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ojuliio/
MiniMax-M3

Name: MiniMax-M3
Rating: 2.5 (10 reviews)

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MiniMax

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:

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.
KTransformers - see KTransformers MiniMax-M3 tutorial.
unsloth - see tutorial

Inference Parameters

We recommend the following parameters for best performance: temperature=1.0, top_p=0.95.

ojuliio/MiniMax-M3

MiniMax Sparse Attention (MSA)

How to Use

Local Deployment

Inference Parameters

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ojuliio/
MiniMax-M3