MiniMax M1 40K
456B-parameter hybrid MoE reasoning model with 40K thinking budget, lightning attention, and 1M token context for efficient reasoning and problem-solving tasks.
About model
MiniMax M1 40K is a large-scale hybrid-attention reasoning model suitable for complex tasks requiring long input processing and extensive thinking. It supports a context length of 1 million tokens and enables efficient scaling of test-time compute. Ideal for users needing advanced language modeling capabilities for tasks like software engineering and mathematical reasoning.
To run this model you first need to deploy it on a Dedicated Endpoint.
Model | FrontierMath Tier 4 | GPQA Diamond | HLE | SciCode | GDPval-AA | Terminal-Bench 2.1 | Agent Arena | FrontierCode | DeepSWE |
|---|---|---|---|---|---|---|---|---|---|
MiniMax M1 40K | 70.0% | Related open-source models | Competitor closed-source models | ||||||
 Claude Fable 5 | 87.8% | 92.6% | 53% | 60% | 62% | 85% | 53.5% | 70% | |
Claude Opus 5 | 73.2% | 93.2% | 53% | 56% | 68% | 89% | +16.4pp | 53.4% | 74% |
 GPT-5.6 Sol | 82.9% | 94.1% | 47% | 56% | 61% | 88% | 47.5% | 73% | |
 Grok 4.5 | 24.4% | 93.1% | 40% | 54% | 51% | 82% | +4.2pp | 42.4% | 54% |
GPT-5.6 Luna | 61.0% | 91.1% | 37% | 53% | 54% | 81% | 39.8% | 67% |
Model card
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
Prompting
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
Applications & use cases
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
- TypeLLMReasoningChat
- Main use casesChatReasoning
- DeploymentDedicated
- Parameters456B
- Activated parameters45.9B
- Context length1M
- Input modalitiesText
- Output modalitiesText
- ReleasedJune 5, 2025
- External link
- CategoryChat
