GLM 5.1 FP4
Autonomous coding. Eight hours.
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
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())