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MiniMax Open-Sources M3 Large Model with MSA Architecture, 1M Context, Rivaling Overseas Flagships
MiniMax Xiyu Technology unveiled its next-generation large model, the MiniMax M3, on June 1, 2026. It stands as China’s first open-source model to combine elite-level programming, an ultra-long 1M context window, and native multimodal capabilities, designed to rival leading overseas closed-source models across the board.
To address context expansion bottlenecks in complex agent tasks, M3 introduces a sparse attention architecture (MSA) at the foundation level. Compared to conventional approaches, it delivers more precise KV partitioning and operator-level optimizations, achieving four times the computational speed of comparable open-source solutions. With a 1M context length, the per-token computational cost drops to just one-tenth of the previous generation. It accelerates the prefill stage by over 9x and the decoding stage by 15x.
Through native trillion-scale interleaved data training, M3’s semantic space achieves deep integration. It outperforms GPT-5.5 and Gemini 3.1 Pro in authoritative software engineering and multimodal evaluations like SWE-Bench Pro. In extreme task tests, M3 demonstrates robust long-thread autonomous planning: it independently reproduced an ICLR top paper’s experiments within 12 hours, ran 24 hours straight without reference code while calling tools nearly 2,000 times, boosted FP8 matrix multiplication hardware utilization on Hopper architecture from 7.6% to 71.3%, and autonomously orchestrated the full “data-training-iteration” pipeline on the open PostTrainBench.
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To address context expansion bottlenecks in complex agent tasks, M3 introduces a sparse attention architecture (MSA) at the foundation level. Compared to conventional approaches, it delivers more precise KV partitioning and operator-level optimizations, achieving four times the computational speed of comparable open-source solutions. With a 1M context length, the per-token computational cost drops to just one-tenth of the previous generation. It accelerates the prefill stage by over 9x and the decoding stage by 15x.
Through native trillion-scale interleaved data training, M3’s semantic space achieves deep integration. It outperforms GPT-5.5 and Gemini 3.1 Pro in authoritative software engineering and multimodal evaluations like SWE-Bench Pro. In extreme task tests, M3 demonstrates robust long-thread autonomous planning: it independently reproduced an ICLR top paper’s experiments within 12 hours, ran 24 hours straight without reference code while calling tools nearly 2,000 times, boosted FP8 matrix multiplication hardware utilization on Hopper architecture from 7.6% to 71.3%, and autonomously orchestrated the full “data-training-iteration” pipeline on the open PostTrainBench.
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