GLM 4.6: Advanced Agentic and Reasoning Model

GLM 4.6 is a large language model developed by Z.ai (Zhipu AI) that excels in agentic applications, reasoning tasks, and code generation. Building upon GLM-4.5, this model introduces significant improvements in context handling, reasoning capabilities, and tool-using agent integration.

This template defaults to 32k context for wider compatibility in search

Key Features

• Extended Context - Expanded context window from 128K to 200K tokens for handling complex, long-form tasks
• Enhanced Reasoning - Clear improvements in reasoning performance with support for tool use during inference
• Superior Coding - Demonstrates stronger real-world coding performance in applications and complex development tasks
• Agentic Capabilities - Advanced tool-using and search-based agent integration for multi-step workflows
• MIT License - Open source with full commercial use permissions

Benchmark Performance

GLM-4.6 was evaluated across eight public benchmarks covering agents, reasoning, and coding, demonstrating clear performance gains over GLM-4.5 and competitive results against leading models.

The model shows particularly strong performance in:

• Agentic task completion
• Complex reasoning workflows
• Real-world coding applications
• Tool-integrated systems

Use Cases

• Agentic applications requiring multi-step reasoning and tool use
• Complex code generation and debugging tasks
• Research and technical analysis with extended context
• Tool-using systems and function calling applications
• Search-based agents and information retrieval
• Long-form document analysis and generation
• Multi-turn conversations with context retention
• Educational applications with detailed explanations

Architecture and Capabilities

GLM-4.6 builds on the General Language Model architecture with specific optimizations for reasoning and tool use. The model supports function calling and tool integration during inference, enabling sophisticated agentic workflows where the model can autonomously use external tools to complete complex tasks.

The expanded 200K token context window allows the model to process extensive documents, maintain coherent multi-turn conversations, and handle complex reasoning chains that require reference to large amounts of information.

Training Approach

The model was trained with a focus on improving real-world performance in coding, reasoning, and agentic tasks. Evaluation settings include temperature of 1.0 for general tasks, with optimized sampling parameters for specialized applications like code generation.

Deploy GLM 4.6 on Vast.ai for access to advanced agentic and reasoning capabilities with flexible GPU infrastructure for research and production applications.