GLM-5.2

glm-5.2

Open sourceGLM series

Overview

1M ContextReasoningCode generationAgentic tasks

GLM-5.2 is the flagship base model of the GLM-5 series, covering text generation, deep reasoning and code engineering. Aiming at "staying usable across long tasks", we redesigned the context-compression path and the goal-consistency mechanism so the model reliably retains key constraints across multi-day engineering collaboration, and keeps aligning with user intent through long-chain tasks without context drift. Combined with tiered thinking effort, function calling, MCP tools and structured output, it can drive project-scale software development and complex automation pipelines.

Million-token context

A 1M-token context window loads project-scale codebases and very long documents in a single pass.

Long-horizon agents

Optimized for multi-step, long-running agentic workflows, maintaining engineering context throughout a task.

Tiered thinking modes

Effort levels such as high / xhigh cover everything from fast responses to deep reasoning.

Open weights, self-hostable

Open model weights support private deployment and enterprise customization.

Features

Tool calling✓

Structured output✓

Context caching✓

Batch✓

Web search✓

Streaming✓

Reasoning✓

MCP✓

Prefix completion✕

Fine-tuning✕

Pricing

Input¥8/M tokens

Input (cache hit)¥2/M tokens

Output¥28/M tokens

Rate Limits & Context

Context Window1M

Max Output128K

RPM (requests/min)200

TPM (tokens/min)3M

Built-in Tools

web_search

Responses API

code_interpreter

Responses API

web_extractor

Responses API

API Reference

ENDPOINT

POST https://api.tokenfab.cn/v1/chat/completions

Request Example

curl https://api.tokenfab.cn/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOKENFAB_API_KEY" \
  -d '{
    "model": "glm-5.2",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "请用 Python 实现一个快速排序算法，并附上时间复杂度分析。"}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://api.tokenfab.cn/v1"
)

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "请用 Python 实现一个快速排序算法，并附上时间复杂度分析。"}
    ]
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.TOKENFAB_API_KEY,
  baseURL: "https://api.tokenfab.cn/v1"
});

const response = await client.chat.completions.create({
  model: "glm-5.2",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "请用 Python 实现一个快速排序算法，并附上时间复杂度分析。" }
  ]
});

console.log(response.choices[0].message.content);

Response Example

JSON

{
  "id": "chatcmpl-9d2k7f1",
  "object": "chat.completion",
  "created": 1784950000,
  "model": "glm-5.2",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "快速排序使用分治策略：选定基准值（pivot），将数组分为小于和大于基准的两部分，递归排序后合并。最优 O(n log n)，最差 O(n²)。"
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 15,
    "completion_tokens": 128,
    "total_tokens": 143
  }
}

Request Parameters

Parameter	Type	Required	Description
model	string	✓	Model ID, e.g. glm-5.2 or GLM-5.1
messages	array	✓	List of chat messages, each with role and content
temperature	float	—	Sampling temperature, 0–2, default 0.7
top_p	float	—	Nucleus sampling, 0–1, default 0.9
max_tokens	int	—	Maximum output tokens
stream	bool	—	Whether to stream the response, default false
tools	array	—	Tool definitions for function calling
tool_choice	string	—	Tool choice policy: auto / none / a named function