MiniMax M1 80K

Architecture Overview:
• Hybrid Mixture-of-Experts with 456 billion total parameters and 45.9 billion activated per token
• Revolutionary lightning attention mechanism enabling efficient test-time compute scaling
• 1 million token context window - 8x larger than DeepSeek R1 for extensive document processing
• Advanced hybrid attention design optimized for reasoning and long-context understanding

Training Methodology:
• Large-scale reinforcement learning on diverse problems from mathematical reasoning to software engineering
• CISPO algorithm for clipping importance sampling weights instead of token updates
• 80K thinking budget for extended reasoning capabilities and complex problem-solving
• Trained on sandbox-based real-world software engineering environments

Performance Characteristics:
• Consumes 25% of FLOPs compared to DeepSeek R1 at 100K token generation
• Outperforms DeepSeek-R1 and Qwen3-235B on complex software engineering and tool use
• Superior performance on AIME 2024 (86.0), SWE-bench Verified (56.0), and long context tasks
• Optimized for complex tasks requiring extensive reasoning and long input processing

‍

Reasoning Capabilities:
• Advanced reasoning model with 80K thinking budget for complex problem-solving
• System/user/assistant format optimized for extensive reasoning chains
• Lightning attention mechanism enables efficient scaling of test-time compute
• Particularly suitable for tasks requiring processing long inputs and thinking extensively

Optimization Settings:
• Temperature 1.0 and top_p 0.95 for optimal creativity and logical coherence
• General scenarios: "You are a helpful assistant"
• Mathematical tasks: "Please reason step by step and put your final answer within \boxed{}"
• Web development: Detailed engineering prompts for complete code generation

Advanced Features:
• Function calling capabilities for structured external function integration
• Supports extensive multi-turn conversations with maintained context
• Efficient reasoning budget allocation for optimal performance vs cost balance
• Superior performance on competition-level mathematics and complex coding tasks

‍

Advanced Reasoning Applications:
• Competition-level mathematics and complex mathematical problem-solving
• Software engineering tasks including SWE-bench verified challenges
• Long-context document analysis and processing with 1M token capability
• Complex agentic tool use and multi-step reasoning scenarios

Technical & Research:
• Real-world software engineering environments and sandbox-based development
• Advanced coding assistance with extensive reasoning capabilities
• Research applications requiring deep analysis and extended reasoning chains
• Complex problem-solving in STEM fields requiring step-by-step reasoning

Enterprise Applications:
• Next-generation language model agents for complex real-world challenges
• Advanced AI systems requiring efficient test-time compute scaling
• Applications demanding extensive reasoning with computational efficiency
• Complex decision-making systems with long-context understanding and analysis

‍