MiniMax-M2.5 is an open-weight large language model designed for real-world productivity across coding, agentic tool use, web search, and office automation. It was trained with reinforcement learning across more than two hundred thousand complex real-world environments, giving it the ability to plan at an architectural level—decomposing features, structure, and UI design before writing code, and covering the full development lifecycle from early system design through late-stage testing. The model spans ten or more programming languages across web, Android, iOS, and Windows stacks, and it produces deliverable office artifacts such as Word documents, PowerPoint presentations, and Excel models.

On benchmark measures, MiniMax-M2.5 reaches 80.2% on SWE-Bench Verified and is reported to complete tasks 37% faster than its M2.1 predecessor while matching Claude Opus 4.6 in throughput. It is offered in two speed variants—a full version at roughly fifty tokens per second and a Lightning version near one hundred tokens per second—with identical capabilities across both, letting users choose between deeper deliberation and faster turnaround. Practically, the model fits well for engineering teams that want an open-weight assistant capable of long-running agentic workflows, full-stack code generation, and structured business-document output without depending on closed proprietary systems.