Kimi K3

Kimi K3 is positioned as a frontier-class open multimodal model built for long-horizon coding, reasoning, and complex knowledge work. It introduces a new architecture that pairs Kimi Delta Attention with Attention Residuals, layered on top of a Stable LatentMoE framework that activates only sixteen of eight hundred ninety-six experts per forward pass. This combination lets a very large 2.8-trillion-parameter model keep inference efficient while preserving native understanding of text, images, and video in a single forward pass, which is well suited for agentic workflows that have to plan, call tools, and produce structured outputs over long contexts.

Practically, the model is aimed at users who want the flexibility of open weights without giving up the very long context needed for serious research and multi-step software engineering tasks. Its one-million-token window, multimodal grounding, and support for reasoning and tool calling make it a strong fit for code agents, document analysis pipelines, and assistants that must reason across large mixed-media knowledge bases. The open-weight release, together with reported frontier-level benchmark performance relative to peers, marks a notable step toward making top-tier agentic intelligence available outside closed proprietary stacks.