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Available now on ModelsLab · Language Model

Z.ai: GLM 5Expert-grade coding. Open weights.

Try Z.ai: GLM 5API Documentation

Built for Complex Systems Engineering

744B Parameters

Mixture-of-Experts Architecture

40B active parameters per token enable efficient long-context reasoning without computational overhead.

200K Context

Extended Context Window

Process massive codebases, technical documentation, and multi-file projects in single sessions.

Agent-Native

Agentic Execution Loop

Understand environment, plan actions, execute tasks seamlessly with Claude Code and OpenClaw.

Examples

See what Z.ai: GLM 5 can create

Copy any prompt below and try it yourself in the playground.

Microservices Architecture

“Design a scalable microservices architecture for an e-commerce platform handling 10M daily transactions. Include service boundaries, API contracts, database schemas, and deployment strategy.”

Legacy System Refactor

“Analyze this 50K-line monolithic Python application and propose a modular refactoring strategy. Identify dependencies, suggest service extraction, and provide migration roadmap.”

Performance Optimization

“Review this Node.js backend codebase for performance bottlenecks. Suggest database query optimization, caching strategies, and infrastructure improvements with implementation examples.”

Security Audit

“Conduct a security review of this REST API implementation. Identify vulnerabilities, suggest fixes, and provide hardening recommendations for production deployment.”

For Developers

A few lines of code.
Complex systems. Three lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Z.ai: GLM 5

Read the docs

GLM-5 combines 744B parameters with mixture-of-experts efficiency, achieving top performance on Artificial Analysis Intelligence Index among open-weights models. It's specifically optimized for long-horizon agentic tasks and complex coding workflows with 200K token context.

Yes. GLM-5 delivers production-grade performance on large-scale programming tasks and excels at architectural design, dependency mapping, and complex system analysis. It's built for expert developers handling real-world codebases.

The model is trained on 30+ task types including agentic execution, enabling robust multi-step task handling with strong continuity. It proactively suggests execution strategies and maintains full context across extended workflows.

GLM-5-Turbo is optimized for agent frameworks with enhanced tool integration and execution-first orientation. GLM-5 is the flagship open-weights model with broader capabilities. Choose GLM-5-Turbo for agent workflows, GLM-5 for general complex coding tasks.

Yes. With 200K token context, GLM-5 analyzes architectural design, maps dependencies across components, and flags cascading effects from code changes in large-scale systems.

GLM-5 supports up to 200,000 input tokens and 128,000 output tokens, enabling comprehensive analysis and generation of extensive code and documentation in single requests.

Ready to create?

Start generating with Z.ai: GLM 5 on ModelsLab.

Try Z.ai: GLM 5API Documentation