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MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it to process long sequences—up to 1 million tokens—while maintaining competitive FLOP efficiency. With 456 billion total parameters and 45.9B active per token, this variant is optimized for complex, multi-step reasoning tasks. Trained via a custom reinforcement learning pipeline (CISPO), M1 excels in long-context understanding, software engineering, agentic tool use, and mathematical reasoning. Benchmarks show strong performance across FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench, often outperforming other open models like DeepSeek R1 and Qwen3-235B.
| Signal | Strength | Weight | Impact |
|---|---|---|---|
| Benchmarksjust now | 62 | 30% | +18.7 |
| Recencyjust now | 82 | 15% | +12.3 |
| Capabilitiesjust now | 50 | 20% | +10.0 |
| Context Windowjust now | 95 | 10% | +9.5 |
| Output Capacityjust now | 77 | 10% | +7.7 |
| Pricingjust now | 2 | 15% | +0.3 |
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Cost Estimator
You save $32.57/month vs category average
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