GLM-4.6

Architecture Overview:
• Mixture-of-Experts (MoE) architecture with 357B total parameters optimized for efficient inference
• Extended context window from 128K to 200K tokens enabling complex agentic task handling
• Advanced attention mechanisms supporting multi-turn conversations and long-form content generation
• Optimized token efficiency achieving 30% reduction in consumption compared to GLM-4.5

Training Methodology:
• Trained on diverse multilingual datasets with emphasis on code, reasoning, and conversational data
• Enhanced alignment training for human preference matching in writing style and readability
• Specialized training for tool-use capabilities and agentic behavior
• Reinforcement learning from human feedback (RLHF) for improved instruction following

Performance Characteristics:
• Competitive performance with Claude Sonnet 4 across 8 authoritative benchmarks (AIME 25, GPQA, LCB v6, HLE, SWE-Bench Verified)
• 48.6% win rate against Claude Sonnet 4 in real-world coding tasks (CC-Bench evaluation)
• Superior aesthetics and logical layout in frontend code generation
• Enhanced translation quality for minor languages (French, Russian, Japanese, Korean)
• Top-performing model developed in China with state-of-the-art domestic capabilities

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Conversation Format:
• Multi-turn conversation support with full context retention across 200K tokens
• System message configuration for role definition and behavior customization
• Streaming and non-streaming response modes available
• Thinking mode with tool-use capabilities during inference

Advanced Techniques:
• Recommended temperature: 1.0 for general tasks
• Code-related tasks: top_p=0.95, top_k=40 for optimal results
• Tool-integrated reasoning with native function calling support
• Search-based agent capabilities with specialized toolcall formatting
• Maximum output tokens: 128K for extended generation tasks

Optimization Strategies:
• 15% more token-efficient than GLM-4.5 for equivalent task completion
• Native support for autonomous planning and tool invocation in agentic workflows
• Enhanced task decomposition and cross-tool collaboration capabilities
• Dynamic adjustment support for complex development and office automation workflows

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AI Coding & Development:
• Superior performance in Python, JavaScript, and Java with aesthetically advanced frontend code generation
• Real-world coding excellence demonstrated across 74 CC-Bench evaluation tasks
• Native integration with popular coding assistants and agent frameworks
• Enhanced debugging, testing, and algorithm implementation capabilities

Agentic Applications:
• Complex multi-step task execution with autonomous planning and tool invocation
• Search-based agents with enhanced user intent understanding and result integration
• Office automation including PowerPoint creation with aesthetically advanced layouts
• Deep Research scenarios with comprehensive information synthesis

Smart Office & Automation:
• High-quality presentation generation with clear logical structures
• Document creation maintaining content integrity and expression accuracy
• Cross-tool collaboration for complex development and office workflows
• Ideal for AI presentation tools and office automation systems

Translation & Multilingual Content:
• Optimized translation for French, Russian, Japanese, Korean and informal contexts
• Semantic coherence and stylistic consistency in lengthy passages
• Superior style adaptation and localized expression for global enterprises
• Suitable for social media, e-commerce content, and cross-border services

Content Creation & Virtual Characters:
• Diverse content production including novels, scripts, and copywriting
• Natural expression through contextual expansion and emotional regulation
• Consistent tone and behavior across multi-turn conversations
• Ideal for virtual humans, social AI, and brand personification operations

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