '49M', arc: 42.47, hellaswag: 31.28, piqa: 62.13, arcChall: 23.29, arithmark3: 31.60, arithmark2: 28.96, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Apricot-50M-Base' } },
{ name: 'michel-nano-v2', org: 'finnianx', addedDate: '2026-06-16', params: 9938176, paramsDisplay: '9.9M', arc: 36.07, hellaswag: 27.36, piqa: 56.96, arcChall: 21.84, arithmark3: 30.20, arithmark2: 25.04, links: { card: 'https://huggingface.co/finnianx/michel-nano-v2' } },
{ name: 'Dumb-1.2-RC3', org: 'small56ai', addedDate: '2026-06-16', params: 34611072, paramsDisplay: '34.6M', arc: 33.25, hellaswag: 26.91, piqa: 55.06, arcChall: 23.12, arithmark3: 28.00, arithmark2: 26.08, links: { card: 'https://huggingface.co/56m/Dumb-1.2-Exp-0616' } },
{ name: 'Dumb-1.2-Preview-0618', org: 'small56ai', addedDate: '2026-06-19', params: 34611072, paramsDisplay: '34.6M', arc: 26.30, hellaswag: 26.45, piqa: 54.57, arcChall: 21.84, arithmark3: 30.40, arithmark2: 25.76, links: { card: 'https://huggingface.co/56m/Dumb-1.2-Preview-0618' } },
{ name: 'Byrne-86M', org: 'quazim0t0', addedDate: '2026-06-19', params: 85327747, paramsDisplay: '85.3M', arc: 29.22, hellaswag: 30.36, piqa: 56.47, arcChall: 23.98, arithmark2: 30.96, links: { card: 'https://huggingface.co/Quazim0t0/Byrne-86M' } },
{ name: 'Byrne-86M-Base', org: 'quazim0t0', addedDate: '2026-06-19', params: 85327747, paramsDisplay: '85.3M', arc: 31.48, hellaswag: 30.58, piqa: 58.81, arcChall: 24.06, arithmark2: 27.32, links: { card: 'https://huggingface.co/Quazim0t0/Byrne-86M-Base' } },
{ name: 'Glint-1.3', org: 'compactai', addedDate: '2026-06-20', params: 982656, paramsDisplay: '982K', arc: 30.47, hellaswag: 26.44, piqa: 52.67, arcChall: 22.95, arithmark3: 28.10, arithmark2: 25.72, links: { card: 'https://huggingface.co/Glint-Research/Glint-1.3' } },
{ name: 'GPT-S2-5M', org: 'axiomiclabs', addedDate: '2026-06-20', params: 5384258, paramsDisplay: '5.4M', arc: 33.92, hellaswag: 27.87, piqa: 57.56, arcChall: 22.87, arithmark3: 27.90, arithmark2: 28.04, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-S2-5M' } },
{ name: 'Escarda-86M-Identity', org: 'quazim0t0', addedDate: '2026-06-22', params: 85656068, paramsDisplay: '85.7M', arc: 33.33, hellaswag: 29.02, piqa: 56.47, arcChall: 25.51, arithmark2: 34.88, links: { card: 'https://huggingface.co/Quazim0t0/Escarda-86M-Identity' } },
{ name: 'kirk-ballerina', org: 'rtc2022', addedDate: '2026-06-22', params: 94576128, paramsDisplay: '94.6M', arc: 31.69, hellaswag: 26.64, piqa: 52.88, arcChall: 20.65, arithmark3: 31.00, arithmark2: 24.88, links: { card: 'https://huggingface.co/rtc2022/kirk-ballerina' } },
{ name: 'Atom 3.4M', org: 'ucr', addedDate: '2026-06-22', params: 3412800, paramsDisplay: '3.4M', arc: 33.33, hellaswag: 27.69, piqa: 55.17, arcChall: 22.10, arithmark3: 31.60, arithmark2: 27.28, links: { card: 'https://huggingface.co/UniversalComputingResearch/Atom3.4m'} },
{ name: 'Isabel-50M', org: 'ideoalabs', addedDate: '2026-06-22', params: 54100000, paramsDisplay: '54M', arc: 43.64, hellaswag: 27.02, piqa: 57.18, arcChall: 23.55, arithmark3: 35.10, arithmark2: 42.32, links: { card: 'https://huggingface.co/MaliosDark/Isabel-50M' } },
{ name: 'tinyLM-8M-exp', org: 'user01110', addedDate: '2026-06-24', params: 8131840, paramsDisplay: '8.1M', arc: 35.65, hellaswag: 27.65, piqa: 56.51, arcChall: 23.61, arithmark3: 27.50, arithmark2: 25.46, links: { card: 'https://huggingface.co/User01110/tinyLM-8M-exp' } },
{ name: 'CMA-1M-Mini', org: 'user01110', addedDate: '2026-07-21', params: 958692, paramsDisplay: '959K', arc: 29.29, hellaswag: 29.35, piqa: 54.62, arcChall: 21.76, arithmark3: 31.20, arithmark2: 27.44, links: { card: 'https://huggingface.co/User01110/CMA-1M-Mini' } },
{ name: 'Archaea-74M-V1.1', org: 'godelev', addedDate: '2026-06-26', params: 74016256, paramsDisplay: '74M', arc: 41.54, hellaswag: 27.05, piqa: 59.19, arcChall: 24.40, arithmark3: 33.60, arithmark2: 37.64, links: { card: 'https://huggingface.co/GODELEV/Archaea-74M-V1.1' } },
{ name: 'MiniBananaMind-v2-9M', org: 'bananamind', addedDate: '2026-06-27', params: 8884992, paramsDisplay: '8.9M', arc: 33.92, hellaswag: 26.99, piqa: 54.79, arcChall: 20.65, arithmark3: 30.20, arithmark2: 25.20, links: { card: 'https://huggingface.co/BananaMind/MiniBananaMind-v2-9M' } },
{ name: 'Gros-Michel-90m-Base', org: 'finnianx', addedDate: '2026-06-29', params: 91071744, paramsDisplay: '91.1M', arc: 40.15, hellaswag: 29.95, piqa: 58.05, arcChall: 23.81, arithmark3: 35.50, arithmark2: 28.96, links: { card: 'https://huggingface.co/finnianx/Gros-Michel-90m-Base' } },
{ name: 'MiniBananaMind-v3-9M', org: 'bananamind', addedDate: '2026-06-30', params: 8884992, paramsDisplay: '8.9M', arc: 34.81, hellaswag: 27.12, piqa: 54.68, arcChall: 22.70, arithmark3: 35.70, arithmark2: 26.32, links: { card: 'https://huggingface.co/BananaMind/MiniBananaMind-v3-9M' } },
{ name: 'BananaMind-1.5-Base', org: 'bananamind', addedDate: '2026-06-30', params: 75054720, paramsDisplay: '75M', hellaswag: 30.91, arc: 42.38, arcChall: 23.98, piqa: 60.55, arithmark3: 34.10, arithmark2: 26.68, links: { card: 'https://huggingface.co/BananaMind/BananaMind-1.5-Base' } },
{ name: 'MiniBananaMind-v4-9M', org: 'bananamind', addedDate: '2026-07-01', params: 8884992, paramsDisplay: '8.9M', arc: 35.02, hellaswag: 26.87, piqa: 55.60, arcChall: 23.12, arithmark3: 36.30, arithmark2: 26.04, links: { card: 'https://huggingface.co/BananaMind/MiniBananaMind-v4-9M' } },
{ name: 'MicroBananaMind-v1', org: 'bananamind', addedDate: '2026-07-02', params: 902272, paramsDisplay: '902K', arc: 31.40, hellaswag: 26.53, piqa: 53.86, arcChall: 20.99, arithmark3: 30.00, arithmark2: 24.16, links: { card: 'https://huggingface.co/BananaMind/MicroBananaMind-v1' } },
{ name: 'Atom 2.7M', org: 'ucr', addedDate: '2026-07-07', params: 2738880, paramsDisplay: '2.7M', arc: 31.19, hellaswag: 27.10, piqa: 52.94, arcChall: 21.50, arithmark3: 25.80, arithmark2: 69.40, links: { card: 'https://huggingface.co/UniversalComputingResearch/Atom2.7m'} },
{ name: 'Gros-Michel-90m-Base-v2', org: 'finnianx', addedDate: '2026-07-07', params: 94911744, paramsDisplay: '94.9M', arc: 43.31, hellaswag: 31.23, piqa: 61.37, arcChall: 23.98, arithmark3: 35.90, arithmark2: 28.84, links: { card: 'https://huggingface.co/finnianx/Gros-Michel-90m-Base-v2' } },
{ name: 'MetaNova-1-60M', org: 'whirlwindai', addedDate: '2026-07-07', params: 46294016, paramsDisplay: '46.3M', arc: 31.65, hellaswag: 26.29, piqa: 55.17, arcChall: 24.15, arithmark2: 38.96, links: { card: 'https://huggingface.co/WhirlwindAI/MetaNova-1-60M' } },
{ name: 'MetaNova-0.1-70M', org: 'whirlwindai', addedDate: '2026-07-07', params: 54158336, paramsDisplay: '54.2M', arc: 33.63, hellaswag: 25.88, piqa: 58.76, arcChall: 22.53, arithmark2: 27.48, links: { card: 'https://huggingface.co/WhirlwindAI/MetaNova-0.1-70M' } },
{ name: 'TinyMoE-100m-2x8-retrained', org: 'flamef0x', addedDate: '2026-07-07', params: 99817344, paramsDisplay: '99.8M', arc: 33.88, hellaswag: 25.87, piqa: 55.60, arcChall: 22.01, arithmark3: 32.20, links: { card: 'https://huggingface.co/FlameF0X/TinyMoE-100m-2x8-retrained' } },
{ name: 'BananaMind-2-Mini', org: 'bananamind', addedDate: '2026-07-09', params: 25178752, paramsDisplay: '25.2M', arc: 39.06, hellaswag: 29.80, piqa: 59.41, arcChall: 25.34, arithmark3: 35.40, arithmark2: 29.28, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Mini' } },
{ name: 'cma-8M', org: 'user01110', addedDate: '2026-07-14', params: 7849161, paramsDisplay: '7.8M', arc: 35.35, hellaswag: 28.19, piqa: 58.22, arcChall: 23.29, arithmark3: 30.80, arithmark2: 29.12, links: { card: 'https://huggingface.co/User01110/CMA-8M' } },
{ name: 'MathBananaMind-1.1', org: 'bananamind', addedDate: '2026-07-15', params: 2901984, paramsDisplay: '2.9M', arc: 26.85, hellaswag: 26.16, piqa: 50.49, arcChall: 27.13, arithmark3: 22.30, arithmark2: 90.28, links: { card: 'https://huggingface.co/BananaMind/MathBananaMind-1.1' } },
{ name: 'CyberSLM-33M-Base', org: 'sabari2005', addedDate: '2026-07-16', params: 33531264, paramsDisplay: '33.5M', arc: 32.79, hellaswag: 26.84, piqa: 54.68, arcChall: 22.70, arithmark3: 31.00, arithmark2: 26.96, links: { card: 'https://huggingface.co/sabari2005/cyberslm-base' } },
{ name: 'CyberSLM-33M-Instruct', org: 'sabari2005', addedDate: '2026-07-16', params: 33531264, paramsDisplay: '33.5M', arc: 32.37, hellaswag: 26.19, piqa: 53.81, arcChall: 22.35, arithmark3: 30.20, arithmark2: 26.88, links: { card: 'https://huggingface.co/sabari2005/cyberslm-instruct' } },
{ name: 'BananaMind-2-Nano', org: 'bananamind', addedDate: '2026-07-20', params: 9968128, paramsDisplay: '9.97M', arc: 36.20, hellaswag: 27.50, piqa: 55.98, arcChall: 23.38, arithmark3: 33.70, arithmark2: 27.68, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Nano' } },
{ name: 'BananaMind-2-MoE', org: 'bananamind', addedDate: '2026-07-20', params: 25086592, paramsDisplay: '25.1M', arc: 34.64, hellaswag: 27.45, piqa: 56.37, arcChall: 21.16, arithmark3: 33.80, arithmark2: 28.44, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-MoE' } },
{ name: 'BananaMind-2-Medium', org: 'bananamind', addedDate: '2026-07-22', params: 49559552, paramsDisplay: '49.6M', arc: 43.81, hellaswag: 32.43, piqa: 61.86, arcChall: 25.34, arithmark3: 36.20, arithmark2: 28.20, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Medium' } },
{ name: 'BananaMind-2-Medium-Chat', org: 'bananamind', addedDate: '2026-07-22', params: 49559552, paramsDisplay: '49.6M', arc: 43.43, hellaswag: 31.71, piqa: 60.66, arcChall: 24.40, arithmark3: 35.60, arithmark2: 29.92, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Medium-Chat' } },
{ name: 'Nexus-Erebus-135M', org: 'ideoalabs', addedDate: '2026-07-22', params: 134000000, paramsDisplay: '135M', arc: 48.61, hellaswag: 30.61, piqa: 64.53, arcChall: 25.43, arithmark3: 31.00, arithmark2: 66.68, links: { card: 'https://huggingface.co/MaliosDark/Nexus-Erebus-135M' } },
{ name: 'Museko-125M', org: 'tobiaslogic', addedDate: '2026-07-26', params: 122702592, paramsDisplay: '123M', arc: 48.06, hellaswag: 33.26, piqa: 63.22, arcChall: 25.68, arithmark3: 34.60, arithmark2: 74.04, links: { card: 'https://huggingface.co/TobiasLogic/Museko-125M' } },
{ name: 'Quark-50m-v2', org: 'thingai', addedDate: '2026-07-26', params: 56666496, paramsDisplay: '56.7M', arc: 26.18, hellaswag: 27.39, piqa: 49.08, arcChall: 25.34, arithmark3: 25.50, arithmark2: 25.20, links: { card: 'https://huggingface.co/ThingAI/Quark-50m-v2' } },
{ name: 'Rose-Mini', org: 'godelev', addedDate: '2026-07-26', params: 49443074, paramsDisplay: '49.4M', arc: 36.57, hellaswag: 28.93, piqa: 58.60, arcChall: 24.23, arithmark3: 36.50, arithmark2: 26.64, links: { card: 'https://huggingface.co/GODELEV/Rose-Mini' } },
//{ name: 'palmer-006', org: 'appvoid', params: 90000000, paramsDisplay: '90M', arc: 44.82, hellaswag: 38.41, piqa: 63.60, arcChall: 29.27, winogrande: 50.36, arithmark3: 52.70, links: { card: 'https://huggingface.co/appvoid/palmer-006' } },
{ name: 'Er-Large-30M', org: 'fromzero', addedDate: '2026-07-31', params: 31000000, paramsDisplay: '31M', arc: 37.56, hellaswag: 30.52, piqa: 60.12, arcChall: 22.17, arithmark3: 35.00, links: { card: 'https://huggingface.co/fromziro/Er-Large-30M' } },
{ name: 'ObsidianSmall-Base', org: 'dreamw', addedDate: '2026-08-02', params: 8700000, paramsDisplay: '8.7M', arc: 33.291, hellaswag: 27.415, piqa: 55.114, arcChall: 21.843, arithmark3: 28.800, arithmark2: 26.160, links: { card: 'https://huggingface.co/Dream-W/ObsidianSmall-Base' } },
