MiniMax M2.5: Intelligence too cheap to meter, RL process rewards, real-world productivity

This post details the integration and performance of MiniMax M2.5, a frontier MoE model. Key advancements include an efficient reinforcement learning setup with per-step process rewards to optimize task decomposition and reasoning, leading to significant improvements in token efficiency and cost-effectiveness compared to closed-source models. The model demonstrates SOTA benchmarks in coding, browser search, and tool use, and is capable of completing economically valuable real-world tasks. The platform now supports this model, making advanced AI capabilities more accessible and affordable for agentic applications and complex workflows.