---
title: GLM-5.1 FP4 LLM — Agentic Coding | ModelsLab
description: Deploy GLM-5.1 FP4 for long-horizon coding tasks. 754B MoE model with 8-hour autonomous execution. Try now.
url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/glm-51-fp4
canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/glm-51-fp4
type: website
component: Seo/ModelPage
generated_at: 2026-05-13T10:30:55.015232Z
---

Available now on ModelsLab · Language Model

GLM 5.1 FP4
Autonomous coding. Eight hours.
---

[Try GLM 5.1 FP4](/models/together_ai/zai-org-GLM-5.1) [API Documentation](https://docs.modelslab.com)

Build Agents That Actually Finish
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Long-Horizon Execution

### 8-Hour Autonomous Tasks

Plan, execute, test, and optimize complex engineering problems without human intervention.

Agentic Optimization

### Tool-Driven Performance Tuning

3.6× speedup on ML workloads through continuous tool invocation and iterative refinement.

Production-Ready Coding

### 28% Better Than GLM-5

Refined post-training delivers 45.3 on Z.ai coding benchmarks with thinking mode support.

Examples

See what GLM 5.1 FP4 can create
---

Copy any prompt below and try it yourself in the [playground](/models/together_ai/zai-org-GLM-5.1).

CUDA Kernel Optimization

“Analyze this PyTorch training loop for performance bottlenecks. Profile memory allocation, compute utilization, and kernel launch overhead. Propose CUDA kernel optimizations with specific implementation details and expected speedup metrics.”

Full-Stack Feature Build

“Implement a REST API endpoint with database schema, validation, error handling, and integration tests. Start with architecture planning, then write production-grade code with proper logging and monitoring.”

System Debugging

“Debug this distributed system timeout issue. Trace logs across services, identify root cause, propose fixes with rollback strategy, and implement monitoring to prevent recurrence.”

Code Refactoring

“Refactor this legacy monolith into microservices. Plan service boundaries, design APIs, handle data migration, and ensure backward compatibility during rollout.”

For Developers

A few lines of code.
Agentic workflows. 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 ](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 GLM 5.1 FP4
---

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

### What is GLM-5.1 FP4 and how does it differ from standard LLMs?

GLM-5.1 FP4 is a 754B parameter MoE model optimized for agentic workflows with 40B parameters active per token. Unlike chat-focused models, it sustains coherence across hundreds of tool calls and conversation turns, enabling autonomous execution on complex engineering tasks for up to 8 hours.

### Can I use GLM-5.1 FP4 API for production coding agents?

Yes. GLM-5.1 FP4 supports thinking mode, tool calling, and structured JSON output specifically designed for production agentic pipelines. It achieves 45.3 on Z.ai coding benchmarks and handles multi-step planning with self-correction.

### What is the context window and output token limit?

GLM-5.1 FP4 supports a 200K token context window with up to 131,072 max output tokens, enabling long-document analysis and extended reasoning chains without context resets.

### How does GLM-5.1 FP4 compare to Claude Opus for coding tasks?

GLM-5.1 FP4 is aligned with Claude Opus 4.6 in overall capability but excels in sustained execution and engineering delivery. It's one of the few models capable of 8-hour autonomous task completion, making it superior for long-horizon agentic workflows.

### What makes GLM-5.1 FP4 better for tool use than other models?

GLM-5.1 FP4 maintains coherence across hundreds of tool invocations and handles unexpected results through self-correction. Its MoE architecture with DeepSeek Sparse Attention reduces latency while preserving reasoning quality for tool orchestration.

### Is GLM-5.1 FP4 open-source and what license does it use?

Yes, GLM-5.1 is open-source under the MIT license, allowing commercial deployment and fine-tuning. The FP4 quantization variant maintains performance while reducing memory requirements for cost-efficient inference.

Ready to create?
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Start generating with GLM 5.1 FP4 on ModelsLab.

[Try GLM 5.1 FP4](/models/together_ai/zai-org-GLM-5.1) [API Documentation](https://docs.modelslab.com)

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*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*