//{ name: 'BananaMind-2-Pro-Preview', org: 'bananamind', params: 138971520, paramsDisplay: '138M', arc: 51.01, hellaswag: 39.83, piqa: 66.76, arcChall: 27.13, arithmark3: 38.90, arithmark2: 28.60, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Pro-Preview' } },
{ name: 'Supra2-100M-Base', org: 'supralabs', addedDate: '2026-08-04', params: 100684032, paramsDisplay: '101M', arc: 47.81, hellaswag: 35.98, piqa: 65.40, arcChall: 24.83, arithmark3: 36.90, links: { card: 'https://huggingface.co/SupraLabs/Supra2-100M-Base' } },
{ name: 'Supra2-100M-Instruct', org: 'supralabs', addedDate: '2026-08-04', params: 100684032, paramsDisplay: '101M', arc: 44.44, hellaswag: 35.89, piqa: 64.36, arcChall: 24.74, arithmark3: 38.20, links: { card: 'https://huggingface.co/SupraLabs/Supra2-100M-Instruct' } },
{ name: 'GPT-X2.5-135M', org: 'axiomiclabs', addedDate: '2026-08-04', params: 135032256, paramsDisplay: '135M', arc: 51.81, hellaswag: 40.57, piqa: 69.42, arcChall: 29.18, arithmark3: 38.40, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X2.5-135M' } },
//{ name: 'Boris-1.3-75M', org: 'opencerebral', params: 77431680, paramsDisplay: '77.4M', arc: 40.11, hellaswag: 27.57, piqa: 59.36, arcChall: 23.89, arithmark3: 33.40, links: { card: 'https://huggingface.co/opencerebral/Boris-1.3-75M' } },
//{ name: 'Boris-1.3-125M', org: 'opencerebral', params: 124475904, paramsDisplay: '124.5M', arc: 41.46, hellaswag: 29.59, piqa: 61.70, arcChall: 24.40, arithmark3: 35.20, links: { card: 'https://huggingface.co/opencerebral/Boris-1.3-125M' } },
//{ name: 'littlerock-1M', org: 'opencerebral', params: 1002720, paramsDisplay: '1.0M', arc: 30.98, hellaswag: 26.43, piqa: 52.23, arcChall: 21.76, arithmark3: 25.20, links: { card: 'https://huggingface.co/opencerebral/littlerock-1M' } },
{ name: 'ZeroS-Qana-5M', org: 'fromzero', addedDate: '2026-08-06', params: 4943712, paramsDisplay: '4.94M', arc: 34.97, hellaswag: 27.60, piqa: 57.18, arcChall: 23.21, arithmark3: 29.90, links: { card: 'https://huggingface.co/fromziro/ZeroS-Qana-5M' } },
{ name: 'Bananamind', org: 'bananamind' , addedDate: '2026-08-07', params: 25100384, paramsDisplay: '25M', arc: 33.77, hellaswag: 25.98, piqa: 52.88, arcChall: 22.35, arithmark3: 29.80, arithmark2: 25.88, links: { card: 'https://huggingface.co/BananaMind/MiniBananaMind-V1' } },
{ name: 'min-spark', org: 'minimalabs', addedDate: '2026-08-07', params: 5758572, paramsDisplay: '5.8Mx4*', arc: 37.08, hellaswag: 27.92, piqa: 54.35, arcChall: 23.21, arithmark3: 33.30, links: { card: 'https://huggingface.co/MinimaLabs/min-spark' } },
{ name: 'SupraElegans-500k', org: 'supralabs', addedDate: '2026-08-10', params: 500000, paramsDisplay: '500k', arc: 21.00, hellaswag: 24.28, piqa: 49.73, arcChall: 22.00, arithmark3: 22.00, links: { card: 'https://huggingface.co/SupraLabs/SupraElegans-500k' } },
{ name: 'BananaMind-2-Micro', org: 'bananamind', addedDate: '2026-08-11', params: 2933193, paramsDisplay: '2.9M', arc: 33.12, hellaswag: 28.27, piqa: 53.21, arcChall: 21.93, arithmark3: 34.00, arithmark2: 24.92, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Micro' } },
{ name: 'Rambley-150M-RealBase', org: 'allura', addedDate: '2026-08-11', params: 149_973_504, paramsDisplay: '150M', arc: 44.74, hellaswag: 32.29, arcChall: 24.40, piqa: 62.57, arithmark3: 37.20, links: { card: "https://huggingface.co/allura-org/Rambley-150M-RealBase" } },
{ name: 'Rambley-150M-Base', org: 'allura', addedDate: '2026-08-11', params: 149_973_504, paramsDisplay: '150M', arc: 48.23, hellaswag: 31.49, arcChall: 24.06, piqa: 61.26, arithmark3: 39.9, links: { card: "https://huggingface.co/allura-org/Rambley-150M-Base" } },
{ name: 'Glint-2', org: 'compactai', addedDate: '2026-08-11', params: 1710049, paramsDisplay: '1.7Mx8*', arc: 36.03, hellaswag: 25.87, piqa: 51.03, arcChall: 23.55, arithmark3: 27.00, links: { card: 'https://huggingface.co/Glint-Research/Glint-2' } },
{ name: 'void.0', org: 'appvoid', addedDate: '2026-08-11', params: 132438656, paramsDisplay: '132.4M', arc: 45.03, hellaswag: 31.57, piqa: 61.15, arcChall: 25.17, arithmark3: 35.20, links: { card: 'https://huggingface.co/appvoid/void.0' } },
{ name: 'Rose-Medium', org: 'godelev', addedDate: '2026-08-11', params: 97825347, paramsDisplay: '97.8M', arc: 44.19, hellaswag: 35.29, piqa: 62.94, arcChall: 26.19, arithmark3: 38.30, links: { card: 'https://huggingface.co/GODELEV/Rose-Medium' } },
{ name: 'Veyra2-Mango-30M-Base', org: 'veyraai', addedDate: '2026-08-12', params: 30683520, paramsDisplay: '30.7M', arc: 37.88, hellaswag: 28.76, piqa: 59.52, arcChall: 23.29, arithmark3: 36.90, arithmark2: 27.96, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base' } },
{ name: 'Veyra2-Mango-15M-Base', org: 'veyraai', addedDate: '2026-08-12', params: 15735168, paramsDisplay: '15.7M', arc: 37.16, hellaswag: 27.73, piqa: 58.00, arcChall: 22.87, arithmark3: 34.90, arithmark2: 28.08, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Mango-15M-Base' } },
{ name: 'BananaMind-2-SLMoE', org: 'bananamind', addedDate: '2026-08-12', params: 25450000, paramsDisplay: '25.5M', arc: 33.92, hellaswag: 27.16, piqa: 55.44, arcChall: 23.12, arithmark3: 33.60 , arithmark2: 26.32, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-SLMoE' } },
{ name: 'Hummingbird-V1', org: 'juinron', addedDate: '2026-08-15', params: 9592720, paramsDisplay: '9.6M', arc: 33.12, hellaswag: 27.56, piqa: 55.55, arcChall: 22.27, arithmark3: 36.10, links: { card: 'https://huggingface.co/juinron/Hummingbird-V1' } },
//{ name: 'Lumen-118M-Base', org: 'kefir090', params: 117477466, paramsDisplay: '117.5M', arc: 49.20, hellaswag: 33.74, piqa: 66.65, arcChall: 25.77, arithmark3: 39.50, links: { card: 'https://huggingface.co/kefir090/Lumen-118M-Base' } },
{ name: 'BananaMind-2-Pro', org: 'bananamind', addedDate: '2026-08-15', params: 138971520, paramsDisplay: '139M', arc: 53.58, hellaswag: 42.78, piqa: 67.52, arcChall: 27.82, arithmark3: 38.20, arithmark2: 32.08, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Pro' } },
{ name: 'Supra2-Nano', org: 'supralabs', addedDate: '2026-08-15', params: 799776, paramsDisplay: '800k', arc: 30.81, hellaswag: 26.81, piqa: 53.43, arcChall: 20.65, arithmark3: 29.00, links: { card: 'https://huggingface.co/SupraLabs/Supra2-Nano' } },
{ name: 'Rose-Pro', org: 'godelev', addedDate: '2026-08-17', params: 151277410, paramsDisplay: '151M', arc: 48.65, hellaswag: 38.24, piqa: 65.18, arcChall: 26.96, arithmark3: 38.20, arithmark2: 33.00, links: { card: 'https://huggingface.co/GODELEV/Rose-Pro' } },
{ name: 'cagliostro-v1', org: 'benchlabs', addedDate: '2026-08-19', params: 157411200, paramsDisplay: '157.4M', arc: 48.91, hellaswag: 36.90, piqa: 65.61, arcChall: 26.11, arithmark3: 33.70, arithmark2: 26.72, links: { card: 'https://huggingface.co/bench-labs/cagliostro-v1' } },
{ name: 'cagliostro-v2', org: 'benchlabs', addedDate: '2026-08-31', params: 150038400, paramsDisplay: '150M', arc: 47.43, hellaswag: 36.76, piqa: 65.67, arcChall: 28.58, arithmark3: 35.20, links: { card: 'https://huggingface.co/bench-labs/cagliostro-v2' } },
{ name: 'BananaMind-2.1-Unified', org: 'bananamind', addedDate: '2026-08-20', params: 34999041, paramsDisplay: '35M', arc: 38.51, hellaswag: 29.94, piqa: 61.75, arcChall: 24.49, arithmark3: 36.70, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2.1-Unified' } },
{ name: 'Byrne-100M-Ultra-MC', org: 'quazim0t0', addedDate: '2026-08-20', params: 113908642, paramsDisplay: '114Mx3*', arc: 38.30, hellaswag: 29.31, piqa: 58.22, arcChall: 22.35, arithmark3: 37.00, links: { card: 'https://huggingface.co/Quazim0t0/Byrne-100M-Ultra-MC' } },
{ name: 'Ember', org: 'surjolabs', addedDate: '2026-08-21', params: 2870432, paramsDisplay: '2.9Mx2*', arc: 33.25, hellaswag: 27.35, piqa: 54.41, arcChall: 21.08, arithmark3: 32.90, links: { card: 'https://huggingface.co/SurjoLabs/Ember' } },
{ name: 'Supra2-Medium-Base', org: 'supralabs', addedDate: '2026-08-21', params: 25371008, paramsDisplay: '25M', arc: 41.67, hellaswag: 29.29, piqa: 59.68, arcChall: 24.15, arithmark3: 34.80, links: { card: 'https://huggingface.co/SupraLabs/Supra2-Medium-Base' } },
{ name: 'Supra2-Medium-Instruct', org: 'supralabs', addedDate: '2026-08-21', params: 25383296, paramsDisplay: '25M', arc: 40.28, hellaswag: 29.33, piqa: 59.25, arcChall: 24.91, arithmark3: 36.50, links: { card: 'https://huggingface.co/SupraLabs/Supra2-Medium-Instruct' } },
{ name: 'Spark', org: 'surjolabs', addedDate: '2026-08-22', params: 7083264, paramsDisplay: '7.1Mx2*', arc: 35.02, hellaswag: 28.17, piqa: 55.55, arcChall: 20.99, arithmark3: 36.10, links: { card: 'https://huggingface.co/SurjoLabs/Spark' } },
{ name: 'min-spark-1.1', org: 'minimalabs', addedDate: '2026-08-22', params: 5758572, paramsDisplay: '5.8Mx4*', arc: 36.45, hellaswag: 28.24, piqa: 57.40, arcChall: 23.04, arithmark3: 35.40, links: { card: 'https://huggingface.co/MinimaLabs/min-spark-1.1' } },
{ name: 'min-spark-1.5', org: 'minimalabs', addedDate: '2026-10-09', params: 9360596, paramsDisplay: '9.4Mx4*', arc: 38.64, hellaswag: 28.27, piqa: 57.34, arcChall: 23.55, arithmark3: 34.90, links: { card: 'https://huggingface.co/MinimaLabs/min-spark-1.5' } },
{ name: 'sorbet-25m', org: 'codesoft', addedDate: '2026-08-23', params: 25185920, paramsDisplay: '25M', arc: 26.12, hellaswag: 26.12, piqa: 53.43, arcChall: 22.95, arithmark3: 32.90, links: { card: 'https://huggingface.co/CodeSoft/sorbet-25m' } },
{ name: 'Flare', org: 'surjolabs', addedDate: '2026-08-23', params: 130393152, paramsDisplay: '130.4Mx2*', arc: 51.14, hellaswag: 39.43, piqa: 65.94, arcChall: 26.88, arithmark3: 39.20, links: { card: 'https://huggingface.co/SurjoLabs/Flare' } },
{ name: 'cRia-LM-75M', org: 'sz14', addedDate: '2026-08-24', params: 75719395, paramsDisplay: '75.72Mx2*', arc: 46.97, hellaswag: 35.49, piqa: 63.93, arcChall: 25.77, arithmark3: 35.30, links: { card: 'https://huggingface.co/sz14/cRia-LM-75M' } },
{ name: 'Ember-2', org: 'surjolabs', addedDate: '2026-08-24', params: 2963360, paramsDisplay: '2.96Mx2*', arc: 33.42, hellaswag: 27.28, piqa: 55.11, arcChall: 22.01, arithmark3: 35.90, links: { card: 'https://huggingface.co/SurjoLabs/Ember-2' } },
{ name: 'Spark-2A', org: 'surjolabs', addedDate: '2026-08-25', params: 9148608, paramsDisplay: '9.15Mx2*', arc: 34.26, hellaswag: 28.13, piqa: 57.24, arcChall: 22.18, arithmark3: 37.00, links: { card: 'https://huggingface.co/SurjoLabs/Spark-2A' } },
{ name: 'ZeroS-v0.1-150M', org: 'fromzero', addedDate: '2026-08-25', params: 151638000, paramsDisplay: '152M', arc: 48.70, hellaswag: 37.02, piqa: 66.76, arcChall: 28.84, arithmark3: 35.80, links: { card: 'https://huggingface.co/fromziro/ZeroS-v0.1-150M' } },
{ name: 'sorbet-v2-25m', org: 'codesoft', addedDate: '2026-08-26', params: 25185920, paramsDisplay: '25M', arc: 26.63, hellaswag: 30.30, piqa: 54.52, arcChall: 22.44, arithmark3: 33.00, links: { card: 'https://huggingface.co/CodeSoft/sorbet-v2-25m' } },
{ name: 'VegaLM1-42M-Base', org: 'cnwplayer', addedDate: '2026-08-26', params: 42054144, paramsDisplay: '42M', arc: 35.23, hellaswag: 26.86, piqa: 57.62, arcChall: 21.25, arithmark3: 33.90, links: { card: 'https://huggingface.co/CNWPlayer/VegaLM1-42M-Base'} },
{ name: 'Speck1-140M-Instruct', org: 'specklabs', addedDate: '2026-08-26', params: 140654208, paramsDisplay: '141M', arc: 46.93, hellaswag: 35.64, piqa: 64.15, arcChall: 26.02, arithmark2: 32.44, arithmark3: 33.7, links: { card: 'https://huggingface.co/specklabs/Speck1-140M-Instruct'} },
{ name: 'Bryne-15M-Looped', org: 'quazim0t0', addedDate: '2026-08-26', params:18900000, paramsDisplay: '18.9Mx3*', arc: 32.80 , hellaswag: 27.20, piqa: 55.40, arcChall: 23.20, arithmark3: 34.7, links: { card: 'https://huggingface.co/Quazim0t0/Byrne-15M-Looped'} },
