Question 1

What context window does GLM-5 support on Chutes?

Accepted Answer

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

Question 2

Does GLM-5 support function calling and tool use?

Accepted Answer

Yes. GLM-5 is trained for agentic tool use (89.7 on tau-2 Bench, 67.8 on MCP-Atlas public set per the model card), and upstream serving stacks run it with a tool-call parser and auto tool choice. Send OpenAI-style tools in your chat completion request.

Question 3

What does the TEE suffix mean?

Accepted Answer

TEE stands for Trusted Execution Environment. This deployment runs inference 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 4

Can I use GLM-5 commercially?

Accepted Answer

Yes. GLM-5 is released under the MIT license, which permits commercial use, modification, and redistribution without regional restrictions.

Question 5

How do I call it from the OpenAI SDK?

Accepted Answer

Point the client at base_url https://llm.chutes.ai/v1 with your Chutes API key, and set model to zai-org/GLM-5-TEE. Chat completions, raw completions, and streaming all work through the standard SDK methods.

Question 6

What sampling settings should I use?

Accepted Answer

The upstream generation_config.json defaults to temperature 1.0 and top_p 0.95, which Z.ai also used for reasoning benchmarks. For coding-agent runs the model card used temperature 0.7 with top_p 0.95. The chute's own default temperature is 0.7.

Question 7

Is this the full-precision model?

Accepted Answer

The chute serves zai-org/GLM-5-FP8, Z.ai's official FP8-quantized build of GLM-5 (e4m3 weights with dynamic activation scaling). The original checkpoint is BF16; FP8 substantially reduces memory and cost with minimal quality impact.

GLM 5

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GLM 5

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