GLM-4.6

Zhipu AI's latest advanced language model with 200K context window, enhanced coding capabilities, and 15% token efficiency improvements for real-world development tasks.

GLMZhipu AILanguage ModelLarge Language ModelCodingOpen SourceMoELatest
Developer
Zhipu AI
Type
Language Model
License
MIT
🌐 Official Website💰 Pricing & API

Overview

GLM-4.6, released by Zhipu AI on September 30, 2025, is the latest iteration in the GLM series that represents a significant advancement in AI-powered coding and agentic workflows. This model introduces a 200K context window, enhanced real-world coding capabilities, and substantial efficiency improvements, positioning it as a competitive alternative to leading models like Claude Sonnet 4.

The release marks a major milestone for Chinese AI development, with GLM-4.6 achieving near-parity performance with Claude Sonnet 4 on real-world coding tasks while offering significant token efficiency gains. The model is available both through Z.ai's API platform and as open weights for local deployment, democratizing access to advanced AI capabilities.

Capabilities

GLM-4.6 demonstrates comprehensive enhancements across multiple domains with specialized strengths:

  • Enhanced Coding Performance: Superior performance in real-world coding scenarios with near-parity to Claude Sonnet 4 (48.6% win rate)
  • Extended Context Processing: 200K context window with 128K maximum output for complex agentic tasks
  • Token Efficiency: 15% reduction in token consumption compared to GLM-4.5 while maintaining quality
  • Advanced Reasoning: Clear improvements in logical reasoning and problem-solving capabilities
  • Tool Integration: Native support for tool use during inference and agent coordination
  • Multi-Language Support: Enhanced performance across Python, JavaScript, Java, and other programming languages
  • Frontend Development: Superior aesthetics and logical layout in frontend code generation
  • Agent Capabilities: Better performance in tool use and search-based agents with enhanced autonomy

Technical Specifications

GLM-4.6 represents a significant technical advancement with optimized architecture:

  • Model Architecture: Large-scale Mixture of Experts (MoE) architecture for efficient inference
  • Context Window: 200K input tokens (expanded from 128K in GLM-4.5)
  • Maximum Output: 128K tokens for comprehensive responses
  • Precision: BF16/F32 tensor support for efficient inference
  • License: MIT license for open deployment and customization
  • Training Data: Trained on diverse datasets with focus on coding, reasoning, and multilingual capabilities
  • Efficiency: 15% more efficient than GLM-4.5, achieving lowest consumption among comparable models
  • Deployment: Available through API and as open weights for local deployment

Use Cases

GLM-4.6 excels across multiple application domains:

AI-Powered Development

  • Multi-language coding: Superior support for Python, JavaScript, Java, and other languages
  • Frontend development: Enhanced capabilities for creating visually appealing interfaces
  • Agent development: Native support for building AI agents and autonomous systems
  • Code optimization: Better performance in code review and optimization tasks
  • Documentation: Enhanced ability to generate comprehensive technical documentation

Smart Office and Automation

  • PowerPoint creation: Significantly enhanced presentation quality and aesthetics
  • Document automation: Better handling of complex office workflows
  • Layout generation: Advanced capabilities for creating aesthetically pleasing layouts
  • Content integrity: Maintains accuracy while improving visual presentation
  • Workflow optimization: Enhanced automation for office productivity tools

Translation and Cross-Language Applications

  • Minor language support: Optimized performance for French, Russian, Japanese, Korean
  • Informal contexts: Better handling of social media and casual communication
  • E-commerce content: Enhanced capabilities for product descriptions and marketing content
  • Semantic coherence: Maintains meaning across lengthy passages
  • Style adaptation: Superior localization and cultural adaptation

Content Creation and Virtual Characters

  • Novel writing: Enhanced capabilities for long-form creative writing
  • Script development: Better performance in screenplay and dialogue creation
  • Copywriting: Improved marketing and advertising content generation
  • Virtual characters: Maintains consistent personality across multi-turn conversations
  • Social AI: Enhanced capabilities for human-like interaction

Intelligent Search and Research

  • User intent understanding: Enhanced ability to understand and respond to user queries
  • Tool retrieval: Better performance in finding and using appropriate tools
  • Result integration: Improved synthesis of information from multiple sources
  • Deep research: Enhanced capabilities for comprehensive research tasks

Performance Metrics

GLM-4.6 demonstrates strong performance across comprehensive evaluations:

Real-World Coding Evaluation

  • CC-Bench Results: 48.6% win rate against Claude Sonnet 4 in head-to-head comparisons
  • Token Efficiency: 15% reduction in token consumption compared to GLM-4.5
  • Test Methodology: 74 real-world coding tests in isolated Docker environments
  • Transparency: All test questions and agent trajectories published for verification
  • Reproducibility: Open access to test data on Hugging Face for community validation

Comprehensive Benchmark Performance

  • AIME 25: Competitive performance on mathematical reasoning
  • GPQA: Strong results on graduate-level physics questions
  • LCB v6: Enhanced performance on legal case analysis
  • HLE: Improved results on human-level evaluation tasks
  • SWE-Bench Verified: Competitive performance on software engineering tasks

Positioning

GLM-4.6 achieves performance on par with Claude Sonnet 4/4.6 on several leaderboards, solidifying its position as the top model developed in China.

Deployment Options

GLM-4.6 is available through multiple deployment channels:

API Access

  • Z.ai API: Direct access through Zhipu AI's platform
  • OpenRouter: Integration with OpenRouter for broader access
  • Coding tools: Integration with Claude Code, Cline, Roo Code, Kilo Code
  • Upgrade path: Existing Coding Plan users can switch to GLM-4.6

Local Deployment

  • Hugging Face: Open weights available on Hugging Face Hub
  • ModelScope: Alternative hosting on ModelScope platform
  • vLLM support: Native support for vLLM inference engine
  • SGLang support: Compatible with SGLang for local serving
  • Community quantizations: Community-developed quantizations for workstation hardware

Limitations

  • Peak Performance: While highly capable, it may not match the absolute peak performance of some proprietary models in specialized tasks
  • Resource Requirements: Local deployment requires significant computational resources
  • Language Support: While strong in multiple languages, performance may vary across different linguistic contexts
  • Specialized Domains: For extremely specialized tasks, domain-specific models may be more appropriate

Safety & Alignment

GLM-4.6 incorporates safety measures appropriate for its capabilities:

  • Open Development: Transparent development process with open weights for community scrutiny
  • Safety Research: Incorporates safety research from the broader AI community
  • Responsible Deployment: Guidelines for responsible use and deployment
  • Community Oversight: Open weights enable community review and safety improvements
  • Alignment Research: Incorporates alignment research from the open-source community

Pricing & Access

GLM-4.6 offers flexible access options:

API Access

  • Z.ai Platform: Competitive pricing through Z.ai's API
  • OpenRouter: Available through OpenRouter for broader access
  • Coding Tools: Integrated into major coding platforms

Local Deployment

  • Open Weights: Free access to model weights under MIT license
  • Hugging Face: Direct download from Hugging Face Hub
  • ModelScope: Alternative hosting on ModelScope platform
  • Community Support: Active community support for deployment and optimization

Ecosystem & Tools

GLM-4.6 is well-integrated across development platforms:

  • Z.ai API: Primary platform for API access
  • Hugging Face: Open weights and model hosting
  • ModelScope: Alternative model hosting platform
  • vLLM: Native support for high-performance inference
  • SGLang: Compatible with SGLang for local serving
  • OpenRouter: Available through OpenRouter for broader access

Community & Resources

Frequently Asked Questions

GLM-4.6 was released by Zhipu AI on September 30, 2025, representing the latest iteration in the GLM series.
GLM-4.6 features a 200K context window, enhanced coding performance with near-parity to Claude Sonnet 4, 15% token efficiency improvements, and better real-world coding capabilities.
GLM-4.6 achieves near-parity with Claude Sonnet 4 (48.6% win rate) on CC-Bench real-world coding tests and uses 15% fewer tokens than GLM-4.5 while maintaining quality.
Yes, GLM-4.6 is available with open weights under MIT license on Hugging Face and ModelScope, supporting local inference with vLLM and SGLang.
GLM-4.6 supports a 200K input context window with 128K maximum output tokens, enabling handling of more complex agentic tasks.
GLM-4.6 shows clear gains over GLM-4.5 across eight public benchmarks, with 15% token efficiency improvements and enhanced real-world coding performance.
GLM-4.6 uses a large-scale Mixture of Experts (MoE) architecture with BF16/F32 tensor support for efficient inference.
GLM-4.6 excels in AI coding, smart office automation, translation, content creation, virtual characters, and intelligent search applications.
GLM-4.6 is available through Z.ai API, OpenRouter, and as open weights on Hugging Face and ModelScope for local deployment.

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