{ name: 'ZeroS-Pico-v1.0', org: 'fromzero', addedDate: '2026-08-26', params:67700, paramsDisplay: '67.7K', arc: 27.65 , hellaswag: 25.94, piqa: 49.62, arcChall: 22.95, arithmark3: 31.50, links: { card: 'https://huggingface.co/fromziro/ZeroS-Pico-v1.0'} },
{ name: 'Blaze', org: 'surjolabs', addedDate: '2026-08-26', params: 48251136, paramsDisplay: '48.3Mx2*', arc: 41.84, hellaswag: 31.84, piqa: 62.51, arcChall: 24.91, arithmark3: 37.80, links: { card: 'https://huggingface.co/SurjoLabs/Blaze' } },
{ name: 'Veyra2-Blueberry-10M-Base', org: 'veyraai', addedDate: '2026-08-28', params: 9976832, paramsDisplay: '9.9M', arc: 35.44, hellaswag: 28.04, piqa: 55.44, arcChall: 21.59, arithmark3: 34.60, arithmark2: 26.08, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Blueberry-10M-Base' } },
{ name: 'Veyra2-Blueberry-5M-Base', org: 'veyraai', addedDate: '2026-08-28', params: 4984192, paramsDisplay: '4.9M', arc: 33.46, hellaswag: 27.62, piqa: 54.62, arcChall: 21.67, arithmark3: 36.40, arithmark2: 26.40, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Blueberry-5M-Base' } },
{ name: 'Haidass1.5-143M', org: 'dalab', addedDate: '2026-08-29', params: 143067456, paramsDisplay: '143M', arc: 56.36, hellaswag: 40.58, piqa: 67.79, arcChall: 27.39, arithmark3: 39.60, links: { card: 'https://huggingface.co/DALabCommunity/Haidass1.5-143M' } },
{ name: 'BananaMind-2.1-Pico-Preview', org: 'bananamind', addedDate: '2026-09-01', params: 1480516, paramsDisplay: '1.4M', arc: 29.50, hellaswag: 26.89, piqa: 54.30, arcChall: 22.53, arithmark3: 33.00, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2.1-Pico-Preview' } },
{ name: 'Myosotis-1-base', org: 'fwkv', addedDate: '2026-09-03', params: 101860800, paramsDisplay: '102M', arc: 34.34, hellaswag: 26.21, piqa: 55.33, arcChall: 21.59, arithmark3: 35.00, links: { card: 'https://huggingface.co/FWKV/Myosotis-1-base' } },
{ name: 'Speck2-140M', org: 'specklabs', addedDate: '2026-09-03', params: 140652288, paramsDisplay: '141M', arc: 45.66, hellaswag: 39.49, piqa: 65.23, arcChall: 24.57, arithmark2: 31.60, arithmark3: 36.30, links: { card: 'https://huggingface.co/specklabs/Speck2-140M'} },
{ name: 'Speck2-140M-Instruct', org: 'specklabs', addedDate: '2026-09-03', params: 140652288, paramsDisplay: '141M', arc: 47.73, hellaswag: 38.94, piqa: 65.83, arcChall: 25.34, arithmark2: 37.92, arithmark3: 39.10, links: { card: 'https://huggingface.co/specklabs/Speck2-140M-Instruct'} },
{ name: 'picollama', org: 'appvoid', addedDate: '2026-09-08', params: 35000000, paramsDisplay: '35M', arc: 37.21, hellaswag: 28.38, piqa: 60.23, arcChall: 22.87, arithmark3: 33.10, links: { card: 'https://huggingface.co/appvoid/picollama' } },
{ name: 'Pebble-10M', org: 'basically-ai', addedDate: '2026-09-08', params: 10281744, paramsDisplay: '10.28M', arc: 34.18, hellaswag: 28.59, piqa: 56.75, arcChall: 22.95, arithmark3: 33.80, links: { card: 'https://huggingface.co/basically-ai/Pebble-10M' } },
{ name: 'Pebble-10M-Chat', org: 'basically-ai', addedDate: '2026-09-08', params: 10281744, paramsDisplay: '10.28M', arc: 27.19, hellaswag: 23.91, piqa: 48.48, arcChall: 25.00, arithmark3: 26.60, links: { card: 'https://huggingface.co/basically-ai/Pebble-10M-Chat' } },
{ name: 'Pebble-25M-Chat', org: 'basically-ai', addedDate: '2026-09-08', params: 24486710, paramsDisplay: '24.49M', arc: 28.24, hellaswag: 24.65, piqa: 51.63, arcChall: 24.91, arithmark3: 30.70, links: { card: 'https://huggingface.co/basically-ai/Pebble-25M-Chat' } },
{ name: 'Pebble-25M', org: 'basically-ai', addedDate: '2026-09-08', params: 24486710, paramsDisplay: '24.49M', arc: 36.15, hellaswag: 29.54, piqa: 58.87, arcChall: 21.67, arithmark3: 33.00, links: { card: 'https://huggingface.co/basically-ai/Pebble-25M' } },
{ name: 'SupraNeo-4M', org: 'supralabs', addedDate: '2026-09-08', params: 4070240, paramsDisplay: '4.07M', arc: 34.47, hellaswag: 27.84, piqa: 55.39, arcChall: 23.38, arithmark3: 31.20, links: { card: 'https://huggingface.co/SupraLabs/SupraNeo-4M' } },
{ name: 'SupraGDN-5M', org: 'supralabs', addedDate: '2026-09-08', params: 5041656, paramsDisplay: '5.04M', arc: 33.54, hellaswag: 26.69, piqa: 53.21, arcChall: 22.87, arithmark3: 26.40, links: { card: 'https://huggingface.co/SupraLabs/SupraGDN-5M' } },
{ name: 'Linear-50M-exp', org: 'user01110', addedDate: '2026-09-08', params: 49552864, paramsDisplay: '49.55M', arc: 44.02, hellaswag: 31.94, piqa: 61.81, arcChall: 24.06, arithmark3: 34.90, links: { card: 'https://huggingface.co/User01110/Linear-50M-exp' } },
{ name: 'ZeroS-Linear-50M', org: 'fromzero', addedDate: '2026-09-11', params: 49552864, paramsDisplay: '49.55M', arc: 43.86, hellaswag: 32.30, piqa: 63.06, arcChall: 23.98, arithmark3: 35.70, links: { card: 'https://huggingface.co/fromziro/ZeroS-Linear-50M' } },
{ name: 'ZeroS-Pico-v1.1', org: 'fromzero', addedDate: '2026-09-10', params: 59769, paramsDisplay: '59.8K', arc: 26.30, hellaswag: 26.19, piqa: 51.14, arcChall: 24.57, arithmark3: 30.50, links: { card: 'https://huggingface.co/fromziro/ZeroS-Pico-v1.1' } },
//{ name: 'Kiyo-135M', org: 'dedeprogames', params: 134515008, paramsDisplay: '134.52M', arc: 55.93, hellaswag: 38.55, piqa: 65.51, arcChall: 29.01, arithmark3: 36.60, links: { card: 'https://huggingface.co/DedeProGames/Kiyo-135M' } },
{ name: 'Kiyo-65M', org: 'dedeprogames', addedDate: '2026-09-10', params: 64994816, paramsDisplay: '64.99M', arc: 46.51, hellaswag: 31.98, piqa: 62.30, arcChall: 23.29, arithmark3: 37.40, links: { card: 'https://huggingface.co/DedeProGames/Kiyo-65M' } },
{ name: 'lille-130m-base', org: 'nikity', addedDate: '2026-09-10', params: 127171200, paramsDisplay: '127.17M', arc: 38.68, hellaswag: 29.76, piqa: 55.77, arcChall: 23.55, arithmark3: 34.50, links: { card: 'https://huggingface.co/Nikity/lille-130m-base' } },
{ name: 'Rose-1.5-Medium', org: 'godelev', addedDate: '2026-09-10', params: 98277437, paramsDisplay: '98.2M', arc: 47.22, hellaswag: 38.09, piqa: 64.80, arcChall: 27.13, arithmark3: 40.70, links: { card: 'https://huggingface.co/GODELEV/Rose-1.5-Medium' } },
{ name: 'GTM-v1-base', org: 'opengcm', addedDate: '2026-09-12', params: 101750400, paramsDisplay: '101.75M', arc: 41.41, hellaswag: 29.00, piqa: 60.12, arcChall: 23.04, arithmark3: 35.70, links: { card: 'https://huggingface.co/OPENGCM/GTM-v1-base' } },
{ name: 'GTM-v2-base', org: 'opengcm', addedDate: '2026-09-13', params: 119494848, paramsDisplay: '119.49M', arc: 42.38, hellaswag: 30.09, piqa: 60.72, arcChall: 24.83, arithmark3: 36.80, links: { card: 'https://huggingface.co/OPENGCM/GTM-v2-base' } },
{ name: 'GTM-v2-chat', org: 'opengcm', addedDate: '2026-09-13', params: 119494848, paramsDisplay: '119.49M', arc: 40.49, hellaswag: 30.70, piqa: 63.98, arcChall: 25.17, arithmark3: 36.60, links: { card: 'https://huggingface.co/OPENGCM/GTM-v2-chat' } },
{ name: 'Ivme-Conversate-v3-Base', org: 'ivmelabs', addedDate: '2026-09-14', params: 24787520, paramsDisplay: '24.79M', arc: 39.02, hellaswag: 28.82, piqa: 57.78, arcChall: 24.06, arithmark3: 34.70, links: { card: 'https://huggingface.co/IvmeLabs/Ivme-Conversate-v3-Base' } },
{ name: 'Aurora1.0-150M', org: 'aurora', addedDate: '2026-09-14', params: 149270272, paramsDisplay: '149.2M', arc: 44.91, hellaswag: 32.20, piqa: 62.24, arcChall: 25.00, arithmark3: 33.90, links: { card: 'https://huggingface.co/AuroraAI-Research/Aurora1.0-150M' } },
//{ name: 'ZeroS-Micro-v1.0', org: 'fromzero', params: 2993797, paramsDisplay: '2.99M', arc: 28.49, hellaswag: 27.32, piqa: 51.14, arcChall: 24.57, arithmark3: 30.80, links: { card: 'https://huggingface.co/fromziro/ZeroS-Micro-v1.0' } },
{ name: 'DynamicMind-MoE', org: 'dedeprogames', addedDate: '2026-09-14', params: 30150912, paramsDisplay: '30.15M', arc: 36.15, hellaswag: 27.47, piqa: 56.09, arcChall: 22.35, arithmark3: 35.00, links: { card: 'https://huggingface.co/DedeProGames/DynamicMind-MoE' } },
{ name: 'DynamicMind-Mini', org: 'dedeprogames', addedDate: '2026-09-14', params: 8884992, paramsDisplay: '8.88M', arc: 35.02, hellaswag: 26.99, piqa: 55.60, arcChall: 23.29, arithmark3: 35.60, links: { card: 'https://huggingface.co/DedeProGames/DynamicMind-Mini' } },
{ name: 'LowOnMind-8M', org: 'slmarchive', addedDate: '2026-09-14', params: 8060256, paramsDisplay: '8.06M', arc: 28.62, hellaswag: 25.45, piqa: 51.03, arcChall: 22.70, arithmark3: 27.30, links: { card: 'https://huggingface.co/SLM-Archive/LowOnMind-8M' } },
{ name: 'LowOnMind-5M', org: 'slmarchive', addedDate: '2026-09-14', params: 4920384, paramsDisplay: '4.92M', arc: 31.78, hellaswag: 27.69, piqa: 51.96, arcChall: 19.62, arithmark3: 33.10, links: { card: 'https://huggingface.co/SLM-Archive/LowOnMind-5M' } },
{ name: 'LowOnMind-1M', org: 'slmarchive', addedDate: '2026-09-14', params: 985152, paramsDisplay: '985K', arc: 28.41, hellaswag: 26.26, piqa: 52.50, arcChall: 22.44, arithmark3: 28.70, links: { card: 'https://huggingface.co/SLM-Archive/LowOnMind-1M' } },
{ name: 'LowOnMind-300k', org: 'slmarchive', addedDate: '2026-09-14', params: 296960, paramsDisplay: '297K', arc: 29.00, hellaswag: 25.07, piqa: 50.22, arcChall: 23.12, arithmark3: 29.40, links: { card: 'https://huggingface.co/SLM-Archive/LowOnMind-300k' } },
{ name: 'NanoDex-1M', org: 'slmarchive', addedDate: '2026-09-14', params: 1062272, paramsDisplay: '1.06M', arc: 29.55, hellaswag: 27.02, piqa: 53.21, arcChall: 22.95, arithmark3: 26.40, links: { card: 'https://huggingface.co/SLM-Archive/NanoDex-1M' } },
{ name: 'Sol-Lite-Base', org: 'solintellegence', addedDate: '2026-09-14', params: 14995843, paramsDisplay: '15M', arc: 34.81, hellaswag: 27.77, piqa: 57.89, arcChall: 22.95, arithmark3: 34.40, links: { card: 'https://huggingface.co/solintellegence/Sol-Lite-Base' } },
{ name: 'Sol Lite 2', org: 'solintellegence', addedDate: '2026-10-02', params: 14942592, paramsDisplay: '14.94M', arc: 36.27946127946128, hellaswag: 28.689504082852025, piqa: 56.80087051142546, arcChall: 24.914675767918087, arithmark3: 35.5, links: { card: 'https://huggingface.co/solintellegence/Sol-Lite-2' } },
{ name: 'Surjo-50M', org: 'surjolabs', addedDate: '2026-09-15', params: 53800000, paramsDisplay: '53.8Mx2*', arc: 44.99, hellaswag: 31.37, piqa: 63.11, arcChall: 25.94, arithmark3: 37.90, links: { card: 'https://huggingface.co/SurjoLabs/Surjo-50m' } },
{ name: 'GTM-3-base', org: 'opengcm', addedDate: '2026-09-15', params: 74900000, paramsDisplay: '74.9M', arc: 32.98, hellaswag: 25.70, piqa: 56.86, arithmark3: 37.20, links: { card: 'https://huggingface.co/OPENGCM/GTM-3-base' } },
{ name: 'Mystery-5M-Preview', org: 'mystery', addedDate: '2026-09-13', params: 5647652, paramsDisplay: '5.65M', arc: 36.03, hellaswag: 28.26, piqa: 57.56, arcChall: 23.89, arithmark3: 33.30, links: { card: 'https://huggingface.co/' } },
{ name: 'typakos-140M-it', org: 'alexliap', addedDate: '2026-09-15', params: 144797184, paramsDisplay: '145M', arc: 30.93, hellaswag: 26.77, piqa: 56.04, arcChall: 25.85, arithmark3: 36.60, arithmark2: 30.32, links: { card: 'https://huggingface.co/alexliap/typakos-140M-it' } },
{ name: 'cRia-LM-75M-Instruct', org: 'sz14', addedDate: '2026-09-16', params: 75719395, paramsDisplay: '75.72Mx2*', arc: 37.75, hellaswag: 32.07, piqa: 59.68, arcChall: 25.17, arithmark3: 35.20, links: { card: 'https://huggingface.co/sz14/cRia-LM-75M-Instruct' } },
{ name: 'NCN-RW', org: 'axiomiclabs', addedDate: '2026-10-03', params: 9720527, paramsDisplay: '9.72Mx2*', arc: 36.95, hellaswag: 27.73, piqa: 58.00, arcChall: 22.70, arithmark3: 30.20, links: { card: 'https://huggingface.co/AxiomicLabs/NCN-RW' } },
{ name: 'Surjo-100m', org: 'surjolabs', addedDate: '2026-09-18', params: 97700000, paramsDisplay: '97.7Mx2*', arc: 47.64, hellaswag: 35.05, piqa: 63.87, arcChall: 25.85, arithmark3: 38.90, links: { card: 'https://huggingface.co/SurjoLabs/Surjo-100m' } },
