Kimi K2.5

Kimi K2.5 is a native multimodal language model that understands images, and text. Built by Moonshot AI through continued training on approximately 15 trillion mixed visual and text tokens, K2.5 brings strong vision understanding together with advanced coding and reasoning capabilities.

What can it do?

Kimi K2.5 handles a wide range of tasks that require understanding both visual and textual information:

Visual coding

Turn visual specifications into working code. Show K2.5 a screenshot of a website or app interface, and it can generate the HTML, CSS, and JavaScript to recreate it. It’s particularly strong at front-end development, including layouts, interactions, and animations.

Image understanding

Analyze images to extract information, answer questions about what’s shown, or describe visual content in detail. K2.5 was trained on vision and language data together from the start, rather than having vision added later, which makes it better at tasks that require both.

Reasoning and problem solving

K2.5 supports two modes: thinking mode that shows its step-by-step reasoning process, and instant mode for quick responses. In thinking mode, it can work through complex problems methodically, making it useful for tasks that require careful analysis.

Agentic workflows

The model can break down complex tasks into smaller steps and coordinate multiple actions automatically. It’s designed to work well with tools and can handle multi-step workflows like code refactoring, document generation, or data analysis pipelines.

Office productivity

Generate polished documents, spreadsheets, presentations, and PDFs from natural language descriptions. K2.5 can reason over large, dense inputs and produce professional outputs directly.

How it works

K2.5 is built on top of Kimi K2, which is a mixture-of-experts language model with 32 billion activated parameters and 1 trillion total parameters. The mixture-of-experts architecture means the model has specialized sub-models that handle different types of tasks efficiently.

Unlike most vision-language models that add vision capabilities as an afterthought, K2.5 was trained on mixed vision and text data from the beginning. This native multimodal training helps it handle tasks that require both visual and textual understanding more effectively.

The model supports a 256,000 token context window, which lets it work with long documents, large codebases, or extended conversations without losing track of earlier information.

Two modes

Thinking mode (recommended temperature: 1.0)

In thinking mode, K2.5 shows its reasoning process before giving a final answer. This is useful for complex problems where you want to see how it arrived at its conclusion. The model can use up to 96,000 tokens for its internal reasoning on challenging tasks.

Instant mode (recommended temperature: 0.6)

For tasks that don’t require extended reasoning, instant mode gives faster, direct responses without showing the thinking process.

Example use cases

Generate a complete website from a hand-drawn sketch or mockup image. Build data visualizations by describing what you want to see. Debug code by showing K2.5 a screenshot of an error. Create presentations or reports by describing the content you need. Analyze screenshots to extract structured information. Refactor code across multiple files while maintaining functionality.

Technical details

The model uses INT4 weight quantization optimized for efficient inference while maintaining performance. It supports images in common formats like JPEG and PNG

For best results with multi-turn conversations, maintain the full conversation history including any reasoning traces from previous responses.

Learn more

For detailed technical information and benchmarks, see the official model documentation.

Try moonshotai/kimi-k2.5 on the Replicate Playground at replicate.com/playground