MiniMaxAI/MiniMax-M3
MiniMaxAI/MiniMax-M3 is a native multimodal model developed by MiniMaxAI, featuring approximately 428 billion parameters with 23 billion activated parameters and a 1 million token context window. It is distinguished by its mixed-modality training from inception, enabling deep semantic fusion across text, image, and video. The model utilizes MiniMax Sparse Attention (MSA) for efficient long-context processing, offering significant speedups and reduced compute, making it highly effective for long-horizon agentic benchmarks, coding, and cowork capabilities.
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MiniMax-M3: A Native Multimodal Model
MiniMax-M3, developed by MiniMaxAI, is a powerful native multimodal model with approximately 428 billion parameters (23 billion activated) and an extensive 1 million token context window. Its core innovation lies in its mixed-modality training approach, integrating text, image, and video data from the very first training step to achieve deeper semantic understanding across modalities.
Key Capabilities & Innovations
- Native Multimodality: Achieves profound semantic fusion by being trained on mixed-modality data from the outset, unlike models that add modalities later.
- Context Scaling with MiniMax Sparse Attention (MSA): M3 introduces MiniMax Sparse Attention (MSA), a high-performance sparse attention operator specifically designed for million-token contexts. This technology dramatically improves efficiency, delivering 9x prefill and 15x decode speedups compared to its predecessor (M2) at 1M context, while reducing per-token compute by 20x. More details are available in the technical report.
- Frontier-Level Agentic Performance: Excels in long-horizon agentic benchmarks, demonstrating strong capabilities in both coding and cowork scenarios.
Recommended Use Cases
- Complex Multimodal Tasks: Ideal for applications requiring deep understanding and generation across text, image, and video.
- Long-Context Applications: Highly efficient for tasks demanding extensive context, such as detailed document analysis, long-form content generation, or complex coding projects.
- Agentic Workflows: Suited for developing AI agents that perform multi-step reasoning and interaction, particularly in coding and collaborative environments.
MiniMax-M3 also offers flexible reasoning modes (enabled, adaptive, disabled) to optimize for specific latency and throughput requirements.