migtissera/

MiniMax-M3

• Native Multimodality: M3 undergoes mixed-modality training from the very first step, enabling deeper semantic fusion across text, image, and video.
• Context Scaling via Sparse Attention: M3 introduces MiniMax Sparse Attention (MSA) to improve long context efficiency. M3 delivers 9× prefill and 15× decode speedups compared to M2 at 1M context, reducing per-token compute to 1/20.
• Coding & Cowork Capability: M3 achieves frontier-level performance across long-horizon agentic benchmarks, excelling in both coding and cowork.

MiniMax Sparse Attention (MSA)

📄 Read the technical report: arXiv:2606.13392 · Hugging Face Papers

How to Use

• MiniMax Agent
• MiniMax API

• enabled — Reasoning is always enabled.
• adaptive — M3 automatically determines when additional reasoning is beneficial.
• disabled — Reasoning is disabled to minimize latency and maximize throughput.

Local Deployment

• SGLang - see SGLang cookbook.
• vLLM - see vLLM recipes.
• Transformers - see Transformers docs.
• KTransformers - see KTransformers MiniMax-M3 tutorial.
• unsloth - see tutorial

Inference Parameters

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