Overview
Kimi K2.5 represents a significant evolution in Moonshot AI's flagship model, introducing native multimodal capabilities and groundbreaking Agent Swarm technology. Released in January 2026, K2.5 builds on K2's foundation through continued pretraining on approximately 15 trillion mixed visual and text tokens, making it the most powerful open-source model to date.
This version targets developers, content creators, and knowledge workers who need advanced visual understanding, autonomous coding capabilities, and efficient multi-agent orchestration. K2.5 excels at front-end development, visual debugging, and complex office productivity tasks that require reasoning over images, videos, and documents simultaneously.
What's New
Native Multimodal Architecture
K2.5 introduces native multimodal processing, enabling simultaneous understanding of text, images, and videos within a single conversation. Unlike Kimi K2 (text-centric), K2.5 is built as a native multimodal model that can reason over visual inputs to generate code from UI designs and videos, perform visual debugging, and create interactive interfaces from natural language descriptions.
The model includes a 400M parameter vision encoder (MoonViT) and was trained on 15 trillion mixed visual and text tokens. This large-scale vision-text joint pretraining eliminates the traditional trade-off between vision and text capabilities—they improve in unison at scale.
Coding with Vision
K2.5 demonstrates state-of-the-art coding capabilities, particularly in front-end development. It can transform simple conversations into complete front-end interfaces with interactive layouts and rich animations such as scroll-triggered effects. The model excels at image/video-to-code generation and autonomous visual debugging, allowing users to express intent visually rather than relying solely on text prompts.
On Kimi Code Bench, Moonshot AI's internal coding benchmark covering diverse end-to-end tasks—from building to debugging, refactoring, testing, and scripting—K2.5 shows consistent and meaningful improvements over K2 across all task types according to official reports. This makes it particularly valuable for software engineers working with visual specifications and UI designs.
Agent Swarm Technology
K2.5 introduces Agent Swarm, a self-directed multi-agent orchestration system that can coordinate up to 100 sub-agents executing parallel workflows across up to 1,500 tool calls. Compared to single-agent setups, this reduces execution time by up to 4.5x through parallel, specialized execution.
The Agent Swarm is automatically created and orchestrated by K2.5 without predefined subagents or workflows. It uses Parallel-Agent Reinforcement Learning (PARL) with a trainable orchestrator agent that decomposes tasks into parallelizable subtasks, each executed by dynamically instantiated subagents running concurrently.
According to Moonshot AI's internal evaluations, Agent Swarm achieves an 80% reduction in end-to-end runtime while enabling more complex, long-horizon workloads. The system reduces minimum critical steps by 3×–4.5× compared to single-agent execution in wide search scenarios.
Enhanced Office Productivity
K2.5 brings agentic intelligence into real-world knowledge work, handling high-density, large-scale office tasks end to end. The model reasons over large, high-density inputs, coordinates multi-step tool use, and delivers expert-level outputs including documents, spreadsheets, PDFs, and slide decks directly through conversation.
On Moonshot AI's internal expert productivity benchmarks—AI Office Benchmark and General Agent Benchmark—K2.5 shows 59.3% and 24.3% improvements over K2 Thinking according to official reports, reflecting stronger end-to-end performance on real-world tasks. It supports advanced tasks such as adding annotations in Word, constructing financial models with Pivot Tables, and writing LaTeX equations in PDFs, while scaling to long-form outputs like 10,000-word papers or 100-page documents.
Four Operating Modes
K2.5 is available in four distinct modes: K2.5 Instant, K2.5 Thinking, K2.5 Agent, and K2.5 Agent Swarm (Beta). Each mode is optimized for different use cases, from quick responses to complex multi-step reasoning and parallel agent orchestration.
Availability & Access
Kimi K2.5 is available through multiple channels:
- Web Application: Accessible via Kimi.com with support for all four operating modes
- Mobile App: Available on iOS and Android through the Kimi App
- API Access: Available through Moonshot AI Open Platform for developers
- Kimi Code: Terminal-based coding assistant that integrates with VSCode, Cursor, Zed, and other IDEs
Agent Swarm is currently in beta on Kimi.com, with free credits available for high-tier paid users. Kimi Code is open-sourced and supports images and videos as inputs, automatically discovering and migrating existing skills and MCPs into your working environment.
Pricing & Plans
Kimi K2.5 follows a freemium pricing model with competitive API rates compared to major competitors. Compared to Gemini 2.5 Pro (input $1.25/1M tokens for ≤200k, output $10/1M tokens for ≤200k), K2.5 offers input at $0.60/1M tokens (cache miss), approximately 2.1× cheaper, and output at $3.00/1M tokens, approximately 3.3× cheaper. For prompts exceeding 200k tokens, K2.5's output pricing is approximately 5× cheaper than Gemini 2.5 Pro's $15/1M tokens.
