GLM-5.1

About

What GLM-5.1 is

GLM-5.1-FP8 👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/wechat.png" target="blank"WeChat</a or <a href="https://discord.gg/QR7SARHRxK" target="blank"Discord</a community. 📖 Check out the GLM-5.1 <a href="https://z.ai/blog/glm-5.1" target="blank"blog</a and GLM-5 <a href="https://arxiv.org/abs/2602.15763" target="blank"Technical report</a. 📍 Use GLM-5.1 API services on <a href="https://docs.z.ai/guides/llm/glm-5.1"Z.ai API Platform. </a 🔜 <a href="https://chat.z.ai"GLM-5.1</a will be available on chat.z.ai in the coming days. [<a href="https://huggingface.co/papers/2602.15763" target="blank"Paper</a] [<a href="https://github.com/zai-org/GLM-5" target="blank"GitHub</a] 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 Serve GLM-5.1 Locally The following open-source frameworks support local deployment of GLM-5.1: - SGLang (v0.5.10+) — see cookbook - vLLM (v0.19.0+) — see recipes - xLLM (v0.8.0+) — see example - Transformers (v0.5.3+) — see transformers docs - KTransformers (v0.5.3+) — see tutorial Citation If you find GLM-5.1 or GLM-5 useful in your research, please cite our te

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys glm-5-1 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (glm-5-1 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"glm-5-1","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

Explore

More language models