Model reference · open weights

GLM-5.1

LLMs zai-org Text gen 2 builds Open weights 396k dl/mo

GLM-5.1 is an open-weight language model from zai-org. GLM-5.1-FP8 (FP8) weighs 756 GB; the smallest configuration that runs it is 8× H200 141 GB.

  • GLM-5.1 is a 753.9B parameter text-generation model developed by zai-org for agentic engineering and coding tasks.
  • It supports English and Chinese with a context length of 202,752 tokens and is released under the MIT license.
  • The model is designed to sustain optimization over long sessions and is available for local deployment via frameworks such as SGLang, vLLM, and Transformers.

Summary of the zai-org/GLM-5.1-FP8 model card, 2026-10-01

What it is

Released byzai-org
Released2026-04-03
Parameters753.9B
VRAM756 GB for the weights

What it runs on

Memory and cards for GLM-5.1-FP8 (FP8)

756 GBweights, file size
100 MBcache per 1K tokens
2.2 GBruntime overhead, at least
202,752 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … 8× A100 80 GB
14 smaller cards
———
8× H200 141 GB
tensor parallel
399all 198K138 GB a card
8× B200 180 GB
tensor parallel
7819all 198K176 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
1759 GB762 GB
5762 GB775 GB
8765 GB785 GB
16772 GB811 GB
32785 GB863 GB
64811 GB968 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
GLM-5.1-FP8 (above) ↗ 753.9BFP8 756 GB8× H200 141 GB
GLM-5.1 ↗ 753.9BBF16 1508 GBnone of the reference configurations

From the model card

What zai-org says about GLM-5.1

Read the model card

📖 Check out the GLM-5.1 blog and GLM-5 Technical report. 📍 Use GLM-5.1 API services on Z.ai API Platform. 🔜 GLM-5.1 will be available on chat.z.ai in the coming days.

[Paper]
[GitHub]

Introduction

GLM-5.1 is our next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).

But the most meaningful leap goes beyond first-pass performance. Previous models—including GLM-5—tend to exhaust their repertoire early: they apply familiar techniques for quick initial gains, then plateau. Giving them more time doesn't help.

GLM-5.1, by contrast, is built to stay effective on agentic tasks over much longer horizons. We've found that the model handles ambiguous problems with better judgment and stays productive over longer sessions. It breaks complex problems down, runs experiments, reads results, and identifies blockers with real precision. By revisiting its reasoning and revising its strategy through repeated iteration, GLM-5.1 sustains optimization over hundreds of rounds and thousands of tool calls. The longer it runs, the better the result.

Benchmark

GLM-5.1GLM-5Qwen3.6-PlusMinimax M2.7DeepSeek-V3.2Kimi K2.5Claude Opus 4.6Gemini 3.1 ProGPT-5.4
HLE31.030.528.828.025.131.536.745.039.8
HLE (w/ Tools)52.350.450.6-40.851.853.1*51.4*52.1*
AIME 202695.395.495.189.895.194.595.698.298.7
HMMT Nov. 202594.096.994.681.090.291.196.394.895.8
HMMT Feb. 202682.682.887.872.779.981.384.387.391.8
IMOAnswerBench83.882.583.866.378.381.875.381.091.4
GPQA-Diamond86.286.090.487.082.487.691.394.392.0
SWE-Bench Pro58.455.156.656.2-53.857.354.257.7
NL2Repo42.735.937.939.8-32.049.833.441.3
Terminal-Bench 2.0 (Terminus-2)63.556.261.6-39.350.865.468.5-
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)
CyberGym68.748.3--17.341.366.638.866.3
BrowseComp68.062.0--51.460.6---
BrowseComp (w/ Context Manage)79.375.9--67.674.984.085.982.7
τ³-Bench70.669.270.767.669.266.072.467.172.9
MCP-Atlas (Public Set)71.869.274.148.862.263.873.869.267.2
Tool-Decathlon40.738.039.846.335.227.847.248.854.6
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:

  • SGLang (v0.5.10+) — see cookbook
  • [vLLM](https://github.com/vllm-project/v

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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