Instructions to use zai-org/GLM-5.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-5.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zai-org/GLM-5.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5.1")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5.1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-5.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-5.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-5.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-5.1

SGLang

How to use zai-org/GLM-5.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zai-org/GLM-5.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-5.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zai-org/GLM-5.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-5.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-5.1 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-5.1
```

zRzRzRzRzRzRzR commited on Apr 12

Commit

bef0214

1 Parent(s): 0e12244

update CyberGym score for Gemini 3.1 Pro and GPT-5.4

Browse files

Files changed (1) hide show

README.md +19 -19

README.md CHANGED Viewed

@@ -40,25 +40,25 @@ GLM-5.1, by contrast, is built to stay effective on agentic tasks over much long
 ## Benchmark
 |                                            | GLM-5.1            | GLM-5              | Qwen3.6-Plus | Minimax M2.7        | DeepSeek-V3.2       | Kimi K2.5  | Claude Opus 4.6 | Gemini 3.1 Pro | GPT-5.4          |
-| ------------------------------------------ | ------------------ | ------------------- | ------------ | -------------------- | -------------------- | ---------- | --------------- | -------------- | ---------------- |
-| HLE                                        | 31.0               | 30.5                | 28.8         | 28.0                 | 25.1                 | 31.5       | 36.7            | **45.0**       | 39.8             |
-| HLE (w/ Tools)                             | 52.3               | 50.4                | 50.6         | -                    | 40.8                 | 51.8       | **53.1***       | 51.4*          | 52.1*            |
-| AIME 2026                                  | 95.3               | 95.4                | 95.1         | 89.8                 | 95.1                 | 94.5       | 95.6            | 98.2           | **98.7**         |
-| HMMT Nov. 2025                             | 94.0               | **96.9**            | 94.6         | 81.0                 | 90.2                 | 91.1       | 96.3            | 94.8           | 95.8             |
-| HMMT Feb. 2026                             | 82.6               | 82.8                | 87.8         | 72.7                 | 79.9                 | 81.3       | 84.3            | 87.3           | **91.8**         |
-| IMOAnswerBench                             | 83.8               | 82.5                | 83.8         | 66.3                 | 78.3                 | 81.8       | 75.3            | 81.0           | **91.4**         |
-| GPQA-Diamond                               | 86.2               | 86.0                | 90.4         | 87.0                 | 82.4                 | 87.6       | 91.3            | **94.3**       | 92.0             |
-| SWE-Bench Pro                              | **58.4**           | 55.1                | 56.6         | 56.2                 | -                    | 53.8       | 57.3            | 54.2           | 57.7             |
-| NL2Repo                                    | 42.7               | 35.9                | 37.9         | 39.8                 | -                    | 32.0       | **49.8**        | 33.4           | 41.3             |
-| Terminal-Bench 2.0 (Terminus-2)            | 63.5               | 56.2                | 61.6         | -                    | 39.3                 | 50.8       | 65.4            | **68.5**       | -                |
-| Terminal-Bench 2.0 (Best self-reported)    | 69.0 (Claude Code) | 56.2 (Claude Code)  | -            | 57.0 (Claude Code)   | 46.4 (Claude Code)   | -          | -               | -              | **75.1** (Codex) |
-| CyberGym                                   | **68.7**           | 48.3                | -            | -                    | 17.3                 | 41.3       | 66.6            | -              | -                |
-| BrowseComp                                 | **68.0**           | 62.0                | -            | -                    | 51.4                 | 60.6       | -               | -              | -                |
-| BrowseComp (w/ Context Manage)             | 79.3               | 75.9                | -            | -                    | 67.6                 | 74.9       | 84.0            | **85.9**       | 82.7             |
-| τ³-Bench                                   | 70.6               | 69.2                | 70.7         | 67.6                 | 69.2                 | 66.0       | 72.4            | 67.1           | **72.9**         |
-| MCP-Atlas (Public Set)                     | 71.8               | 69.2                | **74.1**     | 48.8                 | 62.2                 | 63.8       | 73.8            | 69.2           | 67.2             |
