by MiniMax
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 |
|---|---|---|---|
| Context Window2026-03-03T20:24:49.469Z | 95 | 15% | +14.3 |
| Recency2026-03-03T20:24:49.469Z | 86 | 15% | +12.8 |
| Capabilities2026-03-03T20:24:49.469Z | 43 | 25% | +10.7 |
| Output Capacity2026-03-03T20:24:49.469Z | 77 | 10% | +7.7 |
| Versatility2026-03-03T20:24:49.469Z | 33 | 10% | +3.3 |
| Pricing Tier2026-03-03T20:24:49.469Z | 2 | 25% | +0.6 |
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