This is a simplified guide to an AI model called Kimi-K2.5 maintained by moonshotai. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
Kimi-K2.5 represents a significant advancement in multimodal AI models, built by Moonshot AI through continual pretraining on approximately 15 trillion mixed visual and text tokens. This model extends the capabilities of Kimi-K2-Base by adding native multimodal understanding and agentic features that enable it to reason across text, images, and video simultaneously. Unlike earlier versions, Kimi-K2.5 can generate code directly from visual specifications like UI designs and video workflows, making it particularly suited for developers who need to translate visual concepts into functional implementations.
The architecture uses a Mixture-of-Experts design with 1 trillion total parameters but only activates 32 billion per token, making it efficient despite its scale. It features 384 experts with 8 selected per token, a 256K context window, and the MoonViT vision encoder with 400 million parameters. This configuration balances performance with computational efficiency, allowing the model to handle complex reasoning tasks while maintaining reasonable inference costs.
Model inputs and outputs
Kimi-K2.5 accepts multimodal inputs including text prompts, images, and video content, processing them through a unified architecture that understands relationships between visual and textual information. The model operates in multiple modes including instant and thinking modes, allowing users to choose between fast responses and deeper reasoning. It produces text outputs, generated code, tool orchestration commands, and structured reasoning traces when in thinking mode.
Inputs
- Text prompts: Natural language instructions and questions of any complexity level
- Images: Visual content including diagrams, screenshots, charts, and photographs
- Video content: Sequential visual data for temporal reasoning and understanding
- Tool specifications: Guidance on which tools and capabilities to leverage during execution
Outputs
- Text responses: Answers, explanations, and conversational replies
- Generated code: Working code generated from visual specifications or text descriptions
- Tool execution plans: Orchestrated sequences of tool calls for complex tasks
- Reasoning traces: Step-by-step reasoning processes when thinking mode is enabled
Capabilities
The model demonstrates exceptional performance across reasoning, knowledge, coding, vision, and agentic search benchmarks. It scores 96.1 on AIME 2025, 87.6 on GPQA-Diamond, and 87.1 on MMLU-Pro, showing strong reasoning capabilities. For vision tasks, it achieves 90.1 on MathVista, 92.3 on OCRBench, and 87.4 on VideoMME, demonstrating sophisticated visual understanding across documents, diagrams, and video sequences.
The agent swarm capability represents a key innovation, allowing the model to decompose complex tasks into parallel sub-tasks executed by dynamically instantiated domain-specific agents. This approach improves performance on agentic search benchmarks, scoring 78.4 on BrowseComp and 79.0 on WideSearch when using agent swarm orchestration. The model can autonomously invoke and coordinate multiple tools for tasks like web search, code execution, and visual data processing.
What can I use it for?
Developers can use Kimi-K2.5 for translating UI designs and workflow diagrams into functional code, substantially reducing the time from design to implementation. Research teams benefit from its strong reasoning capabilities for analyzing complex problems across mathematics, science, and programming domains. Companies can deploy it for agentic search and information retrieval tasks that require understanding context across multiple documents and sources.
The model excels in scenarios requiring coordination of multiple specialized agents, such as financial analysis that combines web search, data retrieval, and numerical reasoning. Software engineers can leverage it for code review, debugging, and refactoring tasks where understanding both the visual architecture and textual implementation matters. The extended thinking mode enables use cases requiring careful deliberation, such as strategic planning or complex problem decomposition.
Things to try
Experiment with providing screenshots or wireframes alongside text descriptions to see how the model combines visual and textual context for code generation. Try breaking down large research or analysis questions that would benefit from the model's ability to search the web, process documents, and reason about findings in parallel through agent swarm coordination.
Test the thinking mode on problems that benefit from step-by-step reasoning, particularly those involving mathematics, logic puzzles, or multi-stage planning where deeper reflection leads to better solutions. Use the vision capabilities to process charts, diagrams, and scientific figures alongside text, letting the model reason about relationships between visual elements and textual explanations. Challenge the model on tasks combining multiple domains, such as analyzing financial reports that include both tabular data and explanatory narratives.