Models

Kimi K3 is Moonshot AI's 2.8T-parameter open-weight multimodal reasoning model, designed for complex coding, knowledge work, and long-horizon agentic workflows. It excels at repository-scale development, tool use, debugging, and iterative problem solving across text, images, logs, tests, and runtime feedback. Built with KDA and Attention Residuals for improved computational efficiency, Kimi K3 delivers strong performance on advanced engineering and multimodal reasoning tasks, making it well suited for autonomous coding agents and large-scale production workflows.

Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, designed for long-horizon software engineering and agentic development workflows. Built on a native multimodal Mixture-of-Experts (MoE) architecture, it supports text, image, and video inputs and operates exclusively in thinking mode, preserving reasoning across multi-turn interactions. With approximately 1T total parameters and 32B activated per token, plus a 256K-token context window, K2.7 Code excels at end-to-end programming tasks, agentic task decomposition, repository-scale reasoning, and extended coding conversations, making it well suited for advanced coding agents and long-context development workflows.

Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, UI/UX generation, and multi-agent orchestration. It handles complex end-to-end development tasks across languages such as Python, Rust, and Go, and can transform prompts and visual inputs into production-ready interfaces. Powered by a scalable agent swarm architecture, K2.6 can coordinate hundreds of parallel sub-agents for autonomous task decomposition, enabling the generation of documents, websites, and spreadsheets in a single run without human intervention.

Kimi K2.5 is Moonshot AI's native multimodal model, designed to deliver state-of-the-art visual coding capabilities and support a self-directed agent swarm paradigm. Built upon Kimi K2 and further enhanced through continued pretraining on approximately 15 trillion mixed visual and text tokens, it achieves strong, well-balanced performance across general reasoning, visual understanding and coding, and agentic tool-calling workflows. With its robust multimodal foundations and agent-oriented design, Kimi K2.5 is well suited for advanced applications that combine vision, code, and autonomous agent collaboration.

Kimi K2 Thinking is Moonshot AI's most advanced open reasoning model, built on a trillion-parameter MoE with 32B active parameters and a 256K context window. It's optimized for long-horizon, step-by-step reasoning with dynamic tool use, enabling sustained autonomous research, coding, and writing over hundreds of turns. It sets new open-source records on benchmarks like HLE and LiveCodeBench, and maintains stable multi-agent, tool-heavy workflows (200–300 calls) while balancing deep reasoning with efficient inference.

Kimi K2-Instruct-0905 is the newest and strongest Kimi K2 model — a trillion-parameter MoE system with 32B active parameters. It delivers major gains in agentic coding, better frontend development results, and an expanded 256K context window for longer tasks.

Kimi K2-Instruct-0905 is the newest and strongest Kimi K2 model — a trillion-parameter MoE system with 32B active parameters. It delivers major gains in agentic coding, better frontend development results, and an expanded 256K context window for longer tasks.

Moonlight 16B A3B Instruct by MoonShot AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Moonshot V1 128k by MoonShot AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Moonshot V1 32k by MoonShot AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Moonshot V1 8k by MoonShot AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.