MiniMax-M2.5 is built on a 229B-parameter Mixture-of-Experts architecture with 10B active parameters, designed from the ground up for agentic workflows and complex real-world tasks. The model was trained extensively with reinforcement learning across hundreds of thousands of real-world environments, giving it particular strength in coding, tool-augmented reasoning, search, and office productivity work. Its architecture targets the demands of production AI systems rather than narrow academic benchmarks, positioning it for sustained multi-step tasks rather than single-shot queries.

The training approach leverages large-scale reinforcement learning to cultivate practical capabilities, which shows up in its 80.2% score on SWE-Bench Verified and its 37% inference speed improvement over its predecessor. Practitioners can choose between the standard variant running at 50 tokens per second and a Lightning variant that doubles throughput to 100 tokens per second without capability trade-offs. Being released under an open MIT license, the model invites community scrutiny and customization, while its availability across cloud platforms and dedicated endpoints broadens access for teams building agents, coding assistants, and productivity tools in production environments.