MiniMax M2.5 released: 80.2% in SWE-bench Verified
minimax.io
February 12, 2026
13 min read
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58/100
Summary
MiniMax M2.5 is a state-of-the-art AI model designed for real-world productivity, achieving scores of 80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp. It has been extensively trained using reinforcement learning across hundreds of thousands of complex environments, excelling in coding, agentic tool use, search, and office tasks.
Key Takeaways
- MiniMax M2.5 is trained with reinforcement learning in complex real-world environments and achieves state-of-the-art performance in coding, tool use, and office work tasks.
- The model completes the SWE-Bench Verified evaluation 37% faster than its predecessor, M2.1, and matches the speed of Claude Opus 4.6.
- MiniMax M2.5 costs $1 per hour to run continuously at 100 tokens per second, making it a cost-effective solution for users.
- The model has been trained on over 10 programming languages and can handle the entire development lifecycle, from system design to comprehensive code review.
Community Sentiment
Positives
- MiniMax M2.1 is favored for its speed, cost-effectiveness, and excellent tool calling capabilities, making it a top choice in AI workflows.
- The model is currently free on OpenCode, which enhances accessibility for developers looking to experiment with advanced AI tools.
- The balance of speed, quality, and cost in MiniMax models makes them appealing for various applications, including coding and analysis.
Concerns
- Some users express skepticism about the benchmarks, suggesting they may be too good to be true and questioning the novelty of the training methods.
- Concerns about the limitations of available LLMs in corporate environments highlight the challenges of accessing diverse AI models.
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