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On our own GPUs

GLM-5.2 API

750B sparse MoE, a 1M window, MIT licensed — tuned hard for software engineering.

  • Context

    1M

  • Max output

    131K

  • Serving precision

    BF16 / FP8

  • Licence

    MIT

GLM-5.2 is Z.ai’s open-weight flagship, released in June 2026. Roughly 750B parameters in a sparse mixture-of-experts design with about 40B active per token, paired with a new IndexShare sparse-attention scheme that keeps 1M-context inference affordable. The window is 1 million tokens and a single response can return up to 131,072. Weights ship in both BF16 and FP8 under an MIT licence, and the tuning leans heavily toward software engineering, multi-step reasoning and tool use.

Open weights, deployed in facilities we build out and lease directly, operated by us. We resell nobody else’s account quota.

Specifications

Model ID
glm-5.2
Published by
Z.ai (Zhipu AI)
How it is served
On our own GPUs
Licence
MIT
Total parameters
750B MoE
Active parameters
40B
Context
1M
Max output
131K
Serving precision
BF16 / FP8
Input modalities
Text
Released
2026-06-13

What it is good at

131K tokens in a single response
Enough to emit an entire technical design, a whole module, or a full translation in one shot. For generation-heavy work, every stitch you remove is one fewer place for the output to break.
MIT — no legal detour
Commercial use, redistribution and private fine-tuning carry no extra licensing burden. That is also precisely what lets us run it in our own facilities and publish the weight version we serve.
Strong on both Chinese and code
Tuned for Chinese-language semantics and code generation at the same time, with steady instruction following. A cost-effective default for products that serve Chinese users and still need structured output.

Where it fits

  • Primary product traffic: chat, summarisation, structured extraction
  • One-shot generation of long documents, designs and code
  • Multi-step tool calling and agent orchestration
  • Deployments where a permissive licence is a procurement requirement

Three steps

Change one line of base_url, put the model ID below in the `model` field, and leave the rest of your code alone.

model: "glm-5.2"

curl https://api.nexaix.net/v1/chat/completions \
  -H "Authorization: Bearer $NEXAIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.2",
    "stream": true,
    "messages": [
      { "role": "user", "content": "Summarise this contract." }
    ]
  }'

About this model

Do you serve GLM-5.2 in BF16 or FP8?

Both weight sets are published by the vendor. The precision we currently serve is stated on this page, and changes go through the changelog. Pin a prompt set and re-run it at different times — the distribution should hold.

What does the MIT licence mean for me as an API user?

Nothing extra on your side. It matters on the supply side: we can legally run these weights on our own hardware, so we never resell anyone else’s account quota and your service does not die when somebody else gets banned.

What changed since GLM-5.1?

Most visibly the context window went from 200K to 1M and the single-response output ceiling rose sharply; beyond that, the tuning focused on software engineering and multi-step reasoning. Run your own eval for what it means on your tasks.

Also worth a look

Run your own eval on it

Take the trial credit, run the evals you already have against this model, and then decide whether it gets your production traffic.