{ name: 'JugnuLM-110M-R2+', org: 'altslate', addedDate: '2026-09-19', params: 109737302, paramsDisplay: '110M', arc: 47.64, hellaswag: 36.74, piqa: 65.29, arcChall: 26.96, arithmark3: 38.60, links: { card: 'https://huggingface.co/altslate/JugnuLM-110M-R2plus' } },
{ name: 'peacebell-v1-148M', org: 'wayneworkman', addedDate: '2026-09-20', params: 148553302, paramsDisplay: '148.55M', arc: 27.27, hellaswag: 26.16, piqa: 47.61, arcChall: 26.71, arithmark3: 27.10, links: { card: 'https://huggingface.co/wayneworkman2012/peacebell-v1-148M' } },
{ name: 'cagliostro-v3', org: 'benchlabs', addedDate: '2026-09-20', params: 146352000, paramsDisplay: '146M', arc: 54.88, hellaswag: 42.51, piqa: 67.46, arcChall: 28.75, arithmark3: 43.70, links: { card: 'https://huggingface.co/bench-labs/cagliostro-v3' } },
{ name: 'Pollock 1.5', org: 'slayerlab', addedDate: '2026-09-21', params: 127565312, paramsDisplay: '127M', arc: 42.00, hellaswag: 30.81, piqa: 59.63, arcChall: 24.32, arithmark3: 34.30, links: { card: 'https://huggingface.co/SlayerLab/pollock-mini-lm-125m' } },
{ name: 'ForgePlex-M1-6M', org: 'forgeworks', addedDate: '2026-09-23', params: 6584928, paramsDisplay: '6.58M', arc: 35.02, hellaswag: 27.57, piqa: 56.26, arcChall: 22.70, arithmark3: 29.60, links: { card: 'https://huggingface.co/ForgeWorks/ForgePlex-M1-6M' } },
{ name: 'tinctura-v1', org: 'benchlabs', addedDate: '2026-09-24', params: 96200064, paramsDisplay: '96.2M', arc: 47.98, hellaswag: 37.96, piqa: 65.61, arcChall: 25.77, arithmark3: 38.40, links: { card: 'https://huggingface.co/bench-labs/tinctura-v1' } },
{ name: 'Hummingbird-V2', org: 'juinron', addedDate: '2026-09-24', params: 9592720, paramsDisplay: '9.6M', arc: 39.65, hellaswag: 27.61, piqa: 57.40, arcChall: 21.16, arithmark3: 36.00, links: { card: 'https://huggingface.co/juinron/Hummingbird-V2' } },
{ name: 'NACR-1.0', org: 'saicr', addedDate: '2026-09-25', params: 5067603, paramsDisplay: '3.0M', arc: 30.98, hellaswag: 26.79, piqa: 52.69, arcChall: 20.90, arithmark3: 28.20, links: { card: 'https://huggingface.co/saicr/nacr' } },
{ name: 'Sol-Milkshake', org: 'solintellegence', addedDate: '2026-09-26', params: 2990000, paramsDisplay: '2.99M', arc: 29.76, hellaswag: 25.02, piqa: 52.50, arcChall: 22.70, arithmark3: 30.60, links: { card: 'https://huggingface.co/solintellegence/Sol-Milkshake' } },
{ name: 'ZeroS-Micro-v1.0', org: 'fromzero', addedDate: '2026-09-27', params: 2869813, paramsDisplay: '2.87M', arc: 31.27, hellaswag: 28.46, piqa: 53.86, arcChall: 22.01, arithmark3: 35.60, links: { card: 'https://huggingface.co/fromziro/ZeroS-Micro-v1.0' } },
{ name: 'ForgePlex-M2-9M', org: 'forgeworks', addedDate: '2026-09-30', params: 9949698, paramsDisplay: '9.95M', arc: 36.53, hellaswag: 28.02, piqa: 57.18, arcChall: 23.46, arithmark3: 34.60, links: { card: 'https://huggingface.co/ForgeWorks/ForgePlex-M2-9M' } },
{ name: 'Sol Nano', org: 'solintellegence', addedDate: '2026-09-30', params: 2895188, paramsDisplay: '2.9M', arc: 32.07, hellaswag: 28.40, piqa: 53.92, arcChall: 21.16, arithmark3: 33.80, links: { card: 'https://huggingface.co/solintellegence/sol-nano' } },
{ name: 'Tokle-3M', org: 'techdotus', addedDate: '2026-09-30', params: 2908947, paramsDisplay: '2.91M', arc: 34.85, hellaswag: 27.20, piqa: 55.01, arcChall: 23.98, arithmark3: 40.80, links: { card: 'https://huggingface.co/techdotus/Tokle-3M' } },
{ name: 'pulvis-v1', org: 'benchlabs', addedDate: '2026-09-30', params: 2962240, paramsDisplay: '2.96Mx3*', arc: 31.61, hellaswag: 27.91, piqa: 55.93, arcChall: 22.27, arithmark3: 36.90, arithmark2: 48.04, links: { card: 'https://huggingface.co/bench-labs/pulvis-v1' } },
{ name: 'Veyra2-Mango-30M-Instruct', org: 'veyraai', addedDate: '2026-10-03', params: 30683520, paramsDisplay: '30.7M', arc: 38.55, hellaswag: 29.17, piqa: 59.52, arcChall: 22.27, arithmark3: 37.20, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Instruct' } },
{ name: 'Veyra2-Mango-15M-Instruct', org: 'veyraai', addedDate: '2026-10-03', params: 15735168, paramsDisplay: '15.7M', arc: 37.88, hellaswag: 27.40, piqa: 56.80, arcChall: 22.44, arithmark3: 34.40, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Mango-15M-Instruct' } },
{ name: 'Veyra2-Blueberry-10M-Instruct', org: 'veyraai', addedDate: '2026-10-03', params: 9976832, paramsDisplay: '9.9M', arc: 36.15, hellaswag: 27.77, piqa: 56.26, arcChall: 22.87, arithmark3: 37.60, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Blueberry-10M-Instruct' } },
{ name: 'Veyra2-Blueberry-5M-Instruct', org: 'veyraai', addedDate: '2026-10-03', params: 4984192, paramsDisplay: '4.9M', arc: 33.25, hellaswag: 27.45, piqa: 54.19, arcChall: 21.59, arithmark3: 36.60, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Blueberry-5M-Instruct' } },
{ name: 'pulvis-v2', org: 'benchlabs', addedDate: '2026-10-03', params: 2963840, paramsDisplay: '2.96Mx3*', arc: 31.86, hellaswag: 27.93, piqa: 56.86, arcChall: 22.78, arithmark3: 37.40, arithmark2: 40.16, links: { card: 'https://huggingface.co/bench-labs/pulvis-v2' } },
{ name: 'cagliostro-v3.5', org: 'benchlabs', addedDate: '2026-10-05', params: 146352000, paramsDisplay: '146M', arc: 53.75, hellaswag: 43.41, piqa: 68.06, arcChall: 29.35, arithmark3: 45.30, links: { card: 'https://huggingface.co/bench-labs/cagliostro-v3.5' } },
{ name: 'Rambley-V2-Alpha-10M', org: 'glossia', addedDate: '2026-10-05', params: 9_638_592, paramsDisplay: '10M', arc: 34.89, hellaswag: 27.56, arcChall: 21.84, piqa: 57.13, arithmark3: 33.10, links: { card: "https://huggingface.co/glossia/Rambley-V2-Alpha-10M" } },
{ name: 'Purrence-3M', org: 'jonnester', addedDate: '2026-10-06', params: 2987712, paramsDisplay: '2.99Mx6*', arc: 34.55, hellaswag: 27.81, piqa: 56.64, arcChall: 23.63, arithmark3: 34.80, arithmark2: 26.44, links: { card: 'https://huggingface.co/Jonnester/Purrence-3M' } },
{ name: 'Rambley-V2-Beta-10M', org: 'glossia', addedDate: '2026-10-06', params: 9_835_712, paramsDisplay: '10M', arc: 35.52, hellaswag: 27.06, arcChall: 21.25, piqa: 56.75, arithmark3: 33.50, links: { card: "https://huggingface.co/glossia/Rambley-V2-Beta-10M" } },
{ name: '50M-pro', org: 'fromzero', addedDate: '2026-10-06', params: 49590304, paramsDisplay: '49.59M', arc: 45.62, hellaswag: 33.25, piqa: 64.80, arcChall: 25.51, arithmark3: 37.40, links: { card: 'https://huggingface.co/fromziro/50M-pro' } },
{ name: 'Rambley-V2-Gamma-10M', org: 'glossia', addedDate: '2026-10-07', params: 9_835_712, paramsDisplay: '10M', arc: 35.44, hellaswag: 27.15, arcChall: 21.93, piqa: 56.15, arithmark3: 31.30, links: { card: "https://huggingface.co/glossia/Rambley-V2-Gamma-10M" } },
//{ name: 'GPT-X3-Preview-2', org: 'axiomiclabs', params: 149400930, paramsDisplay: '149M', arc: 55.01, hellaswag: 39.75, piqa: 69.10, arcChall: 28.75, arithmark3: 46.10, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X3-150M' } },
{ name: '100M-exp', org: 'user01110', addedDate: '2026-10-09', params: 98161434, paramsDisplay: '98.2M', arc: 49.83, hellaswag: 37.78, piqa: 66.97, arcChall: 27.22, arithmark3: 40.00, links: { card: 'https://huggingface.co/User01110/100M-exp' } },
//{ name: 'Sol Pro', org: 'solintellegence', params: 138037594, paramsDisplay: '138Mx2*', arc: 54.00, hellaswag: 46.74, piqa: 71.00, arcChall: 31.83, arithmark3: 36.00, links: { card: 'https://huggingface.co/solintellegence/sol-pro' } },
{ name: 'gibc-v2-lm', org: 'unluckyexe', addedDate: '2026-10-09', params: 49822228, paramsDisplay: '49.8M', arc: 47.85, hellaswag: 31.87, piqa: 60.28, arcChall: 26.11, arithmark3: 40.20, arithmark2: 36.80, links: { card: 'https://huggingface.co/Unluckyexe/gibc-v2-lm' } },
{ name: 'Myosotis-1.5-base-preview', org: 'fwkv', addedDate: '2026-10-09', params: 111726720, paramsDisplay: '112M', arc: 40.36, hellaswag: 27.69, piqa: 59.79, arcChall: 21.93, arithmark3: 33.50, links: { card: 'https://huggingface.co/FWKV/Myosotis-1.5-base-Preview' } },
{ name: 'void.1', org: 'dotlabs', addedDate: '2026-10-09', params: 90148352, paramsDisplay: '90.15M', arc: 49.32, hellaswag: 39.46, piqa: 67.19, arcChall: 29.35, arithmark3: 43.70, links: { card: 'https://huggingface.co/dotlabs/void.1' } },
{ name: 'ForgePlex-M2.1-10M', org: 'forgeworks', addedDate: '2026-10-10', params: 9991938, paramsDisplay: '9.99M', arc: 36.45, hellaswag: 29.09, piqa: 59.14, arcChall: 22.87, arithmark3: 35.50, links: { card: 'https://huggingface.co/ForgeWorks/ForgePlex-M2.1-10M' } },
{ type: 'orgMovementCutoff', label: 'Org leaderboard movement cutoff' },
{ name: 'Boris-2-Preview-1', org: 'opencerebral', addedDate: '2026-10-10', params: 143724032, paramsDisplay: '144M', arc: 58.38, hellaswag: 45.23, piqa: 69.21, arcChall: 29.95, arithmark3: 37.60, arithmark2: 37.68, links: { card: 'https://huggingface.co/opencerebral/Boris-2-Preview-1' } },
{ name: 'GPT-S3-3M', org: 'axiomiclabs', addedDate: '2026-10-10', params: 2973666, paramsDisplay: '2.97M', arc: 34.76, hellaswag: 27.68, piqa: 57.02, arcChall: 23.12, arithmark3: 39.10, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-S3-3M' } },
{ name: 'GPT-X3-150M', org: 'axiomiclabs', addedDate: '2026-10-07', params: 149000000, paramsDisplay: '150M', arc: 59.64, hellaswag: 46.16, piqa: 71.33, arcChall: 31.31, arithmark3: 50.90, arithmark2: 68.52, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X3-150M' } },
{ name: 'KLM-1.1', org: 'minimalabs', addedDate: '2026-10-11', params: 88108544, paramsDisplay: '88.1M', arc: 44.36, hellaswag: 33.65, piqa: 64.80, arcChall: 25.17, arithmark3: 35.80, links: { card: 'https://huggingface.co/MinimaLabs/KLM-1.1' } },
{ name: 'TinyBrainBot-100M-v3-Base', org: 'tinybrainbot', addedDate: '2026-10-14', params: 100092672, paramsDisplay: '100M', arc: 54.67, hellaswag: 32.62, piqa: 66.21, arcChall: 30.12, arithmark3: 37.40, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-100m-v3-base' } },
{ name: 'TinyBrainBot-25M-v4-Base', org: 'tinybrainbot', addedDate: '2026-10-14', params: 30554496, paramsDisplay: '30.6M', arc: 42.09, hellaswag: 28.86, piqa: 60.23, arcChall: 24.91, arithmark3: 35.20, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-25m-v4-base' } },
{ name: 'Rambley-V2-Delta-10M', org: 'glossia', addedDate: '2026-10-12', params: 9_851_456, paramsDisplay: '10M', arc: 36.36, hellaswag: 27.21, arcChall: 21.08, piqa: 55.55, arithmark3: 31.30, links: { card: "https://huggingface.co/glossia/Rambley-V2-Delta-10M" } },
{ name: 'Inferno-Exp-1.0', org: 'algodriveai', addedDate: '2026-10-12', params: 76163200, paramsDisplay: '76.2M', arc: 40.24, hellaswag: 27.46, piqa: 56.58, arcChall: 24.66, arithmark3: 54.20, arithmark2: 67.32, links: { card: 'https://huggingface.co/AlgoDriveAI/Inferno-Exp-1.0' } },
{ name: 'heron1i-flash-lite', org: 'heronetic', addedDate: '2026-10-12', params: 9924188, paramsDisplay: '9.92M', arc: 36.20, hellaswag: 28.36, piqa: 58.54, arcChall: 24.15, arithmark3: 35.60, links: { card: 'https://huggingface.co/Heronetic/heron1i-flash-lite' } },
{ name: 'min-spark-1.6', org: 'minimalabs', addedDate: '2026-10-13', params: 9956756, paramsDisplay: '9.96Mx4*', arc: 38.30, hellaswag: 28.52, piqa: 59.14, arcChall: 22.44, arithmark3: 36.90, links: { card: 'https://huggingface.co/MinimaLabs/min-spark-1.6' } },
{ name: 'Inferno-Exp-1.5', org: 'algodriveai', addedDate: '2026-10-13', params: 76163200, paramsDisplay: '76.2M', arc: 43.39, hellaswag: 27.85, piqa: 58.49, arcChall: 26.62, arithmark3: 71.00, arithmark2: 67.00, links: { card: 'https://huggingface.co/AlgoDriveAI/Inferno-Exp-1.5' } },
