minimax/minimax-m2.5

MiniMax M2.5

MiniMax M2.5 is an agent-native frontier model trained explicitly to reason efficiently, decompose tasks optimally, and complete complex workflows under real-world time and cost constraints. It achieves task completion speeds comparable to or faster than leading proprietary frontier models by combining high inference throughput with reinforcement learning focused on token-efficient reasoning and better decision-making in agentic scaffolds. M2.5 is well-suited for production agents handling full-stack software projects, research and analysis workflows, long-horizon planning, and multi-tool orchestration, delivering intelligence for organizations that need both high-quality reasoning and predictable, low latency.