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MiniMax M3 introduces MSA, a sparse attention architecture that reduces per-token compute by 20x at 1M context while matching full attention on most capabilities. The model delivers frontier-level coding performance on SWE-Bench Pro and native multimodal support, positioning it as an open-weight alternative with immediate API availability. These architectural and benchmark improvements suggest a viable path for deploying long-context, agentic workloads without the quadratic cost of standard attention mechanisms.
MiniMax Releases MiniMax M3 with MSA Architecture Supporting 1M-Token Context, Native Multimodality, and Agentic Coding

MiniMax M3 introduces MiniMax Sparse Attention, a 1M-token context window, and native image, video, and computer use support. The post MiniMax Releases MiniMax M3 with MSA Architecture Supporting 1M-Token Context, Native Multimodality, and Agentic Coding appeared first on MarkTechPost.
MiniMax officially released MiniMax M3 on June 1, 2026. The model introduces MSA (MiniMax Sparse Attention), a new sparse attention architecture that gives M3 a 1M-token context window. M3 also supports image and video input and desktop computer operation natively. The API is live now. MiniMax M3 is available today via MiniMax Code, the MiniMax Token Plan, and the MiniMax API. It is the next model in the M-series line after M2.7. MiniMax positions M3 as an open-weight model combining frontier-level coding performance, a 1M-token context window, and native multimodal input in a single architecture — the first to do so, per MiniMax. The corresponding model weights and technical report are scheduled for release within 10 days of launch. The central architectural change in MiniMax M3 is MSA (MiniMax Sparse Attention). Standard full attention has quadratic computational complexity: as context length grows, compute cost grows as the square of the sequence length. MSA is designed to address this. Sparse attention mechanisms generally add a pre-filtering stage before…
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