{ name: 'ForgePlex-M2.2-10M', org: 'forgeworks', addedDate: '2026-10-13', params: 9991938, paramsDisplay: '9.99M', arc: 39.77, hellaswag: 29.19, piqa: 59.79, arcChall: 22.70, arithmark3: 37.40, links: { card: 'https://huggingface.co/ForgeWorks/ForgePlex-M2.2-10M' } },
{ name: 'heron1i-flash-lite-v2', org: 'heronetic', addedDate: '2026-10-14', params: 9924188, paramsDisplay: '9.92M', arc: 38.89, hellaswag: 28.86, piqa: 58.54, arcChall: 23.38, arithmark3: 36.70, links: { card: 'https://huggingface.co/Heronetic/heron1i-flash-lite-v2' } },
{ name: 'Rambley-V2-350M-Preview', org: 'glossia', addedDate: '2026-10-14', params: 348583480, paramsDisplay: '350M', arc: 47.22, hellaswag: 35.95, arcChall: 26.37, piqa: 64.42, arithmark3: 37.80, links: { card: 'https://huggingface.co/glossia/Rambley-V2-350M-Preview' } },
{ name: 'GPT-2 Medium', org: 'openai', addedDate: '2026-10-14', params: 354823168, paramsDisplay: '355M', arc: 43.60, hellaswag: 39.38, arcChall: 25.00, piqa: 66.38, arithmark3: 33.80, links: { card: 'https://huggingface.co/openai-community/gpt2-medium' } },
{ name: 'OPT-350M', org: 'facebook', addedDate: '2026-10-14', params: 331196416, paramsDisplay: '331M', arc: 40.19, hellaswag: 36.82, arcChall: 23.72, piqa: 64.36, arithmark3: 38.30, links: { card: 'https://huggingface.co/facebook/opt-350m' } },
{ name: 'LFM-2.5-230M-Base', org: 'liquid', addedDate: '2026-10-14', params: 229693184, paramsDisplay: '230M', arc: 59.72, hellaswag: 39.29, arcChall: 34.98, piqa: 63.38, arithmark3: 36.60, links: { card: 'https://huggingface.co/LiquidAI/LFM2.5-230M-Base' } },
{ name: 'LFM-2.5-350M-Base', org: 'liquid', addedDate: '2026-10-14', params: 354483968, paramsDisplay: '350M', arc: 68.86, hellaswag: 45.16, arcChall: 41.13, piqa: 67.63, arithmark3: 36.10, links: { card: 'https://huggingface.co/LiquidAI/LFM2.5-350M-Base' } },
{ name: 'SmolLM2-360M', org: 'huggingface', addedDate: '2026-10-14', params: 361821120, paramsDisplay: '360M', arc: 68.48, hellaswag: 56.30, arcChall: 38.31, piqa: 71.60, arithmark3: 59.60, links: { card: 'https://huggingface.co/HuggingFaceTB/SmolLM2-360M' } },
{ name: 'MobileLLM-R1-360M-base', org: 'facebook', addedDate: '2026-10-14', params: 359431168, paramsDisplay: '360M', arc: 61.32, hellaswag: 44.20, arcChall: 34.56, piqa: 68.44, arithmark3: 80.40, links: { card: 'https://huggingface.co/facebook/MobileLLM-R1-360M-base' } },
{ name: 'MICA Flame-W 0.3.6', org: 'mica', addedDate: '2026-10-14', params: 31523432, paramsDisplay: '31.52M', arc: 27.272727, hellaswag: 27.643896, piqa: 52.176279, arcChall: 20.221843, arithmark3: 27.1, links: { card: 'https://huggingface.co/vynly/mica-flame-w-0.3.6' } },
{ name: 'Picolo-BananaMind', org: 'bananamind', addedDate: '2026-10-14', params: 100, paramsDisplay: '100', arc: 25.63, hellaswag: 23.70, piqa: 50.71, arcChall: 27.82, arithmark3: 28.40, links: { card: 'https://huggingface.co/BananaMind/Picolo-BananaMind' } },
{ name: 'Supra2.1-Ultra', org: 'supralabs', addedDate: '2026-10-14', params: 229688064, paramsDisplay: '229.7M', arc: 50.46, hellaswag: 37.18, piqa: 64.91, arcChall: 26.28, arithmark3: 38.20, links: { card: 'https://huggingface.co/SupraLabs/Supra2.1-Ultra' } },
{ name: 'Inferno-Exp-1.6', org: 'algodriveai', addedDate: '2026-10-15', params: 393610240, paramsDisplay: '393.6M', arc: 47.01, hellaswag: 29.80, piqa: 60.50, arcChall: 30.03, arithmark3: 83.10, links: { card: 'https://huggingface.co/AlgoDriveAI/Inferno-Exp-1.6' } },
{ name: 'TinyBrainBot-360M-A75M-v4.1-MoE-Base', org: 'tinybrainbot', addedDate: '2026-10-17', params: 383429376, paramsDisplay: '383M', arc: 55.64, hellaswag: 37.27, piqa: 66.32, arcChall: 32.34, arithmark3: 42.00, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-360m-a75m-v4.1-moe-base' } },
{ name: 'TinyBrainBot-350M-V3-Base', org: 'tinybrainbot', addedDate: '2026-10-17', params: 348342912, paramsDisplay: '348M', arc: 56.61, hellaswag: 35.90, piqa: 66.97, arcChall: 33.28, arithmark3: 39.30, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-350mV3-base' } },
{ name: 'Rambley-V2-64M', org: 'glossia', addedDate: '2026-10-17', params: 63874704, paramsDisplay: '64M', arc: 42.42, hellaswag: 30.60, arcChall: 23.81, piqa: 60.39, arithmark3: 34.30, links: { card: 'https://huggingface.co/glossia/Rambley-V2-64M' } },
{ name: 'Inferno-Exp-1.7', org: 'algodriveai', addedDate: '2026-10-18', params: 393610240, paramsDisplay: '393.6M', arc: 51.60, hellaswag: 33.68, piqa: 64.47, arcChall: 34.04, arithmark3: 85.70, links: { card: 'https://huggingface.co/AlgoDriveAI/Inferno-Exp-1.7' } },
{ name: 'Kiyo-230M-Instruct', org: 'dedeprogames', addedDate: '2026-10-23', params: 229688064, paramsDisplay: '229.7M', arc: 42.17, hellaswag: 36.83, piqa: 64.69, arcChall: 26.28, arithmark3: 35.70, links: { card: 'https://huggingface.co/DedeProGames/Kiyo-230M-Instruct' } },
{ name: 'GPT-U-20M', org: 'dedeprogames', addedDate: '2026-10-23', params: 20453760, paramsDisplay: '20.45M', arc: 37.25, hellaswag: 27.87, piqa: 58.38, arcChall: 21.67, arithmark3: 35.50, links: { card: 'https://huggingface.co/DedeProGames/GPT-U-20M' } },
{ name: 'cosmo.0', org: 'dedeprogames', addedDate: '2026-10-23', params: 34611712, paramsDisplay: '34.61M', arc: 36.07, hellaswag: 27.90, piqa: 59.09, arcChall: 23.89, arithmark3: 28.80, links: { card: 'https://huggingface.co/DedeProGames/cosmo.0' } },
{ name: 'Wisp-15M', org: 'dedeprogames', addedDate: '2026-10-23', params: 15531840, paramsDisplay: '15.53M', arc: 35.82, hellaswag: 27.97, piqa: 57.67, arcChall: 22.01, arithmark3: 34.30, links: { card: 'https://huggingface.co/DedeProGames/Wisp-15M' } },
{ name: 'Wisp-5M', org: 'dedeprogames', addedDate: '2026-10-23', params: 5115456, paramsDisplay: '5.12M', arc: 32.66, hellaswag: 27.09, piqa: 54.30, arcChall: 22.78, arithmark3: 33.50, links: { card: 'https://huggingface.co/DedeProGames/Wisp-5M' } },
{ name: 'KLM-1.5', org: 'minimalabs', addedDate: '2026-10-22', params: 98975308, paramsDisplay: '99.0M', arc: 59.68, hellaswag: 43.74, piqa: 68.55, arcChall: 32.68, arithmark3: 48.20, links: { card: 'https://huggingface.co/MinimaLabs/KLM-1.5' } },
{ name: 'Rambley-V2.1-64M', org: 'glossia', addedDate: '2026-10-20', params: 63903120, paramsDisplay: '64M', arc: 42.13, hellaswag: 31.49, arcChall: 24.23, piqa: 61.10, arithmark3: 35.50, links: { card: 'https://huggingface.co/glossia/Rambley-V2.1-64M' } },
{ name: 'Boris-2-Preview-2', org: 'opencerebral', addedDate: '2026-10-21', params: 143724032, paramsDisplay: '144M', arc: 59.13, hellaswag: 46.60, piqa: 70.84, arcChall: 30.20, arithmark3: 42.20, links: { card: 'https://huggingface.co/opencerebral/Boris-2-Preview-2' } },
{ name: 'Pragya-EXP2', org: 'RuVi', addedDate: '2026-10-20', params: 28500000, paramsDisplay: '28.5M', arc: 31.57, hellaswag: 29.76, piqa: 61.86, arcChall: 23.98, arithmark3: 38.30, links: { card: 'https://huggingface.co/RuVI-AI/Pragya-EXP2' } },
{ name: 'CLOD-149M', org: 'murzik344', addedDate: '2026-10-24', params: 148898078, paramsDisplay: '148.9M', arc: 45.37, hellaswag: 34.86, piqa: 67.08, arcChall: 26.88, arithmark3: 36.00, links: { card: 'https://huggingface.co/Murzik344/CLOD-149M' } },
{ name: 'LittleKedi-moderate-wide-sparse-base', org: 'littlekedi', addedDate: '2026-10-25', params: 173357088, paramsDisplay: '173M', arc: 45.24, hellaswag: 32.79, piqa: 62.02, arcChall: 25.43, arithmark3: 36.20, links: { card: 'https://huggingface.co/edededdy/LittleKedi-moderate-wide-sparse-base' } },
{ name: 'LS3-Micro-Base', org: 'ls', addedDate: '2026-10-25', params: 9973792, paramsDisplay: '9.97M', arc: 34.76, hellaswag: 27.31, piqa: 57.34, arcChall: 23.29, arithmark3: 38.90, links: { card: 'https://huggingface.co/Dsg2/LS3-Micro-Base' } },
{ name: 'Ethoric-10M', org: 'techdotus', addedDate: '2026-10-26', params: 9965664, paramsDisplay: '9.97M', arc: 37.58, hellaswag: 28.75, piqa: 59.85, arcChall: 24.66, arithmark3: 41.60, links: { card: 'https://huggingface.co/techdotus/Ethoric-10M' } },
{ name: 'min-spark-1.7', org: 'minimalabs', addedDate: '2026-10-26', params: 9975188, paramsDisplay: '9.98Mx4*', arc: 39.90, hellaswag: 29.65, piqa: 60.39, arcChall: 24.15, arithmark3: 36.60, links: { card: 'https://huggingface.co/MinimaLabs/min-spark-1.7' } },
{ name: 'Rose-1.5-Pro', org: 'godelev', addedDate: '2026-10-27', params: 148826543, paramsDisplay: '148.8M', arc: 58.29, hellaswag: 43.92, piqa: 69.48, arcChall: 30.72, arithmark3: 40.00, links: { card: 'https://huggingface.co/GODELEV/Rose-1.5-Pro' } },
{ name: 'TinyBrainBot-3M-v2-Base', org: 'tinybrainbot', addedDate: '2026-10-27', params: 2942928, paramsDisplay: '2.94M', arc: 33.38, hellaswag: 27.29, piqa: 56.75, arcChall: 22.53, arithmark3: 45.10, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-3m-v2-base' } },
{ name: 'Samarkand-253M', org: 'meridianmrm', addedDate: '2026-10-27', params: 253556096, paramsDisplay: '253.6M', arc: 50.84, hellaswag: 38.15, piqa: 66.49, arcChall: 26.96, arithmark3: 35.50, links: { card: 'https://huggingface.co/Meridian-MRM/Samarkand-253m' } },
{ name: 'Pythia-160M', org: 'eleutherai', addedDate: '2026-10-30', params: 162323040, paramsDisplay: '162M', arc: 39.65, hellaswag: 30.27, piqa: 61.97, arcChall: 23.63, arithmark3: 35.10, arithmark2: 28.04, links: { card: 'https://huggingface.co/EleutherAI/pythia-160m' } },
];
// ═══════════════════════════════════════════════════════════════
// ═══ STATE
// ═══════════════════════════════════════════════════════════════
const BENCHMARKS = ['arc', 'hellaswag', 'piqa', 'arcChall', 'arithmark3', 'arithmark2'];
const DEFAULT_SIZE_FILTERS = ['<150M', '<100M', '<50M', '<10M', '<3M'];
const SIZE_FILTER_GROUPS = {
default: DEFAULT_SIZE_FILTERS,
extended: ['all', '<250M', ...DEFAULT_SIZE_FILTERS]
};
const SIZE_FILTERS = SIZE_FILTER_GROUPS.extended;
const METRICS = [
{ key: 'intelligenceIndex', label: 'Int Index', fullLabel: 'Intelligence Index' },
{ key: 'hellaswag', label: 'HellaSwag', fullLabel: 'HellaSwag' },
{ key: 'arc', label: 'ARC-Easy', fullLabel: 'ARC-Easy' },
{ key: 'arcChall', label: 'ARC-Challenge', fullLabel: 'ARC-Challenge' },
{ key: 'piqa', label: 'PIQA', fullLabel: 'PIQA' },
{ key: 'arithmark3', label: 'ArithMark-3', fullLabel: 'ArithMark-3' },
{ key: 'arithmark2', label: 'ArithMark-2', fullLabel: 'ArithMark-2' },
];
const EXTRA_COLUMN_KEYS = new Set(['avg', 'fitZ', 'arithmark2']);
const FILTER_METRICS = METRICS.filter(metric => !EXTRA_COLUMN_KEYS.has(metric.key));
const RANDOM_BASELINES = {
avg: 31.25,
intelligenceIndex: 0,
hellaswag: 25,
arc: 25,
arcChall: 25,
piqa: 50,
arithmark2: 25,
arithmark3: 25
};
let sortCol = 'intelligenceIndex';
let sortAsc = false;
let activeSizeMode = 'default';
let activeFilter = SIZE_FILTER_GROUPS.default[0];
let activeBenchmark = 'intelligenceIndex';
let leaderboardQuery = '';
let extraColumnsOpen = false;
let chartInstances = {};
function getChartTooltipOptions() {
const styles = getComputedStyle(document.body);
return {
backgroundColor: styles.getPropertyValue('--chart-tooltip-background').trim(),
borderColor: styles.getPropertyValue('--chart-tooltip-border').trim(),
borderWidth: 1,
titleColor: styles.getPropertyValue('--text').trim(),
bodyColor: styles.getPropertyValue('--text-secondary').trim(),
padding: 10, cornerRadius: 6, displayColors: false,
titleFont: { family: "'DM Sans', sans-serif", size: 12, weight: '600' },
bodyFont: { family: "'DM Sans', sans-serif", size: 12 }
};
}
const ORG_MOVEMENT_CUTOFF_TYPE = 'orgMovementCutoff';
function isBelowOrgMovementCutoff(model) {
const cutoffIndex = MODELS.findIndex(m => m.type === ORG_MOVEMENT_CUTOFF_TYPE);
if (cutoffIndex === -1) return false;
return MODELS.indexOf(model) > cutoffIndex;
}
function getAvg(m) {
// Avg is a weighted mean: HellaSwag, arcAvg, PIQA, and ArithMark-3.
