Provided by OpenRouter
Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...
262,144 tokens$0.660/M$3.41/MKimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...
Performance may vary based on query complexity, context length, and task type. Consider using higher-tier models for production-critical applications.
Try these prompts to explore MoonshotAI: Kimi K2.6's capabilities:
Analyze this image and describe what you see in detail
Extract the key information from this screenshot
Compare the two images and explain the differences
Tip: Customize these prompts to fit your specific needs and use cases.
MoonshotAI: Kimi K2.6 uses tiered credit pricing. Subscribe for a monthly credit allowance, connect your own provider API key (BYOK), or browse lower-cost models on the catalog.
Credit cost per message is shown in the model picker. Economy models typically cost 1 credit; frontier models cost more.
Similar models you might be interested in
Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) is Google's fastest, most cost-efficient Gemini image model, built for high-velocity developer pipelines and rapid-fire visual exploration. It delivers text-to-image generation...
Nex-N2-Mini is an open-source agentic mixture-of-experts model from Nex AGI, the smaller sibling in the Nex-N2 series. It accepts text and image input and is built for coding, tool use,...
Gemini 3.1 Flash Image, a.k.a. "Nano Banana 2," is Google’s latest state of the art image generation and editing model, delivering Pro-level visual quality at Flash speed. It combines advanced...
MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts...