# zai-org/GLM-5-TEE

> zai-org/GLM-5-FP8

This file is intended for AI coding agents wiring an application to this specific Chutes model.

## Important URLs

- Model page: https://chutes.ai/app/chute/chutes-zai-org-glm-5-tee
- Model llms.txt: https://chutes.ai/app/chute/chutes-zai-org-glm-5-tee/llms.txt
- Model OpenAPI 3.1 spec (this chute, callable): https://chutes.ai/app/chute/chutes-zai-org-glm-5-tee/openapi.json
- Global Chutes llms.txt: https://chutes.ai/llms.txt
- Full Chutes docs export: https://chutes.ai/llms-full.txt
- Management API OpenAPI (account/billing/keys, NOT this model): https://api.chutes.ai/openapi.json

## Model Identifiers

- Name: `zai-org/GLM-5-TEE`
- Chute ID: `e51e818e-fa63-570d-9f68-49d7d1b4d12f`
- Slug: `chutes-zai-org-glm-5-tee`
- Owner: `chutes`

## Authentication

- Use `Authorization: Bearer $CHUTES_API_KEY` for inference calls.
- Send JSON request bodies with `Content-Type: application/json` unless the endpoint documentation says otherwise.
- The request body is FLAT — send the request fields at the top level (no `input_args`/`args` wrapper).
- This model is served on the shared OpenAI-compatible gateway. Set the request `model` field to the exact Name above (`zai-org/GLM-5-TEE`), and use base URL `https://llm.chutes.ai/v1`.
- For base64 media fields, send raw base64 strings in API requests. The web playground may use temporary Blob upload references internally for large files before proxying to Chutes.

## Endpoints

### 1. POST /v1/chat/completions

- Base URL: `https://llm.chutes.ai`
- Output content type: `application/json`
- Streaming: yes

Request fields:

- `seed` (integer, optional) Default: 42.
- `model` (string, optional) Default: "zai-org/GLM-5-FP8".
- `stream` (boolean, optional) Default: true.
- `messages` (array, optional) Default: [{"role":"user","content":""}].
- `max_tokens` (integer, optional) Default: 1024.
- `temperature` (number, optional) Default: 0.7.

Example call:

```bash
curl -X POST "https://llm.chutes.ai/v1/chat/completions" \
  -H "Authorization: Bearer $CHUTES_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "zai-org/GLM-5-TEE",
  "messages": [
    {
      "role": "user",
      "content": "Hello!"
    }
  ],
  "stream": true,
  "max_tokens": 1024,
  "temperature": 0.7
}'
```

### 2. POST /v1/completions

- Base URL: `https://llm.chutes.ai`
- Output content type: `application/json`
- Streaming: yes

Request fields:

- `seed` (integer, optional) Default: 42.
- `model` (string, optional) Default: "zai-org/GLM-5-FP8".
- `prompt` (string, required)
- `stream` (boolean, optional) Default: true.
- `max_tokens` (integer, optional) Default: 1024.
- `temperature` (number, optional) Default: 0.7.

Example call:

```bash
curl -X POST "https://llm.chutes.ai/v1/completions" \
  -H "Authorization: Bearer $CHUTES_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "seed": 42,
  "model": "zai-org/GLM-5-TEE",
  "prompt": "a serene mountain lake at sunset",
  "stream": false,
  "max_tokens": 1024,
  "temperature": 0.7,
  "min_p": 0,
  "top_k": -1,
  "top_p": 1,
  "logprobs": false,
  "ignore_eos": false,
  "min_tokens": 0,
  "top_logprobs": 0,
  "length_penalty": 1,
  "use_beam_search": false,
  "presence_penalty": 0,
  "frequency_penalty": 0,
  "repetition_penalty": 1,
  "skip_special_tokens": true,
  "include_stop_str_in_output": false,
  "spaces_between_special_tokens": true
}'
```

### 3. POST /v1/chat/completions

- Base URL: `https://llm.chutes.ai`
- Output content type: `application/json`

Request fields:

- `seed` (integer, optional) Default: 42.
- `model` (string, optional) Default: "zai-org/GLM-5-FP8".
- `stream` (boolean, optional) Default: false.
- `messages` (array, optional) Default: [{"role":"user","content":""}].
- `max_tokens` (integer, optional) Default: 1024.
- `temperature` (number, optional) Default: 0.7.

