Kimi K2.5 is a large-scale multimodal foundation model developed by Moonshot AI, based on a Mixture-of-Experts (MoE) architecture. It features 1.04 trillion total parameters, with 32 billion parameters activated per token to balance computational depth with inference efficiency. The model was pre-trained on approximately 15 trillion tokens of mixed visual and text data, enabling native multimodal capabilities where vision and language understanding are developed in unison. The non-reasoning variant, often utilized as the model's standard or "Instant" mode, is designed for high-speed instruction following and general-purpose tasks without the additional latency of extended reasoning traces. It supports a context window of 256,000 tokens, allowing for the analysis of massive documents and complex codebases. ## Capabilities and Architecture Technical refinements in Kimi K2.5 include the use of Multi-head Latent Attention (MLA) and the SwiGLU activation function. Its visual intelligence is powered by the MoonViT encoder, which facilitates tasks such as "visual coding," where the model generates functional code or interactive user interfaces directly from UI mockups and video demonstrations. The model is also engineered for agentic workflows, supporting an Agent Swarm framework that can decompose complex objectives into parallel sub-tasks coordinated across multiple specialized agents. In benchmark evaluations, Kimi K2.5 demonstrates competitive performance in mathematical reasoning, programming, and long-horizon document synthesis, maintaining stable tool-use execution over hundreds of sequential calls.