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GLM-5.2

Flagship open reasoning model for agentic software engineering with 256K context window

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About model

GLM-5.2 is Z.ai's newest flagship model for agentic coding and long-horizon software engineering, built on the GLM-5 Mixture-of-Experts backbone — 744B total parameters with 40B active per token. The release also adds two thinking-effort levels, letting developers trade compute depth against throughput depending on task complexity. GLM-5.2 ships under the MIT License with open weights. According to Z.ai, the model delivers powerful coding capabilities and continued strengths in long-horizon agentic tasks.

Token Context Window

256K

Usable 256K-token context window with a 131,072-token output cap

Total Parameters (40B Activated)

744B

Mixture-of-Experts backbone optimized for coding agents

Thinking Effort

Configurable

Two thinking-effort levels exposed directly at the API level

Model key capabilities
  • Long-Context Agentic Coding: A usable 256K-token context window — 5× larger than GLM-5.1's ~200K limit — with a 131,072-token output cap. According to Z.ai, the model tracks cross-file dependencies across full repositories in a single pass, enabling whole-repo refactors without repeated context retrieval.
  • Configurable Thinking Effort: Two thinking-effort levels exposed at the API level let developers dial between lighter inference for fast tasks and deeper reasoning for complex long-horizon sessions — a tradeoff between throughput and reasoning depth.
  • Drop-in Agent Compatibility: Day-one support for eight coding agents including Claude Code, Cline, Roo Code, Goose, and OpenCode via an OpenAI-compatible and Anthropic Messages API — a config-level swap with no framework changes required.

  • API usage

    • cURL
    • Python
    • Typescript

    Endpoint:

    zai-org/GLM-5.2

    curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai-org/GLM-5.2",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
}'

    from together import Together

client = Together()

response = client.chat.completions.create(
  model="zai-org/GLM-5.2",
  messages=[
    {
      "role": "user",
      "content": "What are some fun things to do in New York?"
    }
  ]
)
print(response.choices[0].message.content)

    import Together from 'together-ai';
const together = new Together();

const completion = await together.chat.completions.create({
  model: 'zai-org/GLM-5.2',
  messages: [
    {
      role: 'user',
      content: 'What are some fun things to do in New York?'
     }
  ],
});

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

  • Model card

    Architecture Overview:
    • 744B total parameter MoE architecture with 40B parameters activated per token
    • 256K context window with 131,072 max output tokens
    • Supports configurable thinking modes (two effort levels) for step-by-step reasoning
    • Open-weights distribution under the MIT License

    Training Methodology:
    • Pre-trained and instruction-tuned entirely on Huawei Ascend 910B hardware under the MindSpore framework
    • Post-training optimizations targeting repo-scale dependencies and extended plan-then-execute loops

    Performance Characteristics:
    • Flagship reasoning performance built specifically for long-horizon orchestration
    • Native framework compatibility with multiple coding agent workflows
    • No official benchmarks were published at launch; performance metrics will be updated as they are verified

    ‍

  • Prompting

    Together AI API Access:
    • Access GLM-5.2 via Together AI APIs using the endpoint zai-org/GLM-5.2
    • Authenticate using your Together AI API key in request headers
    • Supports configurable thinking effort parameters
    • Available on Together AI serverless infrastructure

    ‍

  • Applications & use cases

    Repository-Scale Engineering:
    • Complete multi-file refactoring, code migration, and complex system updates in a single pass
    • Navigation of large codebases without needing vector-search retrieval chunking

    Autonomous Technical Workflows:
    • Extended plan-then-execute cycles utilizing up to 131K output tokens for deep tracing
    • Multi-step tool use, system debugging, and algorithmic optimization workflows

    Drop-in Agent Integrations:
    • Full compatibility with Claude Code, Cline, Roo Code, Goose, OpenCode, and custom frameworks
    • Switch configurations smoothly via OpenAI-compatible and Anthropic Messages API formats

    ‍

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Model specifications
Model data
  • Model provider
    ZAI
  • Type
    Reasoning
    Chat
    Code
  • Main use cases
    Reasoning
  • Features
    Function Calling
    JSON Mode
  • Deployment
    Serverless
  • Endpoint
    zai-org/GLM-5.2
  • Parameters
    753B
  • Activated parameters
    40B
  • Context length
    256K
  • Input price

    $1.40 / 1M tokens

    $0.26 (cached)/1M

  • Output price

    $4.40 / 1M tokens

  • Input modalities
    Text
  • Output modalities
    Text
  • Released
    June 16, 2026
  • Quantization level
    FP4
  • Category
    Chat
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