// Missing metrics do not contribute weight.
const components = [];
if (m.hellaswag !== null && m.hellaswag !== undefined) components.push(m.hellaswag);
const hasArc = m.arc !== null && m.arc !== undefined;
const hasArcChall = m.arcChall !== null && m.arcChall !== undefined;
if (hasArc && hasArcChall) components.push((m.arc + m.arcChall) / 2);
else if (hasArc) components.push(m.arc);
else if (hasArcChall) components.push(m.arcChall);
const hasPiqa = m.piqa !== null && m.piqa !== undefined;
const piqaWeight = hasPiqa ? 1 : 0;
const hasArithMark3 = m.arithmark3 !== null && m.arithmark3 !== undefined;
const arithMark3Weight = hasArithMark3 ? 1 : 0;
const metricCount = components.length + (hasPiqa ? 1 : 0) + (hasArithMark3 ? 1 : 0);
if (metricCount < 2) return null;
const weightedSum = components.reduce((a, b) => a + b, 0)
+ (hasPiqa ? m.piqa * piqaWeight : 0)
+ (hasArithMark3 ? m.arithmark3 * arithMark3Weight : 0);
return weightedSum / (
components.length
+ piqaWeight
+ arithMark3Weight
);
}
function normalizeFromChance(value, chance) {
return ((value - chance) / (100 - chance)) * 100;
}
function getIntelligenceIndex(m) {
// Intelligence Index uses weight 1 for HellaSwag, combined ARC, and PIQA,
// and weight 0.65 for ArithMark-3. Chance maps to 0; perfect maps to 100.
const components = [];
if (m.hellaswag !== null && m.hellaswag !== undefined) {
components.push(normalizeFromChance(m.hellaswag, RANDOM_BASELINES.hellaswag));
}
const hasArc = m.arc !== null && m.arc !== undefined;
const hasArcChall = m.arcChall !== null && m.arcChall !== undefined;
if (hasArc || hasArcChall) {
const arcScore = hasArc && hasArcChall ? (m.arc + m.arcChall) / 2 : (hasArc ? m.arc : m.arcChall);
components.push(normalizeFromChance(arcScore, RANDOM_BASELINES.arc));
}
if (m.piqa !== null && m.piqa !== undefined) {
components.push(normalizeFromChance(m.piqa, RANDOM_BASELINES.piqa));
}
const hasArithMark3 = m.arithmark3 !== null && m.arithmark3 !== undefined;
const metricCount = components.length + (hasArithMark3 ? 1 : 0);
if (metricCount < 2) return null;
const arithMark3Weight = hasArithMark3 ? 0.65 : 0;
const weightedSum = components.reduce((sum, value) => sum + value, 0)
+ (hasArithMark3
? normalizeFromChance(m.arithmark3, RANDOM_BASELINES.arithmark3) * arithMark3Weight
: 0);
return weightedSum / (components.length + arithMark3Weight);
}
function getSortVal(m, col, fitMap = null) {
if (col === 'avg') return getMetricValue(m, 'avg') ?? -Infinity;
if (col === 'intelligenceIndex') return getIntelligenceIndex(m) ?? -Infinity;
if (col === 'params') return m.params ?? 0;
if (col === 'rank') return getIntelligenceIndex(m) ?? -Infinity;
if (col === 'fitZ') return (fitMap || getModelFitMap()).get(m.name)?.fitZ ?? -Infinity;
return m[col] ?? -Infinity;
}
function getMetricValue(m, metric = activeBenchmark) {
if (metric === 'avg') return getAvg(m);
if (metric === 'intelligenceIndex') return getIntelligenceIndex(m);
return m[metric];
}
function getMetricLabel(metric = activeBenchmark, full = false) {
const item = METRICS.find(x => x.key === metric);
return item ? (full ? item.fullLabel : item.label) : metric;
}
function getMetricUnit(metric = activeBenchmark) {
return metric === 'intelligenceIndex' ? '' : '%';
}
function formatMetricValue(value, metric = activeBenchmark, decimals = 2) {
return `${value.toFixed(decimals)}${getMetricUnit(metric)}`;
}
function getModelEntries(models = MODELS) {
return models.filter(m => !m.type);
}
function modelAnchorId(model) {
return `${model.org}-${model.name}`.toLowerCase().replace(/[^a-z0-9._-]+/g, '-').replace(/-+/g, '-').replace(/^-|-$/g, '');
}
function getFilteredModels(models = MODELS, filter = activeFilter) {
let arr = getModelEntries(models);
if (filter === '<3M') arr = arr.filter(m => m.params && m.params < 3000000);
else if (filter === '<10M') arr = arr.filter(m => m.params && m.params < 10000000);
else if (filter === '<50M') arr = arr.filter(m => m.params && m.params < 50000000);
else if (filter === '<100M') arr = arr.filter(m => m.params && m.params < 100000000);
else if (filter === '<150M') arr = arr.filter(m => m.params && m.params < 150000000);
else if (filter === '<250M') arr = arr.filter(m => m.params && m.params < 250000000);
return arr;
}
function getSizeLeader(filter) {
let leader = null;
let bestScore = -Infinity;
for (const model of getFilteredModels(MODELS, filter)) {
const score = getIntelligenceIndex(model);
if (Number.isFinite(score) && score > bestScore) {
leader = model;
bestScore = score;
}
}
return leader;
}
function getCategoryLeaders() {
const leaders = new Map();
for (const filter of SIZE_FILTERS) {
const leader = getSizeLeader(filter);
if (!leader) continue;
if (!leaders.has(leader.name)) leaders.set(leader.name, []);
leaders.get(leader.name).push(filter === 'all' ? 'All sizes' : filter);
}
return leaders;
}
function getCategoryLeaderTitle(categories) {
return categories ? `Category leader: ${categories.join(', ')}`.replace(/</g, '&lt;') : '';
}
function getChartModelBorders(models) {
const leaders = getCategoryLeaders();
const gold = getComputedStyle(document.documentElement).getPropertyValue('--leader-gold').trim();
return models.map(model => leaders.has(model.name) ? gold : ORGS[model.org].chartBorder);
}
function getLeaderboardModels(models = getFilteredModels()) {
const normalizeSearchText = value => String(value || '')
.normalize('NFKD')
.replace(/[\u0300-\u036f]/g, '')
.toLowerCase()
.replace(/[^a-z0-9]+/g, ' ')
.trim();
const query = normalizeSearchText(leaderboardQuery);
if (!query) return models;
const tokens = query.split(/\s+/).filter(Boolean);
return models.filter(model => {
const org = ORGS[model.org];
const searchable = normalizeSearchText([
model.name,
org?.name,
model.org,
model.paramsDisplay,
model.links?.card
]
.filter(Boolean)
.join(' ')
);
const compactSearchable = searchable.replace(/\s+/g, '');
return tokens.every(token => {
const compactToken = token.replace(/\s+/g, '');
return searchable.includes(token) || compactSearchable.includes(compactToken);
});
});
}
function getChartModels() {
return getFilteredModels()
.filter(m => getMetricValue(m, activeBenchmark) !== null && getMetricValue(m, activeBenchmark) !== undefined);
}
function getAvgModels(models = getFilteredModels()) {
return models
.map(m => ({ ...m, avg: getAvg(m) }))
.filter(m => m.avg !== null && m.avg !== undefined);
}
function getIntelligenceIndexModels(models = getFilteredModels()) {
return models
.map(m => ({ ...m, intelligenceIndex: getIntelligenceIndex(m) }))
.filter(m => Number.isFinite(m.intelligenceIndex));
}
function getSortedModels(models = getFilteredModels(), fitMapForSort = null) {
const arr = [...models];
const fitMap = sortCol === 'fitZ' ? (fitMapForSort || getModelFitMap(arr)) : null;
arr.sort((a, b) => {
const av = getSortVal(a, sortCol, fitMap);
const bv = getSortVal(b, sortCol, fitMap);
return sortAsc ? (av > bv ? 1 : -1) : (av > bv ? -1 : 1);
});
return arr;
} function formatSigned(value) {
if (value === null || value === undefined) return 'N/A';
return `${value >= 0 ? '+' : ''}${value.toFixed(2)}`;
}
function topByMetric(metric) {
return getFilteredModels()
.map(m => ({ ...m, score: getMetricValue(m, metric) }))
.filter(m => m.score !== null && m.score !== undefined)
.sort((a, b) => b.score - a.score)[0] || null;
}
function getStdDev(values) {
if (!values.length) return 0;
const mean = values.reduce((sum, v) => sum + v, 0) / values.length;
const variance = values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length;
return Math.sqrt(variance);
}
function getModelFitMap(modelsForRows = getFilteredModels()) {
const sourceModels = getIntelligenceIndexModels(modelsForRows)
.filter(m => m.params)
.map(m => ({ ...m, logParams: Math.log10(m.params) }));
if (sourceModels.length < 2) return new Map();
const { slope, intercept } = linearRegression(sourceModels.map(m => [m.logParams, m.intelligenceIndex]));
const fitResiduals = sourceModels.map(m => m.intelligenceIndex - (slope * m.logParams + intercept));
const residualStd = getStdDev(fitResiduals);
const denom = residualStd || 1;
const withFit = sourceModels.map(m => {
const expected = slope * m.logParams + intercept;
return { ...m, expected, residual: m.intelligenceIndex - expected };
});
return new Map(withFit.map(m => [m.name, { ...m, fitZ: m.residual / denom }]));
}
// ─── Green color scale: muted gray β†’ #4ade80 ───
function scoreColor(value, min, max) {
if (value === null || value === undefined || min === max) return '';
let pct = (value - min) / (max - min);
pct = Math.max(0, Math.min(1, Math.pow(pct, 1.5)));
const r = Math.round(107 + (74 - 107) * pct);
const g = Math.round(107 + (222 - 107) * pct);
const b = Math.round(110 + (128 - 110) * pct);
return `color: rgb(${r},${g},${b})`;
}
// ═══════════════════════════════════════════════════════════════
// ═══ RENDER: FILTER BAR
// ═══════════════════════════════════════════════════════════════
function renderFilters() {
const bar = document.getElementById('filter-bar');
const filters = SIZE_FILTER_GROUPS[activeSizeMode];
bar.innerHTML = filters.map(f => `
<button class="filter-btn ${activeFilter === f ? 'active' : ''}" type="button" aria-pressed="${activeFilter === f}" onclick="setFilter('${f}')">${f === 'all' ? 'All' : f.replace(/</g, '&lt;')}</button>
`).join('');
Object.keys(SIZE_FILTER_GROUPS).forEach(mode => {
const button = document.getElementById(`size-mode-${mode}`);
button.classList.toggle('active', activeSizeMode === mode);
button.setAttribute('aria-pressed', String(activeSizeMode === mode));
});
}
function renderBenchmarkFilters() {
const benchmarkBar = document.getElementById('benchmark-filter-bar');
if (!benchmarkBar) return;
benchmarkBar.innerHTML = FILTER_METRICS.map(m => `
<button class="filter-btn ${activeBenchmark === m.key ? 'active' : ''}" onclick="setBenchmark('${m.key}')">${m.label}</button>
`).join('');
}
function applyReleaseCardColors() {
document.querySelectorAll('.release-card[data-org]').forEach(card => {
const org = ORGS[card.dataset.org];
if (org) card.style.borderTopColor = org.chartBorder;
});
}
function initReleaseCarousel() {
const viewport = document.querySelector('#new-releases .release-grid');
const previousButton = document.getElementById('release-prev');
const nextButton = document.getElementById('release-next');
const status = document.getElementById('release-page-status');
if (!viewport || !previousButton || !nextButton || !status || viewport.dataset.carouselReady === 'true') return;
const cards = Array.from(viewport.children).filter(card => card.classList.contains('release-card'));
const pageSize = 4;
const track = document.createElement('div');
track.className = 'release-carousel-track';
const pages = [];
for (let start = 0; start < cards.length; start += pageSize) {
const page = document.createElement('div');
page.className = 'release-carousel-page';
page.setAttribute('aria-label', `Release page ${pages.length + 1}`);
cards.slice(start, start + pageSize).forEach(card => page.appendChild(card));
pages.push(page);
track.appendChild(page);
}
viewport.replaceChildren(track);
viewport.dataset.carouselReady = 'true';
let activePage = 0;
let pointerStartX = null;
function renderReleasePage() {
const pageCount = Math.max(pages.length, 1);
activePage = Math.max(0, Math.min(activePage, pageCount - 1));
track.style.transform = `translateX(-${activePage * 100}%)`;
previousButton.disabled = activePage === 0;
nextButton.disabled = activePage >= pageCount - 1;
status.textContent = `${String(activePage + 1).padStart(2, '0')} / ${String(pageCount).padStart(2, '0')}`;
pages.forEach((page, index) => {
page.setAttribute('aria-hidden', String(index !== activePage));
});
window.requestAnimationFrame(function () {
const active = pages[activePage];
if (active) viewport.style.height = `${active.offsetHeight}px`;
});
}
function moveReleasePage(direction) {
const nextPage = activePage + direction;
if (nextPage < 0 || nextPage >= pages.length) return;
activePage = nextPage;
renderReleasePage();
}
previousButton.addEventListener('click', function () { moveReleasePage(-1); });
nextButton.addEventListener('click', function () { moveReleasePage(1); });
viewport.addEventListener('pointerdown', function (event) {
pointerStartX = event.clientX;
});
viewport.addEventListener('pointerup', function (event) {
if (pointerStartX === null) return;
const distance = event.clientX - pointerStartX;
pointerStartX = null;
if (Math.abs(distance) < 48) return;
moveReleasePage(distance < 0 ? 1 : -1);
});
viewport.addEventListener('pointercancel', function () { pointerStartX = null; });
window.addEventListener('resize', renderReleasePage);
renderReleasePage();
}
function renderHighlights() {
const overview = document.getElementById('overview-strip');
if (!overview) return;
const models = getModelEntries();
const orgCount = new Set(models.map(model => model.org).filter(Boolean)).size;
overview.innerHTML = `
<div class="overview-counts">
<span class="overview-stat"><strong>${models.length}</strong><span>Models tracked</span></span>
<span class="overview-stat"><strong>${orgCount}</strong><span>Organizations represented</span></span>
</div>
<span class="overview-scope">Open weights <span aria-hidden="true">&middot;</span> Below 400M parameters</span>
`;
}
function setSizeMode(mode) {
if (!SIZE_FILTER_GROUPS[mode] || mode === activeSizeMode) return;
activeSizeMode = mode;
setFilter(SIZE_FILTER_GROUPS[mode][0]);
}
function setFilter(f) {
if (!SIZE_FILTERS.includes(f)) return;
if (!SIZE_FILTER_GROUPS[activeSizeMode].includes(f)) activeSizeMode = 'extended';
activeFilter = f;
renderFilters();
renderHighlights();
renderTable();
renderBarChart();
renderScatter();
}
function setBenchmark(metric) {
if (EXTRA_COLUMN_KEYS.has(metric)) return;
activeBenchmark = metric;
renderBenchmarkFilters();
renderBarChart();
renderScatter();
}
// ═══════════════════════════════════════════════════════════════
// ═══ RENDER: TABLE
// ═══════════════════════════════════════════════════════════════
function renderTable() {
const tbody = document.getElementById('leaderboard-body');
const allModels = getLeaderboardModels();
const fitMap = getModelFitMap(allModels);
const sorted = getSortedModels(allModels, fitMap);
const categoryLeaders = getCategoryLeaders();
if (!sorted.length) {
