Posted on Mar 16

GLM-5-Turbo Complete Guide 2026

#ai #llm #zhipu #guide

GLM-5-Turbo Complete Guide 2026: China's New Frontier AI Model

🎯 Key Takeaways (TL;DR)

GLM-5-Turbo is Zhipu AI's latest flagship model, designed specifically for high-throughput agentic workloads with improved stability and efficiency
The GLM-5-Turbo model scales to 744B parameters (40B active) with 28.5T training tokens, integrating DeepSeek Sparse Attention for reduced deployment costs
GLM-5-Turbo pricing starts at approximately $0.96 per million input tokens and $3.20 per million output tokens on OpenRouter—significantly undercutting competitors
GLM-5-Turbo is designed for complex agent tasks including advanced reasoning, coding, tool use, web browsing, and multi-step workflows

What is GLM-5-Turbo?
Technical Specifications
Performance and Benchmarks
GLM-5-Turbo vs Competitors
Pricing and Availability
Use Cases
Summary

What is GLM-5-Turbo?

GLM-5-Turbo is the latest flagship large language model from Zhipu AI (also known as Z.ai), a Chinese AI company and the first public AI company in China. Released on February 11, 2026, just days before Lunar New Year, GLM-5 represents a significant leap forward in open-source AI capabilities.

Unlike its predecessors, GLM-5-Turbo is specifically engineered for high-throughput agentic workloads. The "Turbo" variant focuses on improving stability and efficiency in long-chain agent tasks, enabling smoother execution for complex, multi-step workflows.

💡 Pro Tip
GLM-5-Turbo is specifically optimized for OpenClaw and similar agent-driven environments, making it an excellent choice for automation and coding tasks.

Technical Specifications

Specification	GLM-5	GLM-4.5
Total Parameters	744B	355B
Active Parameters	40B	32B
Pre-training Tokens	28.5T	23T
Context Length	Up to 200K	200K
Attention Mechanism	DeepSeek Sparse Attention (DSA)	Standard

Key Technical Innovations

DeepSeek Sparse Attention (DSA): The integration of DSA largely reduces deployment costs while maintaining high performance, making the model more accessible for production use.
Agentic Design: GLM-5 is specifically designed for complex systems engineering and long-horizon agentic tasks, including:
- Advanced reasoning
- Coding and software development
- Tool use and function calling
- Web browsing automation
- Terminal operations
- Multi-step agentic workflows
Extended Context: Supports up to 200K tokens of context, enabling the model to handle long documents and complex conversations without losing track of important details.

Performance and Benchmarks

According to benchmarks and independent testing:

Coding Capabilities: GLM-5 approaches Anthropic's Claude Opus 4.5 in coding benchmark tests
Benchmark Performance: Surpasses Google's Gemini 3 Pro on several benchmarks
Hallucination Rate: Achieves a record-low hallucination rate among open-source models, according to VentureBeat
Agent Stability: Specifically optimized for long-running agent tasks with improved error handling and task continuity

Key Improvements Over GLM-4.5

The model shows significant improvements across multiple dimensions:

Metric	Improvement
Parameter Scale	2x increase (355B → 744B)
Training Data	24% more tokens (23T → 28.5T)
Active Parameters	25% increase (32B → 40B)
Deployment Efficiency	Significantly improved via DSA