GLM-5.2 Official Documentation: Overview and API Quick Start (docs.z.ai)

One-sentence takeaway

The official standard integration configuration for GLM-5.2 is: model name glm-5.2, a 1M context window / 128K maximum output, thinking.type: enabled + reasoning_effort: max, and temperature: 1.0. You can copy the curl / Python examples directly to make your first call and review the typical use cases identified by the official documentation.

Use cases

• Suitable tasks: first-time GLM-5.2 API integration; understanding and modifying an entire project-level codebase; long-horizon tasks; the complete development workflow from requirements to a deployable product; research reproduction (paper → runnable project); and official example scenarios such as WeChat Mini Program migration, mini-game development, on-device debugging loops, and Code-to-Video loops.

• Unsuitable tasks: tasks requiring visual/multimodal input (GLM-5.2 supports text input and text output only); scenarios where the official API has not made strict structured output with json_schema available (see the migration guide).

• Applicable model versions: GLM-5.2 (API model ID glm-5.2; GLM-5.2 in the GLM Coding Plan; GLM-5.2[1m] can be used in Claude Code to enable a 1M context window).

• Applicable clients, Agents, or APIs: the official Z.ai API (https://api.z.ai/api/paas/v4/chat/completions, OpenAI-compatible protocol); the official Python/Java SDKs and OpenAI Python SDK; and the GLM Coding Plan (ZCode, Claude Code, OpenCode, and others).

• Recommended reasoning tier and parameters: thinking: {"type": "enabled"} (thinking is enabled by default); reasoning_effort supports high / max (the default is max, and the official recommendation for coding tasks is max); temperature defaults to 1.0 and top_p defaults to 0.95; max_tokens supports up to 128K; maximum context length is 1M.

Ready-to-use content

At-a-glance specs (official)

Typical use cases identified by the official documentation

• Taking over an entire project-level codebase: put the entire project into the model at once for it to understand

• Long-horizon refactoring: have the model run real engineering tasks end to end

• Production-grade standard stress testing: test whether the model can hold up under demanding engineering constraints

• Closed-loop debugging on mobile devices: from code implementation to device verification

• WeChat Mini Program development: migrate a web application to a Mini Program

• Mini-game development: from gameplay rules to a playable loop

• Research reproduction: from papers and data to a runnable engineering project

• Code-to-Video loop: from a natural-language idea to a demonstrable video

Basic call (official Quick Start, reproduced verbatim)

curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-api-key" \
-d '{
  "model": "glm-5.2",
  "messages": [
    {
      "role": "system",
      "content": "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks."
    },
    {
      "role": "user",
      "content": "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack."
    }
  ],
  "thinking": {
    "type": "enabled"
  },
  "reasoning_effort": "max",
  "max_tokens": 4096,
  "temperature": 1.0
}'

Streaming call (official Quick Start, reproduced verbatim)

curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-api-key" \
-d '{
  "model": "glm-5.2",
  "messages": [
    {
      "role": "system",
      "content": "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks."
    },
    {
      "role": "user",
      "content": "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack."
    }
  ],
  "thinking": {
    "type": "enabled"
  },
  "reasoning_effort": "max",
  "stream": true,
  "max_tokens": 4096,
  "temperature": 1.0
}'

Official promotional points (relevant to usage decisions)

• "Solid 1M context": the 1M context window was specially trained for coding-agent scenarios (large-scale implementation, automated research, performance optimization, and complex debugging), rather than merely being able to "accept more tokens."

• It ranks highest among open-source models on FrontierSWE, PostTrainBench, and SWE-Marathon; on FrontierSWE, it trails Claude Opus 4.8 by only about 1%.

• Standard coding benchmarks: Terminal-Bench 2.1 is 81.0 (GLM-5.1 is 63.5; the official documentation page says 62.0), and SWE-bench Pro is 62.1 (GLM-5.1 is 58.4). The gap with Opus 4.8 (85.0) has narrowed, and GLM-5.2 leads Gemini 3.1 Pro.

• Before release, it was made available early to GLM Coding Plan users. Developer feedback focused on stronger project-level context handling, more stable execution of long tasks, better adherence to engineering standards, and stronger client-side and mobile engineering capabilities.

Notes and limitations

• This page describes the vendor's self-reported capabilities and configuration; the benchmark scores use the vendor's methodology. See the evaluation directory for independent third-party verification (NIST CAISI, Arena.ai, and others).

• The documentation gives two Terminal-Bench 2.1 comparisons: 81.0 vs. 62.0 in one place, and 81.0 vs. 63.5 (GLM-5.1) in another. The official blog reports 81.0 vs. 63.5; use the blog figure as the reference.