MiMo-V2.5 is a 310B-parameter Sparse MoE model with 15B active parameters, built to serve as a versatile engine for both multimodal perception and autonomous agency. Its architecture is rooted in a hybrid sliding-window attention backbone, which is further enhanced by dedicated visual and audio encoders connected through lightweight projectors. This design allows the model to reason fluidly across text, image, and audio inputs, making it highly effective for tasks that require deep contextual awareness and the ability to act on perceived information.

The model underwent a rigorous five-stage training process, beginning with extensive text pre-training and projector warmup, followed by large-scale multimodal pre-training. Subsequent supervised fine-tuning and agentic post-training, which included a progressive expansion of the context window up to 1 million tokens, were finalized with reinforcement learning and MOPD to sharpen its reasoning and perception. These methods enable the model to excel in real-world agentic workflows, such as coding assistance and automated task execution, positioning it as a robust, efficient alternative for developers seeking high-performance, enterprise-ready AI tools.