Today we're introducing our latest model, MiniMax-M2.5.
Extensively trained with reinforcement learning in hundreds of thousands of complex real-world environments, M2.5 is SOTA in coding, agentic tool use and search, office work, and a range of other economically valuable tasks, boasting scores of 80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp (with context management).
Trained to reason efficiently and decompose tasks optimally, M2.5 exhibits tremendous speed in performing complicated agentic tasks, completing the SWE-Bench Verified evaluation 37% faster than M2.1, matching the speed of Claude Opus 4.6.
M2.5 is the first frontier model where users do not need to worry about cost, delivering on the promise of intelligence too cheap to meter. It costs just $1 to run the model continuously for an hour at a rate of 100 tokens per second. At 50 tokens per second, the cost drops to $0.30. We hope that the speed and cost effectiveness of M2.5 enable innovative new agentic applications.
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