MiniMax M1 40K

Architecture Overview:
• Hybrid Mixture-of-Experts with 456 billion total parameters and 45.9 billion activated per token
• Lightning attention mechanism for efficient test-time compute scaling
• 1 million token context window for extensive document processing and analysis
• Optimized hybrid attention design balancing performance with computational efficiency

Training Methodology:
• Large-scale reinforcement learning on diverse reasoning and engineering problems
• CISPO algorithm optimization for efficient importance sampling weight management
• 40K thinking budget providing balanced reasoning capabilities with computational efficiency
• Trained on diverse problems from mathematical reasoning to real-world software engineering

Performance Characteristics:
• Efficient test-time compute scaling with lightning attention mechanism
• Strong performance on AIME 2024 (83.3), SWE-bench Verified (55.6), and coding benchmarks
• Superior efficiency compared to larger reasoning models while maintaining quality
• Optimized for tasks requiring substantial reasoning with moderate computational budgets

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Reasoning Capabilities:
• Advanced reasoning model with 40K thinking budget for efficient problem-solving
• System/user/assistant format optimized for reasoning chains and complex tasks
• Lightning attention enables efficient scaling while maintaining reasoning quality
• Balanced approach to extensive reasoning with computational efficiency considerations

Optimization Settings:
• Temperature 1.0 and top_p 0.95 for creativity and logical coherence balance
• General scenarios: "You are a helpful assistant" for broad applications
• Mathematical reasoning: Step-by-step reasoning with structured output formatting
• Code generation: Comprehensive web development and engineering assistance

Efficiency Features:
• Function calling capabilities for structured external integrations
• Efficient reasoning budget allocation for cost-effective complex problem-solving
• Strong performance across diverse domains with moderate computational requirements
• Optimal balance between reasoning capability and resource utilization

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Efficient Reasoning Applications:
• Mathematical problem-solving and competition-level tasks with budget efficiency
• Software engineering and coding assistance requiring moderate reasoning depth
• Long-context document analysis with 1M token processing capability
• Multi-step reasoning tasks with computational efficiency requirements

Business & Development:
• Cost-effective reasoning applications for business problem-solving
• Development environments requiring advanced AI assistance with budget considerations
• Educational applications requiring step-by-step reasoning and explanation
• Research and analysis tasks with moderate complexity and reasoning requirements

Balanced Applications:
• Applications requiring advanced reasoning capabilities without premium computational costs
• Complex problem-solving scenarios with efficiency and performance balance
• Next-generation AI agents for real-world challenges with resource optimization
• Advanced decision-making systems requiring substantial reasoning with cost-effectiveness

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