MiniMaxAI/MiniMax-M3
huggingface.co/MiniMaxAI/MiniMax-M3
Model card
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
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:
``bash
hf download MiniMaxAI/MiniMax-M3 --local-dir MiniMax-M3
``
We recommend the following inference frameworks to serve the model:
- SGLang - see SGLang cookbook.
- vLLM - see vLLM recipes.
- Transformers - see Transformers docs.
Inference Parameters
We recommend the following parameters for best performance: temperature=1.0, top_p=0.95.
Contact Us
Contact us at [email protected].