# zai-org/GLM-5.2-TEE

> nvidia/GLM-5.2-NVFP4

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-5-2-tee
- Model llms.txt: https://chutes.ai/app/chute/chutes-zai-org-glm-5-2-tee/llms.txt
- Model OpenAPI 3.1 spec (this chute, callable): https://chutes.ai/app/chute/chutes-zai-org-glm-5-2-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-5.2-TEE`
- Chute ID: `08901219-159f-55a7-87cf-9d0d02744668`
- Slug: `chutes-zai-org-glm-5-2-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-5.2-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: "nvidia/GLM-5.2-NVFP4".
- `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-5.2-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: "nvidia/GLM-5.2-NVFP4".
- `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-5.2-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: "nvidia/GLM-5.2-NVFP4".
- `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-5.2-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: "nvidia/GLM-5.2-NVFP4".
- `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-5.2-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: 753B total (safetensors)
- Architecture: Mixture-of-Experts transformer with sparse attention and IndexShare (indexer reused across every four sparse attention layers); 78 layers, 256 routed + 1 shared experts, 8 routed experts per token
- Context length: 1,048,576 tokens
- Modalities: text in → text out
- Precision: NVFP4 (as served on Chutes, via NVIDIA ModelOpt; upstream weights BF16)
- License: MIT
- Released: 2026-06
- Experts: 256 routed + 1 shared, 8 routed active per token
- Layers / hidden size: 78 / 6144
- Thinking effort: Multiple effort levels to balance performance and latency
- Terminal Bench 2.1 (Terminus-2): 81.0 (model card)
- SWE-bench Pro: 62.1 (model card)
- AIME 2026: 99.2 (model card)
- Hugging Face: https://huggingface.co/zai-org/GLM-5.2

## Recommended Parameters

- `temperature`: 1.0 (generation_config.json default; also used for the card's reasoning evaluations with top_p 0.95)
- `top_p`: 0.95 (generation_config.json default)

## Best For

- Million-token context workloads: whole-repository analysis, long document sets, multi-day agent transcripts
- Long-horizon coding agents (81.0 Terminal Bench 2.1, 62.1 SWE-bench Pro, 74.4 FrontierSWE dominance)
- Competition-grade math and reasoning (99.2 AIME 2026, 91.0 IMOAnswerBench, 40.5 HLE)
- Agentic tool use at scale (76.8 MCP-Atlas public set)
- Latency-sensitive deployments that still need frontier quality, via flexible thinking effort and 4-bit serving

Not ideal for:

- Image, audio, or video inputs: text-only model
- Workloads that must match full-precision outputs bit-for-bit: this chute serves a 4-bit NVFP4 quantization

## FAQ

### What context window does GLM-5.2 support?

The config.json sets max_position_embeddings to 1,048,576 tokens: a true 1M-token window. Z.ai's long-context evaluations (FrontierSWE, SWE-Marathon, PostTrainBench) ran at the full 1M length. The chute's default max_tokens is 1,024, so raise it for long outputs.

### How is GLM-5.2 different from GLM-5.1?

Three things: a 1M-token context (up from ~200K), stronger coding with selectable thinking effort levels, and an IndexShare architecture change that cuts per-token FLOPs 2.9x at 1M context. Benchmark deltas are large: Terminal Bench 81.0 vs 63.5, SWE-bench Pro 62.1 vs 58.4, HLE 40.5 vs 31.0.

### What does NVFP4 mean for output quality?

This chute serves nvidia/GLM-5.2-NVFP4, a 4-bit quantization produced with NVIDIA's ModelOpt (TensorRT Model Optimizer). It substantially cuts memory and cost per token; outputs will not be bit-identical to the BF16 checkpoint, but the quantized release is published by NVIDIA specifically for production deployment.

### What does the TEE suffix mean?

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.

### Can I use GLM-5.2 commercially?

Yes. GLM-5.2 is MIT-licensed, which the card highlights as pure open with no regional limits. The NVFP4 quantization is likewise governed by the MIT license and marked ready for commercial use.

### Does it support function calling and tool use?

Yes. GLM-5.2 is built for agentic work, scoring 76.8 on MCP-Atlas (public set) and 48.2 on Tool-Decathlon. Send OpenAI-style tools in your chat completion request.

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

Set base_url to https://llm.chutes.ai/v1 with your Chutes API key and model to zai-org/GLM-5.2-TEE. Chat completions, raw completions, and streaming 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 evaluations. Their coding-agent runs (SWE-Bench Pro, Terminal-Bench) used temperature 1.0 with top_p 1.0. The chute's own default temperature is 0.7.


## Model Guide & Sources

- Full model guide: https://chutes.ai/docs/models/chutes-zai-org-glm-5-2-tee
- Source: https://huggingface.co/zai-org/GLM-5.2
- Source: https://huggingface.co/nvidia/GLM-5.2-NVFP4
- Source: https://chutes.ai/app/chute/chutes-zai-org-glm-5-2-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.