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MiniMax M3 - MiniMax's next-generation AI model
MiniMax M3 is a brand-new AI model from MiniMax, boasting leading programming, agent, and long text processing capabilities. The model employs an innovative MSA sparse attention architecture, supporting ultra-long contexts of up to 1MB, resulting in a 20x efficiency improvement.
What is MiniMax M3?
MiniMax M3 is a brand-new AI model from MiniMax, boasting leading programming, agent, and long text processing capabilities. The model employs an innovative MSA sparse attention architecture, supporting ultra-long contexts of up to 1MB, resulting in a 20x efficiency improvement. It also natively supports image and video input, allowing for desktop control. It surpasses models like GPT-5.5 in benchmarks such as SWE-Bench and is open-source, accessible through MiniMax Code and API.
Main functions of MiniMax M3
- Programming and Intelligent AgentsOutperforms mainstream models such as GPT-5.5 in SWE-Bench Pro coding tests.It can autonomously perform task decomposition, tool invocation, and multi-step reasoning; the code goal is to be directly deliverable..
- 1M ultra-long contextBased on the self-developed MSA architecture, the API supports a maximum context window of 1 million tokens (ensuring at least 512K tokens are available)..
- Native multimodal supportIt supports image and video input, can understand figures and formulas in academic papers, and has the ability to simulate computer desktop operation..
- High-efficiency model architectureIt adopts a sparse MoE architecture with a total of 196 bytes of parameters, but only activates about 11 bytes of parameters in each inference to ensure running efficiency..
The technical principles of MiniMax M3
- MSA Sparse Attention ArchitectureThe model adopts the self-developed MSA (MiniMax Sparse Attention) mechanism, which quickly filters out key KV blocks through a lightweight indexing module, and then performs sparse computation on these blocks, which greatly reduces the amount of computation for long sequences.
- Two-stage calculation processDuring the indexing phase, the model uses a low-cost attention module to evaluate the importance of all tokens; during the sparsity phase, full attention computation is performed only on the selected high-value blocks, reducing the computational cost of 1 million token contexts to 1/20 of that of traditional models.
- MoE Hybrid Expert StructureThe model has a total of 196B parameters, and only about 11B parameters (about 6 experts) are activated in each inference, which significantly improves inference speed and deployment efficiency while maintaining a large model capacity.
How to use MiniMax M3
- Experience it online (MiniMax Code):access MiniMax Code official website: https://agent.minimaxi.com/downloadUsers can directly experience programming, long text, and other capabilities without deployment.
- API callsAccess the MiniMax Open Platform to obtain the API key and integrate M3 into your applications or workflows. Image/video input and Agent tasks are supported.
MiniMax M3's core advantages
- Top-notch programming and agent capabilitiesIt has achieved international leading levels in authoritative evaluations such as SWE-Bench Pro and Terminal Bench, with some indicators surpassing GPT-5.5 and Gemini 3.1 Pro, and can complete end-to-end code delivery and automation of complex tasks.
- Extremely long context + extreme efficiencySupports 1M token contexts; leveraging its self-developed MSA sparse attention architecture, the computational cost for processing millions of tokens is only a fraction of that of traditional models. 1/20The pre-filling and decoding stages were accelerated by 9.7 times and 15.6 times, respectively.
- Native multimodal and desktop operationIt directly supports image and video input, can understand visual information such as paper charts and formulas, and can simulate computer desktop operations (such as clicking and inputting), thus expanding the application boundaries of Agent.
MiniMax M3 Comparison with Similar Products
| Comparison items | MiniMax M3 | GPT-5.5 |
|---|---|---|
| Programming skills | SWE-Bench Pro Beyond GPT-5.5 | Excellent, but slightly below M3 |
| Context efficiency | 1M tokens require only 1/20 of the computational effort. | Supports 1M but has high computing cost |
| Multimodal | Native support for image/video/desktop operations | Image only (multimodal version required) |
| open source | Fully open source | Closed source |
Application scenarios of MiniMax M3
- Intelligent software developmentIt directly generates deliverable code based on requirements, and completes automated testing, code refactoring, and debugging. The Agent capability can autonomously call tools and break down tasks to assist in the completion of the entire project development.
- Long document analysisProcess millions of tokens at once (such as hundreds of pages of financial reports, an entire technical manual, or all medical records) for summarizing, Q&A, contract review, or multi-document comparison.
- Desktop automation and digital employeesThe model can "understand" screen content and simulate computer operations such as clicking and input, making it suitable for scenarios such as RPA process automation, software testing, and data entry.
- Multimodal research and educationIt can be used to understand the figures, formulas, and experimental screenshots in papers, assisting in literature interpretation and experimental reproduction; it can also be used for teaching courseware analysis and intelligent Q&A.