MoonshotAI AI models

Kimi K3 is Moonshot AI’s 2.8-trillion-parameter flagship multimodal reasoning model with a 1M-token context window, designed for long-horizon coding, complex knowledge work, visual reasoning, and tool-using workflows. It uses Kimi Delta Attention and Attention Residuals to support efficient million-token contexts and always runs in high-effort reasoning mode. The model is exposed via an OpenAI-compatible chat completions API.

Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built for reliable end-to-end programming tasks over long contexts. It supports text and image input, always operates in thinking mode, and is designed for long-horizon coding, agentic task decomposition, tool use, and multi-turn workflows.

Moonshot AI's Kimi-K2.6 is a frontier-scale, open-source Mixture-of-Experts (MoE) multimodal model with 1 trillion total parameters (32B active), a 256K-262K token context window, and native support for text, image, and video inputs, excelling in long-horizon coding, agentic workflows, multi-agent orchestration, and complex software engineering.

MoonshotAI's Kimi-K2.5 is a cutting-edge open-source multimodal large language model series from MoonshotAI, featuring a massive Mixture-of-Experts (MoE) architecture with 1 trillion total parameters—yet only 32 billion activated per token for highly efficient inference and low computational costs. It boasts 61 transformer layers, 64 attention heads, a 256K context window enabled by advanced MLA attention mechanisms, and SwiGLU activations, paired with a MoonViT vision encoder for native handling of images, videos, and cross-modal tasks like visual reasoning, UI mockup-to-code generation (e.g., React from screenshots), and agentic workflows.