---
title: Z.ai GLM 4.7 — Agentic Coding LLM | ModelsLab
description: Access Z.ai GLM 4.7 API for superior agentic coding and reasoning. Generate clean code and handle complex tasks via OpenAI-compatible endpoint. Try now.
url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/zai-glm-47
canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/zai-glm-47
type: website
component: Seo/ModelPage
generated_at: 2026-05-13T12:21:54.945313Z
---

Available now on ModelsLab · Language Model

Z.ai: GLM 4.7
Code Agents, Think Deep
---

[Try Z.ai: GLM 4.7](/models/open_router/z-ai-glm-4.7) [API Documentation](https://docs.modelslab.com)

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](/models/open_router/z-ai-glm-4.7).

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

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

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

FAQ

Common questions about Z.ai: GLM 4.7
---

[Read the docs ](https://docs.modelslab.com)

### What is Z.ai: GLM 4.7 model?

Z.ai: GLM 4.7 is a 358B parameter MoE LLM optimized for agentic coding and reasoning. It tops SWE-bench at 73.8% and supports 200K context. Use via Z.ai: GLM 4.7 API.

### How does Z.ai: GLM 4.7 API work?

OpenAI-compatible chat completions endpoint accepts messages array. Set model to zai-org/GLM-4.7 with bearer token. Supports streaming and tool calling.

### What makes Z.ai: GLM 4.7 alternative stand out?

Advanced thinking modes like interleaved and preserved improve multi-turn stability over predecessors. Excels in UI generation and terminal tasks. MIT licensed open weights.

### Z.ai: GLM 4.7 LLM context length?

200K input tokens with 128K max output. Handles long conversations and agent workflows without truncation. Ideal for complex reasoning.

### z ai glm 4.7 model benchmarks?

73.8% SWE-bench Verified, 87.4% τ²-Bench, 52% BrowseComp. Outperforms GLM-4.6 in coding and tool use. Strong multilingual support.

### z.ai: glm 4.7 model for agents?

Built for task-oriented development with preserved thinking across turns. Generates clean code, modern UIs, and stable reasoning traces. Deploy via API instantly.

Ready to create?
---

Start generating with Z.ai: GLM 4.7 on ModelsLab.

[Try Z.ai: GLM 4.7](/models/open_router/z-ai-glm-4.7) [API Documentation](https://docs.modelslab.com)

---

*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-05-13*