MiniMax-M3-MXFP8

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-M3-MXFP8 is the MXFP8 quantized variant of [MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3), a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters. M3 is powered by [**MiniMax Sparse Attention (MSA)**](https://github.com/MiniMax-AI/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](https://arxiv.org/abs/2606.13392) · [Hugging Face Papers](https://huggingface.co/papers/2606.13392) - [MiniMax Agent](https://agent.minimax.io/) - [MiniMax API](https://platform.minimax.io/) 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. Download the model: hf download MiniMaxAI/MiniMax-M3 --local-dir MiniMax-M3 We recommend the following inference frameworks to serve the model: - [SGLang](https://docs.sglang.io/) - see [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/MiniMax/MiniMax-M3). - [vLLM](https://github.com/vllm-project/vllm) - see [vLLM recipes](https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3). - [Transformers](https://github.com/huggingface/transformers) - see [Transformers docs](https://huggingface.co/docs/transformers/model_doc/minimax_m3_vl). We recommend the following parameters for best performance: `temperature=1.0`, `top_p=0.95`. Contact us at [model@minimax.io](mailto:model@minimax.io).