Turn visual and textual requirements into interactive code and analysis results
Kimi K2.5 is a native multimodal model developed by Moonshot AI for visual programming, complex analysis, and knowledge work. It can understand tasks by combining text and visual materials, turn page designs into frontend code, and is also suitable for organizing documents and producing research drafts.
Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
Specifications and API features
First, clarify this model's inputs and standard invocation method.
Model name
kimi-k2.5
Input and output
Text message input; assistant text output
Standard API
POST /v1/chat/completions; submit model and messages
Applications pass relevant history and the current question in messages
Model features
Native multimodal model with a focus on visual programming, research, and knowledge work
Native model features are for model selection; this platform's input limits, available parameters, and billing are subject to this model's API and pricing. Use stream for continuous Chat Completions output, and the client is responsible for storing message history.
Core capabilities
Learn what kimi-k2.5 can bring to your work.
From visual design to interactive implementation
K2.5 is distinguished by applying visual understanding to programming rather than merely describing images. Combined with page screenshots, layout references, and text requirements, it can generate frontend code and handle complex layouts, animations, and interactions. Providing component boundaries, the technology stack, and responsive requirements can make the implementation closer to project goals and easier to continue modifying and validating.
Organize mixed materials into work deliverables
When working with materials that interweave text and images, K2.5 can combine visual information for summarization, comparison, and content organization. Tasks can focus on research outlines, report drafts, explanations of table analyses, or presentation structures. It is better suited to reading materials with a delivery goal than simply compressing each section into a short summary.
Turn visual references into editable implementations
K2.5 combines visual understanding with frontend programming. Screenshots provide structure and appearance, while text supplements behavior and technical constraints; output can extend to component code, state design, and review feedback, while fonts, assets, and pixel-level details can continue to be refined in the actual page.
Applicable Scenarios
Start with specific tasks to find where the model can be effective.
Screenshot-Driven Page Development
Provide a screenshot of a product page, along with framework, color scheme, layout, and interaction requirements, and let K2.5 generate component code and implementation notes. Then provide an actual rendering screenshot to further refine spacing, hierarchy, and state changes. Suitable for prototype validation and page reconstruction; delivered code should still be tested in target browsers before going live.
Preserve Supporting Materials for Results
Keep versions of the materials submitted to kimi-k2.5 and the actual responses, distinguishing original facts, model suggestions, and actions already completed by the application. Before structured results enter the system, check required fields, value types, and business rules to avoid directly turning missing information into definitive records.
Standard Messages Simplify Integration with Existing Applications
Use /v1/chat/completions and explicitly set model=kimi-k2.5. Existing OpenAI-compatible applications can retain their message and result handling; when integrating, configure the platform address, API Key, and exact model version.
How to Choose This Model
Choose based on task complexity, input materials, and expected results.
Choose K2.5 When You Need Visual Programming
Compared with earlier Kimi versions focused on single-task assistance, K2.5 places greater emphasis on combined understanding of text and visuals, as well as the workflow from design references to code. If screenshots, layouts, and interaction requirements are central to the task, it offers clear value. If the task is only simple text Q&A, choose based on actual response quality instead; there is no need to increase input complexity solely for visual capabilities.
Do Not Apply the Control Methods of Newer Versions
Existing K2.5 image-text and code workflows can continue to organize tasks according to its capabilities. If model selection requires an explicit toggle for thinking, evaluate kimi-k2.6 separately; if reasoning_effort control is needed, evaluate kimi-k3 separately. They are different versions, so you cannot simply replace the model name while retaining all control fields; test task results and invocation behavior during migration.
Getting Started: Reconstruct a Frontend Prototype from a Design Screenshot
Arrange the inputs first, then connect them to the corresponding application workflow.
Prepare Inputs
Prepare clear screenshots, the target framework, available icons and components, and specify which text or interactions must be retained.
Organize Calls and Follow-Up Workflows
Explicitly select kimi-k2.5 in the Chat Completions request, and organize the background, materials, and output requirements for this request into messages. First use a clearly scoped task to check the response, then include actual review or testing feedback in the next message.
Practical Task Example: Recreate a Frontend Prototype from a Design Screenshot
Design the task directly from the following inputs and acceptance priorities.
Recommended Task
First explain the page's regional hierarchy, then implement a responsive layout; retain the specified copy, and complete loading, empty, and form error states.
Key Checks
At each target screen width, check the screenshots, click flows, and text one by one, and record visual differences that require manual adjustment; do not infer native file reading support from visual understanding.
Usage Boundaries
Before formal use, understand the output quality and scope of capabilities.
Multi-turn history is managed by the application through messages. At each target screen width, check the screenshots, click flows, and text one by one, and record visual differences that require manual adjustment; do not infer native file reading support from visual understanding.
Visual-to-code does not mean deployment is complete or pixel-level consistency is guaranteed. Fonts, assets, responsive breakpoints, and interaction states may all require additional clarification; after generation, run the code, check the page result, and continue revising through screenshot feedback, especially validating dynamic behavior rather than only looking at static layouts.
Agent and Agent Swarm in Kimi products are not equivalent to a regular single conversation request. Research, file generation, and external operations require the corresponding tools and authorization; returned tool calls do not mean the operation has already been performed. When writing or publishing is involved, distinguish between recommendations, execution results, and final confirmation.
Frequently Asked Questions
Answers to common questions about using kimi-k2.5.
Can Kimi K2.5 create webpages from screenshots?
Yes, converting visuals into frontend code is one of its main capabilities. It is recommended to provide the screenshot, tech stack, component scope, and interaction requirements at the same time, rather than only asking it to “copy it.” After generating the output, run the page and then submit rendered screenshots and a description of the differences to help refine the layout and functionality iteratively.
Can K2.5 reasoning be controlled with thinking or reasoning_effort?
Do not use these two fields directly to switch modes for K2.5. In this endpoint, thinking applies to kimi-k2.6, and reasoning_effort applies to kimi-k3. K2.5 has reasoning capabilities, but this does not mean it uses the switches or intensity settings of other versions.
How do I call kimi-k2.5 using the standard API?
Submit model=kimi-k2.5 and messages to /v1/chat/completions. Read normal results from choices[].message.content; for streaming calls, use stream to obtain incremental results. Use this platform's API Key and configure the full base URL according to the SDK you use.
How do I continue analysis from the previous turn?
Have the application save the message history, and include the user and assistant messages relevant to the current question in messages. Prepare clear screenshots, the target framework, available icons, and components, and specify which text or interactions must be retained. When materials or constraints change, update them with the next request.
How do I determine whether kimi-k2.5 is suitable for an existing application?
Use a fixed set of real inputs and acceptance requirements, and record answer omissions, citation accuracy, and the amount of manual editing. Applications that integrate tools should also check parameters, permissions, and result write-back; choose the model based on delivery performance across the complete task, not just the length of a single response.
