MiniMax-M3

MiniMax-M3 is a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters. Highlights:** **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) M3 is powered by **MiniMax Sparse Attention (MSA)**, a high-performance sparse attention operator designed for million-token contexts. Compared with GQA, MSA dramatically reduces the attention compute and memory footprint while preserving model quality. 📄 Read the technical report: arXiv:2606.13392 · Hugging Face Papers How to Use MiniMax Agent MiniMax API M3 supports three reasoning modes through the `thinking` parameter: **`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 Download the model: We recommend the following inference frameworks to serve the model: SGLang - see SGLang cookbook. vLLM - see vLLM recipes. Transformers - see Transformers docs. KTransformers - see KTransformers MiniMax-M3 tutorial. unsloth - see tutorial ATOM - see MiniMax-M3 MXFP4/MXFP8 Usage Guide Inference Parameters We recommend the following parameters for best performance: `temperature=1.0`, `top_p=0.95`. Contact Us Contact us at model@minimax.io.