-| Tool-Decathlon                             | 40.7               | 38.0                | 39.8         | 46.3                 | 35.2                 | 27.8       | 47.2            | 48.8           | **54.6**         |
-| Vending Bench 2                            | $5,634.41          | $4,432.12           | $5,114.87    | -                    | $1,034.00            | $1,198.46  | **$8,017.59**   | $911.21        | $6,144.18        |
 ## Serve GLM-5.1 Locally
 The following open-source frameworks support local deployment of GLM-5.1:

 ## Benchmark
 |                                            | GLM-5.1            | GLM-5              | Qwen3.6-Plus | Minimax M2.7        | DeepSeek-V3.2       | Kimi K2.5  | Claude Opus 4.6 | Gemini 3.1 Pro | GPT-5.4          |
+| ------------------------------------------ | ------------------ | ------------------- | ------------ | -------------------- | -------------------- | ---------- | --------------- | ------------- | ---------------- |
+| HLE                                        | 31.0               | 30.5                | 28.8         | 28.0                 | 25.1                 | 31.5       | 36.7            | **45.0**      | 39.8             |
+| HLE (w/ Tools)                             | 52.3               | 50.4                | 50.6         | -                    | 40.8                 | 51.8       | **53.1***       | 51.4*         | 52.1*            |
+| AIME 2026                                  | 95.3               | 95.4                | 95.1         | 89.8                 | 95.1                 | 94.5       | 95.6            | 98.2          | **98.7**         |
+| HMMT Nov. 2025                             | 94.0               | **96.9**            | 94.6         | 81.0                 | 90.2                 | 91.1       | 96.3            | 94.8          | 95.8             |
+| HMMT Feb. 2026                             | 82.6               | 82.8                | 87.8         | 72.7                 | 79.9                 | 81.3       | 84.3            | 87.3          | **91.8**         |
+| IMOAnswerBench                             | 83.8               | 82.5                | 83.8         | 66.3                 | 78.3                 | 81.8       | 75.3            | 81.0          | **91.4**         |
+| GPQA-Diamond                               | 86.2               | 86.0                | 90.4         | 87.0                 | 82.4                 | 87.6       | 91.3            | **94.3**      | 92.0             |
+| SWE-Bench Pro                              | **58.4**           | 55.1                | 56.6         | 56.2                 | -                    | 53.8       | 57.3            | 54.2          | 57.7             |
+| NL2Repo                                    | 42.7               | 35.9                | 37.9         | 39.8                 | -                    | 32.0       | **49.8**        | 33.4          | 41.3             |
+| Terminal-Bench 2.0 (Terminus-2)            | 63.5               | 56.2                | 61.6         | -                    | 39.3                 | 50.8       | 65.4            | **68.5**      | -                |
+| Terminal-Bench 2.0 (Best self-reported)    | 69.0 (Claude Code) | 56.2 (Claude Code)  | -            | 57.0 (Claude Code)   | 46.4 (Claude Code)   | -          | -               | -             | **75.1** (Codex) |
+| CyberGym                                   | **68.7**           | 48.3                | -            | -                    | 17.3                 | 41.3       | 66.6            | 38.8          | 66.3             |
+| BrowseComp                                 | **68.0**           | 62.0                | -            | -                    | 51.4                 | 60.6       | -               | -             | -                |
+| BrowseComp (w/ Context Manage)             | 79.3               | 75.9                | -            | -                    | 67.6                 | 74.9       | 84.0            | **85.9**      | 82.7             |
+| τ³-Bench                                   | 70.6               | 69.2                | 70.7         | 67.6                 | 69.2                 | 66.0       | 72.4            | 67.1          | **72.9**         |
+| MCP-Atlas (Public Set)                     | 71.8               | 69.2                | **74.1**     | 48.8                 | 62.2                 | 63.8       | 73.8            | 69.2          | 67.2             |
+| Tool-Decathlon                             | 40.7               | 38.0                | 39.8         | 46.3                 | 35.2                 | 27.8       | 47.2            | 48.8          | **54.6**         |
+| Vending Bench 2                            | $5,634.41          | $4,432.12           | $5,114.87    | -                    | $1,034.00            | $1,198.46  | **$8,017.59**   | $911.21       | $6,144.18        |
 ## Serve GLM-5.1 Locally
 The following open-source frameworks support local deployment of GLM-5.1: