Model reference · open weights
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.
Summary of the zai-org/GLM-5.1-FP8 model card, 2026-10-01
What it is
| Released by | zai-org |
|---|---|
| Released | 2026-04-03 |
| Parameters | 753.9B |
| VRAM | 756 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … 8× A100 80 GB 14 smaller cards | — | — | — | |
| 8× H200 141 GB tensor parallel | 39 | 9 | all 198K | 138 GB a card |
| 8× B200 180 GB tensor parallel | 78 | 19 | all 198K | 176 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 759 GB | 762 GB |
| 5 | 762 GB | 775 GB |
| 8 | 765 GB | 785 GB |
| 16 | 772 GB | 811 GB |
| 32 | 785 GB | 863 GB |
| 64 | 811 GB | 968 GB |
One card, with vLLM's small-card settings.
Builds
From 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]
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.
| 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 |
The following open-source frameworks support local deployment of GLM-5.1:
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.