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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.

Model Details

👁 Image

How to Use

• MiniMax Agent
• MiniMax API

M3 supports two reasoning modes:

• thinking — for complex reasoning, agentic tasks, and long-horizon collaboration.
• non-thinking — for latency-sensitive scenarios such as chat and code completion.

Local Deployment

Download the model:

hf download MiniMaxAI/MiniMax-M3 --local-dir MiniMax-M3

We recommend the following inference frameworks (listed alphabetically) to serve the model:

SGLang

We recommend using SGLang to serve MiniMax-M3. Please refer to our SGLang Deployment Guide.

vLLM

We recommend using vLLM to serve MiniMax-M3. Please refer to our vLLM Deployment Guide.

Transformers

We recommend using Transformers to serve MiniMax-M3. Please refer to our Transformers Deployment Guide.

ModelScope

You can also get model weights from ModelScope.

Inference Parameters

We recommend the following parameters for best performance: temperature=1.0, top_p=0.95, top_k=40. Default system prompt:

You are a helpful assistant. Your name is MiniMax-M3 and was built by MiniMax.

Tool Calling Guide

Please refer to our Tool Calling Guide.

Contact Us

Contact us at model@minimax.io.

Base model

MiniMaxAI/MiniMax-M3

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