Example call:

```bash
curl -X POST "https://llm.chutes.ai/v1/chat/completions" \
  -H "Authorization: Bearer $CHUTES_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "zai-org/GLM-5-TEE",
  "messages": [
    {
      "role": "user",
      "content": "Hello!"
    }
  ],
  "stream": false,
  "max_tokens": 1024,
  "temperature": 0.7
}'
```

### 4. POST /v1/completions

- Base URL: `https://llm.chutes.ai`
- Output content type: `application/json`

Request fields:

- `seed` (integer, optional) Default: 42.
- `model` (string, optional) Default: "zai-org/GLM-5-FP8".
- `prompt` (string, required)
- `stream` (boolean, optional) Default: false.
- `max_tokens` (integer, optional) Default: 1024.
- `temperature` (number, optional) Default: 0.7.

Example call:

```bash
curl -X POST "https://llm.chutes.ai/v1/completions" \
  -H "Authorization: Bearer $CHUTES_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "seed": 42,
  "model": "zai-org/GLM-5-TEE",
  "prompt": "a serene mountain lake at sunset",
  "stream": false,
  "max_tokens": 1024,
  "temperature": 0.7,
  "min_p": 0,
  "top_k": -1,
  "top_p": 1,
  "logprobs": false,
  "ignore_eos": false,
  "min_tokens": 0,
  "top_logprobs": 0,
  "length_penalty": 1,
  "use_beam_search": false,
  "presence_penalty": 0,
  "frequency_penalty": 0,
  "repetition_penalty": 1,
  "skip_special_tokens": true,
  "include_stop_str_in_output": false,
  "spaces_between_special_tokens": true
}'
```

### 5. GET /v1/models

- Base URL: `https://llm.chutes.ai`
- Output content type: `application/json`

Request fields:


Example call:

```bash
curl -X GET "https://llm.chutes.ai/v1/models" \
  -H "Authorization: Bearer $CHUTES_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{}'
```

## Model Facts

- Parameters: 744B total
- Active parameters: 40B per token
- Architecture: Mixture-of-Experts transformer with DeepSeek Sparse Attention (DSA); 78 layers, 256 routed + 1 shared experts, 8 routed experts per token
- Context length: 202,752 tokens
- Modalities: text in → text out
- Precision: FP8 (as served on Chutes; upstream weights BF16)
- License: MIT
- Released: 2026-02
- Experts: 256 routed + 1 shared, 8 routed active per token
- Layers / hidden size: 78 / 6144
- Pre-training data: 28.5T tokens
- Vocabulary: 154,880 tokens
- SWE-bench Verified: 77.8 (model card)
- Tau-2 Bench: 89.7 (model card)
- Hugging Face: https://huggingface.co/zai-org/GLM-5

## Recommended Parameters

- `temperature`: 1.0 (generation_config.json default; also used for the card's reasoning evaluations)
- `top_p`: 0.95 (generation_config.json default)
- `temperature`: 0.7 (used by the model card for SWE-bench coding runs (with top_p 0.95))

## Best For

- Complex systems engineering and multi-file coding tasks
- Long-horizon agentic loops with hundreds of tool calls
- Repository-scale software tasks (77.8 SWE-bench Verified on the model card)
- Web research agents (62.0 BrowseComp, 75.9 with context management)
- Tool-use workflows measured by tau-2 Bench (89.7) and MCP-Atlas

Not ideal for:

- Image, audio, or video inputs: text-only model
- Latency-critical short completions where a 40B-active MoE is oversized
- Contexts beyond ~200K tokens: use GLM-5.2 for 1M-token windows

## FAQ

### What context window does GLM-5 support on Chutes?

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.

### Does GLM-5 support function calling and tool use?

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.

### What does the TEE suffix mean?

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.

### Can I use GLM-5 commercially?

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

### How do I call it from the OpenAI SDK?

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.

### What sampling settings should I use?

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.

### Is this the full-precision model?

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.


## Model Guide & Sources

- Full model guide: https://chutes.ai/docs/models/chutes-zai-org-glm-5-tee
- Source: https://huggingface.co/zai-org/GLM-5
- Source: https://huggingface.co/zai-org/GLM-5-FP8
- Source: https://chutes.ai/app/chute/chutes-zai-org-glm-5-tee/llms.txt

## Agent Integration Checklist

- Pick the endpoint path that matches the desired task.
- Set `CHUTES_API_KEY` in the server-side environment only.
- Validate required fields before sending requests.
- For media models, keep file upload, base64 conversion, and output preview/download handling explicit in the app UI.