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GLM 5.1 FP4Autonomous coding. Eight hours.

Try GLM 5.1 FP4API Documentation

Build Agents That Actually Finish

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.

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 
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 GLM 5.1 FP4

Read the docs

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.

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.

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.

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.

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.

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?

Start generating with GLM 5.1 FP4 on ModelsLab.

Try GLM 5.1 FP4API Documentation