MiniMax M1 40K
MiniMax-M1 is an open-source, large-scale reasoning model that uses a hybrid-attention architecture for efficient long-context processing. It supports up to a 1 million token context window and 80,000-token reasoning output, matching Gemini 2.5 Pro’s scale while being highly cost-effective. Its Lightning Attention mechanism reduces compute requirements to about 30% of DeepSeek R1’s, and a new reinforcement learning algorithm, CISPO, doubles convergence speed compared to other RL methods. Trained on 512 H800s over three weeks, M1 achieves near state-of-the-art results across software engineering, long-context, and tool-use benchmarks, outperforming most open models and rivaling top closed systems.
Benchmark results
| Benchmark | Score | Tags | Source |
|---|---|---|---|
| AIME 2024 | 83.3% | self-reported llm-stats | link → |
| AIME 2025 | 74.6% | self-reported llm-stats | link → |
| GPQA | 69.2% | self-reported llm-stats | link → |
| Humanity's Last Exam | 7.2% | self-reported llm-stats | link → |
| LiveCodeBench | 62.3% | self-reported llm-stats | link → |
| LongBench v2 | 61.0% | self-reported llm-stats | link → |
| MATH-500 | 96.0% | self-reported llm-stats | link → |
| MMLU-Pro | 80.6% | self-reported llm-stats | link → |
| Multi-Challenge | 44.7% | self-reported llm-stats | link → |
| OpenAI-MRCR: 2 needle 128k | 76.1% | self-reported llm-stats | link → |
| OpenAI-MRCR: 2 needle 1M | 58.6% | self-reported llm-stats | link → |
| SimpleQA | 17.9% | self-reported llm-stats | link → |
| SWE-Bench Verified | 55.6% | self-reported llm-stats | link → |
| TAU-bench Airline | 60.0% | self-reported llm-stats | link → |
| TAU-bench Retail | 67.8% | self-reported llm-stats | link → |
| ZebraLogic | 80.1% | self-reported llm-stats | link → |