tbody.innerHTML = '<tr><td colspan="13" class="td-model" style="padding:24px;color:var(--text-secondary);">No models match this search.</td></tr>';
return;
}
// Best values per column (for highlighting + color scaling)
const getBest = (key) => {
const vals = allModels.map(m => m[key]).filter(v => v !== null && v !== undefined);
return vals.length ? Math.max(...vals) : null;
};
const getMin = (key) => {
const vals = allModels.map(m => m[key]).filter(v => v !== null && v !== undefined);
return vals.length ? Math.min(...vals) : null;
};
const best = {};
const mins = {};
BENCHMARKS.forEach(k => { best[k] = getBest(k); mins[k] = getMin(k); });
const intelligenceIndexVals = allModels.map(getIntelligenceIndex).filter(v => v !== null);
best.intelligenceIndex = intelligenceIndexVals.length ? Math.max(...intelligenceIndexVals) : null;
mins.intelligenceIndex = intelligenceIndexVals.length ? Math.min(...intelligenceIndexVals) : null;
const avgVals = allModels.map(getAvg).filter(v => v !== null);
best.avg = avgVals.length ? Math.max(...avgVals) : null;
mins.avg = avgVals.length ? Math.min(...avgVals) : null;
const fmtCell = (val, key) => {
if (val === null || val === undefined) return '<span class="td-score na">β€”</span>';
const isBest = best[key] !== null && Math.abs(val - best[key]) < 0.001;
const cls = isBest ? 'best' : '';
return `<span class="td-score ${cls}" style="${scoreColor(val, mins[key], best[key])}">${val.toFixed(2)}%</span>`;
};
const fmtFitCell = (m) => {
const fit = fitMap.get(m.name);
if (!fit) return '<span class="td-score na">N/A</span>';
const color = fit.fitZ >= 0 ? 'var(--best)' : '#f87171';
return `<span class="td-score" style="color:${color}">${formatSigned(fit.fitZ)}</span>`;
};
tbody.innerHTML = sorted.map((m, i) => {
const org = ORGS[m.org];
const rank = i + 1;
const intelligenceIndex = getIntelligenceIndex(m);
const avg = getAvg(m);
const cutoffTag = isBelowOrgMovementCutoff(m) ? '<span class="rank-move new">new</span>' : '';
const inCompare = compareSet.has(m.name);
return `
<tr id="${modelAnchorId(m)}">
<td class="td-compare"><button class="compare-toggle ${inCompare ? 'active' : ''}" onclick="toggleCompare('${encodeURIComponent(m.name)}')" title="${inCompare ? 'Remove from comparison' : 'Add to comparison'}" aria-pressed="${inCompare}">${inCompare ? 'βˆ’' : '+'}</button></td>
<td class="td-rank">${rank}</td>
<td class="td-model"><a class="${categoryLeaders.has(m.name) ? 'category-leader' : ''}" title="${getCategoryLeaderTitle(categoryLeaders.get(m.name))}" href="${m.links.card}" target="_blank">${m.name}</a>${cutoffTag}<span class="model-org"><a href="${org.url}" target="_blank">${org.name}</a> &middot; ${m.paramsDisplay}</span></td>
<td class="td-params">${m.paramsDisplay}</td>
<td class="td-score">${intelligenceIndex !== null ? `<span class="${Math.abs(intelligenceIndex - best.intelligenceIndex) < 0.001 ? 'best' : ''}" style="${scoreColor(intelligenceIndex, Math.min(0, mins.intelligenceIndex), best.intelligenceIndex)}">${intelligenceIndex.toFixed(2)}</span>` : '<span class="td-score na">&mdash;</span>'}</td>
<td class="td-score">${fmtCell(m.hellaswag, 'hellaswag')}</td>
<td class="td-score">${fmtCell(m.arc, 'arc')}</td>
<td class="td-score">${fmtCell(m.arcChall, 'arcChall')}</td>
<td class="td-score">${fmtCell(m.piqa, 'piqa')}</td>
<td class="td-score">${fmtCell(m.arithmark3, 'arithmark3')}</td>
<td class="td-score extra-col extra-col-start">${avg !== null ? fmtCell(avg, 'avg') : '<span class="td-score na">&mdash;</span>'}</td>
<td class="td-score extra-col">${fmtFitCell(m)}</td>
<td class="td-score extra-col">${fmtCell(m.arithmark2, 'arithmark2')}</td>
</tr>
`;
}).join('');
// Sort indicators
document.querySelectorAll('#leaderboard-table thead th').forEach(th => {
th.classList.remove('sorted');
const ind = th.querySelector('.sort-indicator');
if (ind) ind.textContent = '';
});
const colMap = { rank: 1, name: 2, params: 3, intelligenceIndex: 4, hellaswag: 5, arc: 6, arcChall: 7, piqa: 8, arithmark3: 9, avg: 10, fitZ: 11, arithmark2: 12 };
const thIdx = colMap[sortCol];
if (thIdx !== undefined) {
const th = document.querySelectorAll('#leaderboard-table thead th')[thIdx];
if (th) {
th.classList.add('sorted');
const ind = th.querySelector('.sort-indicator');
if (ind) ind.textContent = sortAsc ? 'β–²' : 'β–Ό';
}
}
}
function sortTable(col) {
if (sortCol === col) {
sortAsc = !sortAsc;
} else {
sortCol = col;
sortAsc = false;
}
renderTable();
}
function toggleExtraColumns() {
const nextOpen = !extraColumnsOpen;
extraColumnsOpen = nextOpen;
const shell = document.getElementById('leaderboard-table-shell');
const toggle = document.getElementById('extra-columns-toggle');
if (shell) {
if (shell._extrasCloseTimer) {
clearTimeout(shell._extrasCloseTimer);
shell._extrasCloseTimer = null;
}
shell.classList.remove('extras-closing');
if (nextOpen) {
shell.classList.remove('extras-open');
void shell.offsetWidth;
shell.classList.add('extras-open');
} else {
shell.classList.remove('extras-open');
shell.classList.add('extras-closing');
shell._extrasCloseTimer = setTimeout(function () {
shell.classList.remove('extras-closing');
shell._extrasCloseTimer = null;
}, 430);
}
}
if (toggle) {
toggle.setAttribute('aria-expanded', String(extraColumnsOpen));
const actionLabel = `${extraColumnsOpen ? 'Hide' : 'Show'} more columns`;
toggle.setAttribute('aria-label', actionLabel);
toggle.title = actionLabel;
}
if (!extraColumnsOpen && EXTRA_COLUMN_KEYS.has(sortCol)) {
sortCol = 'intelligenceIndex';
sortAsc = false;
}
renderTable();
}
// ═══════════════════════════════════════════════════════════════
// ═══ COMPARE
// ═══════════════════════════════════════════════════════════════
const MAX_COMPARE = 6;
let compareSet = new Set();
function toggleCompare(encodedName) {
const name = decodeURIComponent(encodedName);
if (compareSet.has(name)) {
compareSet.delete(name);
} else {
if (compareSet.size >= MAX_COMPARE) {
flashCompareLimit();
return;
}
compareSet.add(name);
}
renderTable();
renderCompare();
renderCompareBar();
}
function removeCompare(encodedName) {
const name = decodeURIComponent(encodedName);
compareSet.delete(name);
renderTable();
renderCompare();
renderCompareBar();
}
function clearCompare() {
compareSet.clear();
renderTable();
renderCompare();
renderCompareBar();
}
// In-page navigation that works even with <base target="_blank">
// (which would otherwise open hash links in a new tab inside the HF iframe).
function scrollToSection(id) {
const el = document.getElementById(id);
if (el) el.scrollIntoView({ behavior: 'smooth', block: 'start' });
return false;
}
// ─── Side nav rail ───
const NAV_ITEMS = [
{ id: 'leaderboard', label: 'Leaderboard' },
{ id: 'compare', label: 'Compare' },
{ id: 'score-chart', label: 'Scores' },
{ id: 'charts', label: 'Efficiency' },
{ id: 'org-performance', label: 'History' },
{ id: 'rules', label: 'Rules' },
];
function renderSideNav() {
const nav = document.getElementById('side-nav');
if (!nav) return;
nav.innerHTML = NAV_ITEMS.map(i => `
<a href="#${i.id}" data-target="${i.id}" onclick="return scrollToSection('${i.id}')">
<span class="side-nav-dot"></span><span class="side-nav-label">${i.label}</span>
</a>`).join('');
}
function initSideNav() {
renderSideNav();
const links = {};
document.querySelectorAll('#side-nav a').forEach(a => { links[a.dataset.target] = a; });
if (!('IntersectionObserver' in window)) return;
const observer = new IntersectionObserver((entries) => {
entries.forEach(entry => {
if (entry.isIntersecting) {
Object.values(links).forEach(l => l.classList.remove('active'));
const a = links[entry.target.id];
if (a) a.classList.add('active');
}
});
}, { rootMargin: '-45% 0px -50% 0px', threshold: 0 });
NAV_ITEMS.forEach(i => {
const el = document.getElementById(i.id);
if (el) observer.observe(el);
});
}
function flashCompareLimit() {
const note = document.getElementById('compare-limit-note');
if (!note) return;
note.classList.add('show');
clearTimeout(flashCompareLimit._t);
flashCompareLimit._t = setTimeout(() => note.classList.remove('show'), 2200);
}
// Resolve compare names against the FULL model list so a comparison
// survives changes to the active size filter.
function getCompareModels() {
const byName = new Map(getModelEntries(MODELS).map(m => [m.name, m]));
return [...compareSet].map(name => byName.get(name)).filter(Boolean);
}
const COMPARE_ROWS = [
{ key: 'params', label: 'Parameters', type: 'params' },
{ key: 'avg', label: 'Avg', type: 'score' },
{ key: 'fitZ', label: 'Fit Std Devs', type: 'fit' },
{ key: 'hellaswag', label: 'HellaSwag', type: 'score' },
{ key: 'arc', label: 'ARC-Easy', type: 'score' },
{ key: 'arcChall', label: 'ARC-Challenge', type: 'score' },
{ key: 'piqa', label: 'PIQA', type: 'score' },
{ key: 'arithmark3', label: 'ArithMark-3', type: 'score' },
{ key: 'arithmark2', label: 'ArithMark-2', type: 'score' },
];
function compareRowValue(m, row, fitMap) {
if (row.type === 'params') return m.params ?? null;
if (row.type === 'fit') return fitMap.get(m.name)?.fitZ ?? null;
if (row.key === 'avg') return getAvg(m);
return m[row.key] ?? null;
}
function renderCompare() {
const section = document.getElementById('compare');
const body = document.getElementById('compare-content');
if (!section || !body) return;
const models = getCompareModels();
const countEl = document.getElementById('compare-count-label');
if (countEl) countEl.textContent = models.length ? `${models.length} selected` : 'None selected';
if (!models.length) {
body.innerHTML = `
<div class="compare-empty">
<p>No models selected yet.</p>
<p class="compare-empty-hint">Hit the <span class="compare-empty-plus">+</span> button on any leaderboard row to line models up side by side here.</p>
</div>`;
if (chartInstances['compare']) { chartInstances['compare'].destroy(); delete chartInstances['compare']; }
return;
}
// Fit computed against the full model set so it's stable regardless of filter.
const fitMap = getModelFitMap(getModelEntries(MODELS));
// Best value per row (higher is better for every metric here).
const bestByRow = {};
COMPARE_ROWS.forEach(row => {
if (row.type === 'params') return; // don't crown a "best" param count
const vals = models.map(m => compareRowValue(m, row, fitMap)).filter(v => v !== null && v !== undefined);
bestByRow[row.key] = vals.length ? Math.max(...vals) : null;
});
const headCells = models.map(m => {
const org = ORGS[m.org];
return `
<th class="compare-model-head" style="border-top:2px solid ${org.chartBorder}">
<button class="compare-remove" onclick="removeCompare('${encodeURIComponent(m.name)}')" title="Remove ${m.name}">Γ—</button>
<a class="compare-model-name" href="${m.links.card}" target="_blank">${m.name}</a>
<span class="compare-model-org"><a href="${org.url}" target="_blank">${org.name}</a></span>
</th>`;
}).join('');
const bodyRows = COMPARE_ROWS.map(row => {
const cells = models.map(m => {
const val = compareRowValue(m, row, fitMap);
if (val === null || val === undefined) return `<td class="compare-cell na">β€”</td>`;
if (row.type === 'params') {
return `<td class="compare-cell params">${m.paramsDisplay}</td>`;
}
if (row.type === 'fit') {
const isBest = bestByRow[row.key] !== null && Math.abs(val - bestByRow[row.key]) < 0.0001;
const color = val >= 0 ? 'var(--best)' : '#f87171';
return `<td class="compare-cell ${isBest ? 'best' : ''}" style="color:${color}">${formatSigned(val)}</td>`;
}
const isBest = bestByRow[row.key] !== null && Math.abs(val - bestByRow[row.key]) < 0.0001;
return `<td class="compare-cell ${isBest ? 'best' : ''}" style="${isBest ? '' : scoreColor(val, RANDOM_BASELINES[row.key] ?? 25, bestByRow[row.key])}">${val.toFixed(2)}%</td>`;
}).join('');
return `<tr><td class="compare-metric">${row.label}</td>${cells}</tr>`;
}).join('');
body.innerHTML = `
<div class="table-wrap compare-table-wrap">
<table class="compare-table">
<thead>
<tr><th class="compare-metric-head">Metric</th>${headCells}</tr>
</thead>
<tbody>${bodyRows}</tbody>
</table>
</div>
<div class="chart-card" style="margin-top:24px;">
<h3>Benchmark comparison</h3>
<canvas id="compareChart"></canvas>
</div>`;
renderCompareChart(models);
}
function renderCompareChart(models) {
const canvas = document.getElementById('compareChart');
if (!canvas) return;
const ctx = canvas.getContext('2d');
if (chartInstances['compare']) chartInstances['compare'].destroy();
const benchLabels = ['HellaSwag', 'ARC-Easy', 'ARC-Challenge', 'PIQA', 'ArithMark-3', 'ArithMark-2'];
const benchKeys = ['hellaswag', 'arc', 'arcChall', 'piqa', 'arithmark3', 'arithmark2'];
const modelBorders = getChartModelBorders(models);
const datasets = models.map((m, index) => {
const org = ORGS[m.org];
return {
label: m.name,
data: benchKeys.map(k => m[k] ?? null),
backgroundColor: org.chartColor.replace('0.70', '0.82'),
borderColor: modelBorders[index],
borderWidth: 1.5,
borderRadius: 4,
borderSkipped: false
};
});
chartInstances['compare'] = new Chart(ctx, {
type: 'bar',
data: { labels: benchLabels, datasets },
options: {
responsive: true,
maintainAspectRatio: true,
animation: { duration: 0 },
plugins: {
legend: {
display: true,
position: 'top',
labels: { color: '#6b6b6e', font: { family: "'DM Sans', system-ui, sans-serif", size: 11 }, boxWidth: 10, boxHeight: 10, usePointStyle: true, pointStyle: 'circle' }
},
tooltip: {
...getChartTooltipOptions(),
displayColors: true,
callbacks: { label: (item) => `${item.dataset.label}: ${item.raw === null ? 'N/A' : item.raw.toFixed(2) + '%'}` }
}
},
scales: {
x: {
grid: { display: false },
ticks: { color: '#6b6b6e', font: { family: "'DM Sans', system-ui, sans-serif", size: 11 } },
border: { display: false }
},
y: {
min: 0,
title: { display: true, text: 'Score (%)', color: '#454548', font: { family: "'Space Mono', monospace", size: 10 } },
grid: { color: 'rgba(255,255,255,0.03)' },
ticks: { color: '#454548', font: { family: "'Space Mono', monospace", size: 10 }, callback: v => v + '%', maxTicksLimit: 8 },
border: { display: false }
}
}
}
});
}
function renderCompareBar() {
const bar = document.getElementById('compare-bar');
if (!bar) return;
const models = getCompareModels();
if (!models.length) {
bar.classList.remove('show');
bar.innerHTML = '';
return;
}
const chips = models.map(m => {
const org = ORGS[m.org];