API Pricing (per million tokens):
- Input (cache hit): $0.10
- Input (cache miss): $0.60
- Output: $3.00
- Context window: 256K tokens
Free Access: The model is available for free through Kimi.com and the Kimi App with usage limits. Specific quotas and limits are subject to change and should be verified on Kimi.com or the Kimi App for current policies. High-tier paid users receive free credits for Agent Swarm beta access.
Commercial Use: The model is open-source and available on Hugging Face, allowing for self-hosting and commercial deployment. For API access, pricing scales based on usage volume and can be customized for enterprise needs.
Pros & Cons
Pros
- Native multimodal capabilities enable simultaneous processing of text, images, and videos, opening new possibilities for visual understanding and code generation
- Agent Swarm technology reduces execution time by up to 4.5x through parallel agent orchestration, making complex tasks significantly faster
- State-of-the-art coding capabilities particularly in front-end development, with strong performance on visual debugging and UI generation
- Competitive pricing offers substantial cost savings compared to major competitors like Gemini 2.5 Pro
- Multiple access modes provide flexibility for different use cases, from instant responses to complex reasoning
- Open-source availability allows for self-hosting and customization, making it accessible for developers and enterprises
Cons
- Agent Swarm is in beta and may have stability issues or limitations during the preview period
- Agent Swarm beta limitations may include quota restrictions, rate limits, or queuing delays that affect availability for high-volume users
- Learning curve exists for effectively utilizing Agent Swarm capabilities and understanding parallel agent orchestration
- Vision capabilities require careful prompt engineering to achieve optimal results, especially for complex visual tasks
- API rate limits may apply for free-tier users, potentially restricting high-volume usage
Best For
- Front-end developers building interactive UIs who need to generate code from visual designs and implement complex animations
- Software engineers working with visual specifications, UI mockups, or video-based requirements who need autonomous visual debugging
- Content creators producing interactive web experiences who want to transform natural language descriptions into functional interfaces
- Knowledge workers handling large-scale office productivity tasks requiring multi-step reasoning over documents, spreadsheets, and presentations
- Research teams conducting complex multi-step research that benefits from parallel agent orchestration and reduced execution time
- Developers building agentic applications who need cost-effective multimodal AI with strong coding and reasoning capabilities
FAQ
What makes K2.5 different from K2 Thinking?
K2.5 introduces native multimodal capabilities, allowing it to process images and videos alongside text, while K2 Thinking is text-only. K2.5 also features Agent Swarm technology for parallel agent orchestration, whereas K2 Thinking focuses on sequential deep reasoning with long-horizon tool use. K2.5 excels at visual coding and front-end development, while K2 Thinking specializes in mathematical reasoning and complex problem-solving.
How does Agent Swarm work?
Agent Swarm uses a trainable orchestrator agent that automatically decomposes complex tasks into parallelizable subtasks. Each subtask is executed by dynamically instantiated subagents running concurrently, reducing end-to-end latency by up to 4.5x compared to sequential execution. The system can coordinate up to 100 sub-agents across 1,500 tool calls without predefined roles or workflows.
Is K2.5 available for local deployment?
Yes, K2.5 is open-source and available on Hugging Face, allowing for local deployment and self-hosting. The model has 1 trillion total parameters with 32 billion activated parameters per forward pass, requiring significant computational resources for local deployment.
What are the system requirements for using Agent Swarm?
Agent Swarm is currently in beta and available through Kimi.com with free credits for high-tier paid users. The feature requires API access or web application access. For local deployment, you'll need substantial computational resources to run the full model, though specific hardware requirements depend on your deployment configuration.
How does K2.5 compare to competitors in terms of cost?
K2.5 offers competitive pricing compared to Gemini 2.5 Pro. For standard usage (≤200k tokens), K2.5's input pricing ($0.60/1M tokens, cache miss) is approximately 2.1× cheaper than Gemini 2.5 Pro's $1.25/1M tokens, while output pricing ($3.00/1M tokens) is approximately 3.3× cheaper than Gemini's $10/1M tokens. For larger contexts (>200k tokens), K2.5's output pricing is approximately 5× cheaper than Gemini's $15/1M tokens. The model delivers strong performance on agentic benchmarks (HLE, BrowseComp, SWE-Verified) at a fraction of the cost of major competitors, making it an attractive option for cost-conscious developers and enterprises.
