Question 1

How is GLM-5.1 different from GLM-5?

Accepted Answer

Same architecture and size, retrained for agentic engineering. It reaches 58.4 on SWE-Bench Pro (state-of-the-art at release) and improves sharply on NL2Repo (42.7 vs 35.9) and Terminal-Bench 2.0 (63.5 vs 56.2). Most importantly, it stays productive over much longer autonomous sessions instead of plateauing early.

Question 2

What context window does GLM-5.1 support?

Accepted Answer

The config.json sets max_position_embeddings to 202,752 tokens, roughly 200K. The chute's default max_tokens is 1,024, so raise it for long outputs.

Question 3

Does GLM-5.1 support function calling and tool use?

Accepted Answer

Yes. Long-horizon tool use is the model's design focus: Z.ai reports it sustaining thousands of tool calls per session, and it scores 71.8 on MCP-Atlas and 40.7 on Tool-Decathlon. Pass OpenAI-style tools in your chat completion request.

Question 4

What does the TEE suffix mean?

Accepted Answer

TEE stands for Trusted Execution Environment. Inference runs inside attested confidential-compute hardware, so your prompts and the model's outputs are processed within a hardware-isolated enclave rather than on an open host.

Question 5

Can I use GLM-5.1 commercially?

Accepted Answer

Yes. GLM-5.1 is released under the MIT license, which permits commercial use, modification, and redistribution.

Question 6

How do I call it from the OpenAI SDK?

Accepted Answer

Set base_url to https://llm.chutes.ai/v1 with your Chutes API key and model to zai-org/GLM-5.1-TEE. Chat completions, raw completions, and streaming work through the standard SDK methods.

Question 7

What sampling settings should I use?

Accepted Answer

The upstream generation_config.json defaults to temperature 1.0 and top_p 0.95. The chute's own default temperature is 0.7 with max_tokens 1024; for agent workloads, raise max_tokens well above the default.

GLM 5.1

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GLM 5.1

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