Z.ai: GLM 4.7
Code Agents, Think Deep
Reason. Code. Deploy.
Agentic Coding
SWE-bench 73.8%
Leads open-source models on verified coding benchmarks with stable multi-step reasoning.
Thinking Modes
Interleaved Preserved
Thinks before actions, retains context across turns for complex agent workflows.
Context Power
200K Tokens
Handles long inputs with 128K output via Z.ai: GLM 4.7 API.
Examples
See what Z.ai: GLM 4.7 can create
Copy any prompt below and try it yourself in the playground.
UI Component
“Generate a modern React component for a responsive dashboard with dark mode toggle, charts using Recharts, and clean Tailwind CSS styling. Include full code with imports.”
Agent Workflow
“Design a Python agent that uses interleaved thinking to scrape a webpage, extract product data, analyze prices with pandas, and output a CSV summary. Enable preserved thinking mode.”
Math Proof
“Prove the fundamental theorem of calculus step-by-step using turn-level thinking. Explain integrals and derivatives with formal notation and examples.”
Terminal Script
“Write a bash script for terminal automation: monitor logs, alert on errors via email, and summarize trends. Optimize for efficiency on Linux systems.”
For Developers
A few lines of code.
Agents live. One call.
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
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())