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.
4 providers serve this model. FastRouter routes each request to the best available one, and you can bring your own key for any of them.
telnyx
alibaba
basetenmoonshotaiRequest parameters you can send with this model.
Median throughput and time to first token per provider over the last week.
OpenAI-compatible. Point your SDK at FastRouter, or call the API directly.
<FASTROUTER_API_KEY> with your key · Get your key →Installmoonshotai/kimi-k2.7-codeKimi 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.
moonshotai/kimi-k2.6Moonshot 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/kimi-k2.5MoonshotAI'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.
Side by side: price per provider, context window, benchmarks and speed.
Model scores sourced from ArtificialAnalysis