Model overview
MiniMax-M2.5 represents a significant advancement in frontier AI models, specifically designed for agentic tasks and complex real-world applications. Built by MiniMaxAI, this model achieves state-of-the-art performance across coding, tool use, search, and office work applications through extensive reinforcement learning trained on hundreds of thousands of complex environments. The model demonstrates remarkable performance metrics: 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp. Compared to predecessors like MiniMax-M2.1, it completes complex agentic tasks 37% faster while maintaining cost effectiveness, matching the speed of Claude Opus 4.6 at a fraction of the price.
Model inputs and outputs
MiniMax-M2.5 processes text inputs and generates text outputs, making it a versatile solution for diverse applications. The model handles long-context tasks efficiently through native support for parallel tool calling and search functionality. It accepts task specifications ranging from simple queries to complex multi-step workflows and produces detailed, structured outputs suitable for immediate use in production environments.
Inputs
- Task descriptions specifying goals for coding, research, or office work
- Search queries and website content for information retrieval tasks
- Document requirements for office applications like Word, PowerPoint, and Excel
- Code specifications and architecture requirements for software development
- Tool or API descriptions for function calling and agent orchestration
Outputs
- Executable code across 10+ programming languages with architectural planning
- Search results with efficient navigation strategies across information-dense webpages
- Office documents including financial models, presentations, and reports
- Task decompositions showing reasoning and execution paths
- Tool calling responses with precise parameter selection
Capabilities
The model excels at planning and decomposing complex tasks before execution. It approaches coding like an experienced software architect, writing specifications and designing system structure before implementation. Performance spans the entire development lifecycle from system design through comprehensive testing, handling full-stack projects across web, Android, iOS, and Windows platforms.
In search and tool calling, the model demonstrates industry-leading performance with stable generalization across unfamiliar scaffolding environments. It solves problems using approximately 20% fewer rounds than predecessors while maintaining superior accuracy, indicating more efficient reasoning paths. For office work, the model produces truly deliverable outputs collaboratively designed with senior professionals in finance, law, and social sciences. It achieved a 59% average win rate in workplace productivity comparisons against mainstream alternatives.
What can I use it for?
The model serves multiple high-value applications. Software development teams can use it for end-to-end project management, from initial architecture design to code review and testing. Research professionals benefit from its advanced search capabilities for expert-level information gathering across complex domains. Organizations can deploy it for office automation, creating financial models, preparing presentations, and drafting reports that meet professional standards. Its cost structure makes it practical for continuous operation: running the model at 100 tokens per second costs approximately $1 per hour, making it economically viable for applications that would be prohibitively expensive with competing models.
Things to try
Experiment with having the model design and build complete applications from scratch, observing how it breaks down architectural decisions before writing code. Try complex research tasks requiring deep exploration across multiple information-dense webpages to see how it optimizes search strategies. Test office workflows combining multiple tools like spreadsheet modeling with document generation to understand its real-world productivity gains. Challenge it with multilingual coding projects across its 10+ supported languages to evaluate generalization performance. Use the faster Lightning version for time-sensitive operations and compare token consumption patterns to understand how reinforcement learning training shaped its reasoning efficiency.
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