zai-org/GLM-5
GLM-5 is Zhipu AI's February 2026 flagship — a 744B-parameter sparse MoE (40B active) with Interleaved (deep) thinking that fuses DeepSeek Sparse Attention and Multi-Token Prediction for frontier reasoning over a 200K-token window.
api_example.sh
Technical Specifications
Model Architecture & Performance
Pricing
Pay-per-use, no commitments
API Reference
Complete parameter documentation
| Parameter | Type | Default | Description |
|---|---|---|---|
| stream | boolean | true | Enable streaming responses for real-time output. |
| temperature | number | 0.7 | Controls randomness. |
| max_tokens | number | 4096 | Maximum number of tokens to generate. |
| top_p | number | 1 | Controls nucleus sampling. |
| enable_thinking | boolean | false | Toggle chain-of-thought reasoning mode. Set temperature=1.0 when enabled. |
Explore the full request and response schema in our external API documentation
Performance
Strengths & considerations
| Strengths | Considerations |
|---|---|
| 744B MoE with 40B active parameters delivers frontier-level quality with efficient routing Interleaved/Deep Thinking keeps intermediate reasoning while exposing a toggle to control verbosity 200K token window supports persistent context across large codebases or knowledge stores Trained on 28.5T tokens with upgraded tool streaming and multi-agent orchestration support | Full checkpoint requires ~1.65TB of GPU memory for 200K context, limiting on-prem deployments Interleaved thinking increases latency and token usage when enabled Higher power and networking demand compared with slimmer GLM-4.x releases |
Use cases
Recommended applications for this model
Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
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