MiniMax unveils M3, an open-weights model touting coding-agentic gains and 1M context
In a thread on X, MiniMax cites 59.0% on SWE-Bench Pro and a new sparse attention scheme that scales context to 1M, with an API promo rolling out this week.
By Ryan Merket · Published · Updated
Primary source: X · @MiniMax_AI
Why it matters
If the reported numbers hold, M3 pushes open-weights models further into practical coding and agent workflows while adding 1M context via sparse attention. That combination could let teams self-host or fine-tune a more capable agentic stack without closed-model lock-in. A short-term API discount signals MiniMax is angling for fast developer adoption, turning benchmark wins into early integrations.
MiniMax announced M3 in a thread on X, casting it as the first open-weights model to combine strong coding and agentic performance with a 1M context window via its MiniMax Sparse Attention.
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