MiniMax-M1

MiniMax-M1, released in June 2025, is the lab’s first reasoning model and, by its own description, the world’s first open-weight large-scale hybrid-attention reasoning model. It is not a new base model: it reuses MiniMax-Text-01 in full — the same 456 billion parameters, the same 45.9 billion active per token, the same lightning-attention stack — and adds large-scale reinforcement learning on top. The weight files of M1 and Text-01 are identical in size down to the byte. The reinforcement learning is where the work went. MiniMax introduced CISPO, an algorithm that clips importance-sampling weights rather than token updates, which the company reports as outperforming competing RL variants on the same budget. The hybrid attention pays off during long chains of thought: MiniMax reports that at a generation length of 100,000 tokens M1 consumes a quarter of the compute of DeepSeek-R1, and it natively supports a one-million-token context — eight times R1’s. M1 shipped in two variants distinguished only by thinking budget: 40K and 80K tokens of extended reasoning. Its licence is the outlier in MiniMax’s catalogue. Text-01 before it and every M2 release after it carry custom terms; M1 alone is Apache 2.0, which cannot be withdrawn. Independent providers still host it at roughly $0.55 per million input tokens and $2.20 per million output — today more expensive than MiniMax’s own newer and larger-context M3.

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