return `<span class="compare-chip" style="border-color:${org.chartBorder}66"><span class="compare-chip-dot" style="background:${org.chartBorder}"></span>${m.name}<button class="compare-chip-x" onclick="removeCompare('${encodeURIComponent(m.name)}')" title="Remove ${m.name}">Γ—</button></span>`;
}).join('');
bar.innerHTML = `
<div class="compare-bar-inner">
<span class="compare-bar-label">Comparing ${models.length}/${MAX_COMPARE}</span>
<div class="compare-bar-chips">${chips}</div>
<div class="compare-bar-actions">
<button class="compare-bar-view" onclick="scrollToSection('compare')">View comparison β†’</button>
<button class="compare-bar-clear" onclick="clearCompare()">Clear</button>
</div>
</div>`;
bar.classList.add('show');
}
function renderLegend() {
const bar = document.getElementById('legend-bar');
bar.innerHTML = Object.entries(ORGS).map(([key, o]) => `
<div class="legend-item"><span class="legend-dot" style="background:${o.chartBorder}"></span>${o.name}</div>
`).join('');
}
function linearRegression(points) {
const n = points.length;
if (n < 2) return { slope: 0, intercept: 0 };
let sumX = 0, sumY = 0, sumXY = 0, sumXX = 0;
for (const [x, y] of points) {
sumX += x; sumY += y; sumXY += x * y; sumXX += x * x;
}
const denom = n * sumXX - sumX * sumX;
if (Math.abs(denom) < 1e-10) return { slope: 0, intercept: sumY / n };
const slope = (n * sumXY - sumX * sumY) / denom;
const intercept = (sumY - slope * sumX) / n;
return { slope, intercept };
}
function renderBarChart() {
const canvas = document.getElementById('barChart');
if (!canvas) return;
const ctx = canvas.getContext('2d');
if (chartInstances['bar']) chartInstances['bar'].destroy();
const metricLabel = getMetricLabel(activeBenchmark, true);
const metricUnit = getMetricUnit(activeBenchmark);
const sub = document.getElementById('score-chart-sub');
if (sub) sub.textContent = `Top ${metricLabel.toLowerCase()} scores for the active size filter. Gold outlines mark size category leaders.`;
const models = getChartModels()
.map(m => ({ ...m, score: getMetricValue(m, activeBenchmark) }))
.filter(m => m.score !== null && m.score !== undefined)
.sort((a, b) => b.score - a.score)
.slice(0, 12);
if (!models.length) {
canvas.parentElement.querySelector('h3').textContent = `Top ${metricLabel} Scores - no models with data`;
return;
}
canvas.parentElement.querySelector('h3').textContent = `Top ${models.length} ${metricLabel} Scores`;
chartInstances['bar'] = new Chart(ctx, {
type: 'bar',
data: {
labels: models.map(m => m.name),
datasets: [{
label: metricLabel,
data: models.map(m => m.score),
backgroundColor: models.map(m => ORGS[m.org].chartColor.replace('0.70', '0.82')),
borderColor: getChartModelBorders(models),
borderWidth: 1.5,
borderRadius: 4,
borderSkipped: false
}]
},
options: {
indexAxis: 'y',
responsive: true,
maintainAspectRatio: true,
animation: { duration: 0 },
plugins: {
legend: { display: false },
tooltip: {
...getChartTooltipOptions(),
callbacks: {
label: (item) => {
const model = models[item.dataIndex];
return [`${metricLabel}: ${formatMetricValue(item.raw, activeBenchmark)}`, `Params: ${model.paramsDisplay}`];
}
}
}
},
scales: {
x: {
min: 0,
suggestedMax: Math.max(...models.map(m => m.score)) * 1.08,
title: {
display: true,
text: metricUnit ? `${metricLabel} (${metricUnit})` : metricLabel,
color: '#454548',
font: { family: "'Space Mono', monospace", size: 10 }
},
grid: { color: 'rgba(255,255,255,0.03)' },
ticks: { color: '#454548', font: { family: "'Space Mono', monospace", size: 10 }, callback: v => `${v}${metricUnit}`, maxTicksLimit: 8 },
border: { display: false }
},
y: {
grid: { display: false },
ticks: { color: '#6b6b6e', font: { family: "'DM Sans', system-ui, sans-serif", size: 11 } },
border: { display: false }
}
}
}
});
}
function renderScatter() {
const canvas = document.getElementById('scatterChart');
if (!canvas) return;
const ctx = canvas.getContext('2d');
if (chartInstances['scatter']) chartInstances['scatter'].destroy();
const metricLabel = getMetricLabel(activeBenchmark, true);
const metricUnit = getMetricUnit(activeBenchmark);
const sub = document.getElementById('efficiency-sub');
if (sub) sub.textContent = `${metricLabel} vs parameter count (log scale), 600K+ params only. Shaded zone = above regression line. Gold outlines mark size category leaders.`;
const chartSourceModels = getChartModels().filter(m => m.params && m.params >= 600000);
const models = chartSourceModels
.filter(m => m.params && getMetricValue(m, activeBenchmark) !== null && getMetricValue(m, activeBenchmark) !== undefined)
.map(m => ({
x: Math.log10(m.params),
y: getMetricValue(m, activeBenchmark),
name: m.name,
org: m.org,
params: m.paramsDisplay
}));
const fitModels = models;
if (fitModels.length < 2) {
canvas.parentElement.querySelector('h3').textContent = `${metricLabel} vs Log Parameters - need 2+ models with data`; return;
}
canvas.parentElement.querySelector('h3').textContent = `${metricLabel} vs Log Parameters`;
const points = fitModels.map(m => [m.x, m.y]);
const { slope, intercept } = linearRegression(points);
const residualStd = getStdDev(fitModels.map(m => m.y - (slope * m.x + intercept)));
models.forEach(m => {
const residual = m.y - (slope * m.x + intercept);
m.fitZ = residual / (residualStd || 1);
});
const xMin = Math.min(...models.map(m => m.x));
const xMax = Math.max(...models.map(m => m.x));
const pad = (xMax - xMin) * 0.1;
const xLow = xMin - pad;
const xHigh = xMax + pad;
const yAtLow = slope * xLow + intercept;
const yAtHigh = slope * xHigh + intercept;
const randomBaseline = RANDOM_BASELINES[activeBenchmark] ?? null;
const yValues = [...models.map(m => m.y), yAtLow, yAtHigh];
if (randomBaseline !== null) yValues.push(randomBaseline);
const yLow = Math.min(...yValues);
const yHigh = Math.max(...yValues);
const ySpan = Math.max(yHigh - yLow, 1);
const yPadding = ySpan * 0.08;
const yMin = yLow >= 0 ? Math.max(0, yLow - yPadding) : yLow - yPadding;
const yMax = yHigh + yPadding;
renderLegend();
chartInstances['scatter'] = new Chart(ctx, {
type: 'scatter',
data: {
datasets: [
{
type: 'line',
label: 'Regression',
data: [{ x: xLow, y: yAtLow }, { x: xHigh, y: yAtHigh }],
borderColor: 'rgba(194, 182, 255, 0.45)',
borderWidth: 1.5,
borderDash: [5, 4],
pointRadius: 0,
fill: 'end',
backgroundColor: 'rgba(194, 182, 255, 0.10)',
order: 0
},
{
type: 'line',
label: 'Random',
data: randomBaseline === null ? [] : [{ x: xLow, y: randomBaseline }, { x: xHigh, y: randomBaseline }],
borderColor: 'rgba(107, 107, 110, 0.75)',
borderWidth: 1.25,
borderDash: [2, 4],
pointRadius: 0,
fill: false,
order: 1
},
{
label: 'Models',
data: models,
backgroundColor: models.map(m => ORGS[m.org].chartColor.replace('0.70', '0.90')),
borderColor: getChartModelBorders(models),
borderWidth: 1.5,
pointRadius: 7,
pointHoverRadius: 10,
order: 2
}
]
},
options: {
responsive: true,
maintainAspectRatio: true,
animation: { duration: 0 },
plugins: {
legend: { display: false },
tooltip: {
...getChartTooltipOptions(),
callbacks: {
title: (items) => items[0]?.raw?.name || '',
label: (item) => {
const d = item.raw;
const lines = [
`Params: ${d.params}`,
`${metricLabel}: ${formatMetricValue(d.y, activeBenchmark)}`,
`Fit residual: ${formatSigned(d.fitZ)} std`
];
return lines;
}
}
}
},
scales: {
x: {
type: 'linear',
min: xLow,
max: xHigh,
title: {
display: true,
text: 'Log10(Parameters)',
color: '#454548',
font: { family: "'Space Mono', monospace", size: 10 }
},
grid: { color: 'rgba(255,255,255,0.03)' },
ticks: { color: '#454548', font: { family: "'Space Mono', monospace", size: 10 }, maxTicksLimit: 8 },
border: { display: false }
},
y: {
min: yMin,
max: yMax,
title: {
display: true,
text: metricUnit ? `${metricLabel} (${metricUnit})` : metricLabel,
color: '#454548',
font: { family: "'Space Mono', monospace", size: 10 }
},
grid: { color: 'rgba(255,255,255,0.03)' },
ticks: { color: '#454548', font: { family: "'Space Mono', monospace", size: 10 }, callback: v => `${v.toFixed(0)}${metricUnit}`, maxTicksLimit: 8 },
border: { display: false }
}
}
}
});
}
// ═══ INIT
// ═══════════════════════════════════════════════════════════════
window.addEventListener('DOMContentLoaded', () => {
const leaderboardSearch = document.getElementById('leaderboard-search');
if (leaderboardSearch) {
leaderboardSearch.addEventListener('input', event => {
leaderboardQuery = event.target.value;
renderTable();
});
}
renderFilters();
renderBenchmarkFilters();
initReleaseCarousel();
applyReleaseCardColors();
renderHighlights();
renderTable();
renderBarChart();
renderLegend();
renderScatter();
renderCompare();
renderCompareBar();
initSideNav();
});
</script>
<script src="leaderboard-history.js"></script>
<script>
(function () {
const sizeLabels = { all: 'All sizes', '<250M': 'Under 250M', '<150M': 'Under 150M', '<100M': 'Under 100M', '<50M': 'Under 50M', '<10M': 'Under 10M', '<3M': 'Under 3M' };
function escapeHtml(value) {
return String(value).replace(/[&<>"']/g, function (char) {
return ({ '&': '&amp;', '<': '&lt;', '>': '&gt;', '"': '&quot;', "'": '&#039;' })[char];
});
}
function modelsFor(filter) {
return getFilteredModels(MODELS, filter);
}
function winner(filter, metric) {
if (metric === 'intelligenceIndex') {
const model = getSizeLeader(filter);
return model ? { model, score: getIntelligenceIndex(model) } : null;
}
return modelsFor(filter)
.map(function (model) { return { model: model, score: getMetricValue(model, metric) }; })
.filter(function (entry) { return Number.isFinite(entry.score); })
.sort(function (a, b) { return b.score - a.score; })[0] || null;
}
function card(entry, kicker, metric, active, attribute, value, showSizeLabel = false) {
if (!entry) return '';
const model = entry.model;
const org = ORGS[model.org] || { name: model.org };
const orgColor = org.chartBorder || 'var(--accent)';
const href = model.links && model.links.card ? model.links.card : '#';
const score = '<span class="filter-leader-score">' + escapeHtml(formatMetricValue(entry.score, metric, 2)) + '</span>';
const params = escapeHtml(model.paramsDisplay || model.params) + ' params';
const cardId = modelAnchorId(model) + '-' + (showSizeLabel ? 'highlight' : attribute === 'data-size-filter' ? 'size' : 'benchmark') + '-' + String(value).toLowerCase().replace(/[^a-z0-9._-]+/g, '-').replace(/-+/g, '-').replace(/^-|-$/g, '');
const details = showSizeLabel
? '<div class="highlight-leader-footer"><span class="filter-leader-meta"><span class="highlight-leader-org">' + escapeHtml(org.name) + '</span><span class="highlight-leader-params">' + params + '</span></span><div class="highlight-leader-score">' + score + '<span class="size-score-label">Int Index</span></div></div>'
: score + '<span class="filter-leader-meta">' + escapeHtml(org.name) + ' Β· ' + params + '</span>';
return '<article class="filter-leader-card' + (active ? ' active' : '') + '" id="' + escapeHtml(cardId) + '" role="button" tabindex="0" ' +
attribute + '="' + escapeHtml(value) + '" style="--leader-org-color:' + escapeHtml(orgColor) + '" aria-pressed="' + active + '" aria-label="' + escapeHtml(kicker + ': ' + model.name) + '">' +
'<span class="filter-leader-kicker"><span>' + escapeHtml(kicker) + '</span></span>' +
'<a class="filter-leader-name" href="' + escapeHtml(href) + '" target="_blank" rel="noopener noreferrer">' + escapeHtml(model.name) + '</a>' +
details +
'</article>';
}
function renderFilterLeaders() {
const sizeFilters = SIZE_FILTER_GROUPS[activeSizeMode];
const highlightGrid = document.getElementById('highlight-leader-grid');
const sizeGrid = document.getElementById('size-leader-grid');
const benchmarkGrid = document.getElementById('benchmark-leader-grid');
if (highlightGrid) {
highlightGrid.innerHTML = sizeFilters.map(function (filter) {
return card(winner(filter, 'intelligenceIndex'), sizeLabels[filter], 'intelligenceIndex', activeFilter === filter, 'data-size-filter', filter, true);
}).join('');
}
if (sizeGrid) {
sizeGrid.innerHTML = sizeFilters.map(function (filter) {
return card(winner(filter, 'intelligenceIndex'), 'Best ' + sizeLabels[filter], 'intelligenceIndex', activeFilter === filter, 'data-size-filter', filter);
}).join('');
}
if (benchmarkGrid) {
benchmarkGrid.innerHTML = FILTER_METRICS.map(function (metric) {
return card(winner(activeFilter, metric.key), 'Best ' + metric.label, metric.key, activeBenchmark === metric.key, 'data-benchmark-filter', metric.key);
}).join('');
}
}
function activate(cardElement) {
if (cardElement.hasAttribute('data-size-filter')) {
setFilter(cardElement.getAttribute('data-size-filter'));
if (cardElement.closest('#highlight-leader-grid')) {
document.getElementById('leaderboard').scrollIntoView({ behavior: window.matchMedia('(prefers-reduced-motion: reduce)').matches ? 'auto' : 'smooth' });
}
}
if (cardElement.hasAttribute('data-benchmark-filter')) setBenchmark(cardElement.getAttribute('data-benchmark-filter'));
window.setTimeout(renderFilterLeaders, 0);
}
document.addEventListener('click', function (event) {
const cardElement = event.target.closest('.filter-leader-card');
if (cardElement && !event.target.closest('a')) activate(cardElement);
if (event.target.closest('#filter-bar, #benchmark-filter-bar')) window.setTimeout(renderFilterLeaders, 0);
});
document.addEventListener('keydown', function (event) {
const cardElement = event.target.closest('.filter-leader-card');
if (cardElement && !event.target.closest('a') && (event.key === 'Enter' || event.key === ' ')) {
event.preventDefault();
activate(cardElement);
}
});
const filterObserver = new MutationObserver(function () {
renderFilterLeaders();
});
['filter-bar', 'benchmark-filter-bar'].forEach(function (id) {
const filterBar = document.getElementById(id);
if (filterBar) filterObserver.observe(filterBar, { childList: true });
});
function scrollToModelHash() {
let id;
try { id = decodeURIComponent(window.location.hash.slice(1)); } catch { return; }
if (!id) return;
const highlight = Array.from(document.querySelectorAll('#highlight-leader-grid .filter-leader-card[id]')).find(card => card.id.startsWith(id + '-highlight-'));
const target = highlight || document.getElementById(id);
if (target && (target.matches('.filter-leader-card') || target.matches('#leaderboard-body > tr'))) target.scrollIntoView({ block: 'center' });
}
window.addEventListener('load', function () { renderFilterLeaders(); scrollToModelHash(); });
window.addEventListener('hashchange', scrollToModelHash);
renderFilterLeaders();
})();
</script>
</body>
</html>