Kimi AI | Multimodal LLM with Agent Swarm & 256K Context
Kimi by Moonshot AI: frontier multimodal LLM with native vision, 256K context, Agent Swarm technology. MMLU 92.0, HumanEval 99.0. $0.60/$2.50 per M tokens.
Kimi (Moonshot AI) is an open-source multimodal AI with a 128K token context window. The April 2026 Kimi-K2.6 release features 1 trillion parameters with advanced attention optimizations, setting new benchmarks for open-source models. Free to use via kimi.ai; API access priced per token. Supports vision, reasoning, and agent-swarm multi-task workflows.
About Kimi
Kimi is a frontier-class AI assistant developed by Moonshot AI, featuring a native multimodal architecture that seamlessly integrates visual and text understanding with advanced agentic capabilities. Built on the Kimi K2.5 model with 1 trillion total parameters (32 billion activated) using a Mixture-of-Experts architecture, Kimi excels at complex reasoning, coding, document analysis, and autonomous multi-step tasks. The platform offers both a consumer-facing chat interface and a developer API, with support for thinking modes, instant responses, Agent Swarm technology for parallel task execution, and extended context windows up to 256K tokens. Kimi differentiates itself through aggressive pricing (starting at $0.60/$2.50 per million tokens for API), automatic context caching that reduces costs by 75%, and exceptional benchmark performance across MMLU (92.0), HumanEval (99.0), and mathematical reasoning tasks. The platform natively supports vision inputs through its MoonViT-3D vision encoder, enabling visual coding from UI designs and video demonstrations. Kimi's Agent Swarm feature enables self-directed, coordinated task decomposition and parallel execution across dynamically instantiated domain-specific agents. The tool is positioned as an open-source model under a Modified MIT license, making it suitable for researchers, developers, enterprises, and individual users seeking cost-effective frontier-quality AI capabilities with enterprise-grade reasoning, coding, and visual understanding.
Pricing
Free tier (Adagio) with unlimited basic chat but limited agent usage. Consumer subscriptions: Andante $19/month (¥49 CNY), Moderato/Allegretto $39-79/month (¥99-199 CNY), Vivace $159-199/month. API pricing: $0.60 per million input tokens, $2.50 per million output tokens; cached input tokens $0.15/M (75% discount). Turbo model: $1.15/$8.00/M. Automatic $5 voucher when cumulative recharge reaches $5.
Key Features
- Kimi-K2.6 (1 Trillion Parameters): Released April 2026, Kimi-K2.6 is Moonshot AI's latest open-source model with 1 trillion parameters and advanced attention optimizations, setting a new benchmark for open-source multimodal performance.
- Native Multimodality: Pre-trained on vision and language tokens with advanced visual understanding, enabling code generation from UI designs and visual task comprehension
- Agent Swarm Technology: Decomposes complex tasks into parallel sub-tasks executed by dynamically instantiated, domain-specific agents for efficient multi-step problem solving
- Extended Context Window: Supports 256K token context window enabling processing of entire documents, books, and extended conversations without losing context
- Thinking & Instant Modes: Offers both deep reasoning mode for complex problems and instant mode for quick responses, with configurable reasoning depth
- OpenAI-Compatible API: Drop-in replacement for OpenAI API with automatic context caching, reducing input costs by 75% on repeated content
- 300-Step Tool Calling: Supports autonomous execution of up to 300 sequential tool calls for complex workflows including web search, file operations, and system commands
Pros
- Exceptional benchmark performance with MMLU 92.0, HumanEval 99.0, and MATH-500 98.0, competing with frontier models at fraction of cost
- Aggressive pricing at $0.60/$2.50 per million tokens with automatic 75% context caching, making it 4-17x cheaper than GPT-5.4
- Native multimodal capabilities enabling visual coding, chart understanding, and video processing without separate vision models
- Open-source under Modified MIT license with publicly available weights and comprehensive documentation
- OpenAI-compatible API enabling seamless migration and integration with existing OpenAI tooling
Cons
- Chinese-first interface and primary focus on Chinese market may limit international support and documentation
- Younger ecosystem with less established enterprise support compared to OpenAI and Anthropic
- Data residency concerns for users requiring non-China infrastructure (Moonshot AI based in Beijing)
- Limited established integrations compared to competitor platforms with longer market presence
Frequently Asked Questions
What is Kimi K2.5 and how does it differ from K2?
Kimi K2.5 is the latest multimodal version of Kimi released in January 2026, featuring native vision capabilities through a 400M-parameter vision encoder called MoonViT-3D. K2.5 can process images and video natively, enabling visual coding from UI designs and autonomous visual task execution. Both K2 and K2.5 use a 1T parameter MoE architecture with 32B activated parameters and 256K context, but K2.5 excels at visual understanding tasks while K2 remains the standard model for text-only workloads.
How much does Kimi cost and what are the pricing tiers?
Kimi offers three pricing models: (1) Free consumer tier (Adagio) with unlimited basic chat but limited agent usage; (2) Consumer subscriptions ranging from Andante ($19/month) to Vivace ($159-199/month), each unlocking more agent quotas and research credits; (3) Developer API at $0.60/$2.50 per million input/output tokens with automatic 75% context caching discounts. Pay-as-you-go minimum is $1 recharge, with cumulative spend unlocking higher rate limits and a $5 bonus voucher at $5 recharge.
How does Kimi's Agent Swarm technology work?
Agent Swarm enables Kimi to decompose complex tasks into parallel sub-tasks executed by dynamically instantiated, domain-specific agents. Instead of processing tasks sequentially, the orchestrator agent delegates work to multiple specialized agents that run concurrently, dramatically speeding up multi-step workflows. This is particularly powerful for research, document analysis, coding tasks, and autonomous web browsing where multiple parallel operations can be executed simultaneously.
Is Kimi open-source and can I run it locally?
Yes, Kimi K2 and K2.5 are open-source under a Modified MIT license with weights publicly available on Hugging Face and GitHub. You can download, fine-tune, and run them locally using inference engines like vLLM or SGLang. However, the consumer chat interface and commercial API remain proprietary Moonshot AI services. Local deployment is ideal for researchers and teams with on-premise infrastructure requirements.
What makes Kimi cheaper than GPT-4 and Claude?
Kimi's low cost stems from its Mixture-of-Experts (MoE) architecture, which activates only 32 billion of its 1 trillion parameters per request, dramatically reducing computational overhead. Moonshot AI also employs aggressive pricing for rapid market adoption. The automatic context caching feature reduces input costs by 75% on repeated content, providing additional savings for applications processing similar documents or maintaining long conversation histories. Combined, these factors make Kimi 4-17x cheaper than GPT-5.4 while delivering competitive benchmark performance.
Can I use Kimi as a drop-in replacement for OpenAI's API?
Yes, Kimi's API is fully OpenAI-compatible, allowing you to use it as a drop-in replacement by simply changing the endpoint to api.moonshot.ai/v1 and providing your Moonshot API key. All OpenAI SDKs (Python, JavaScript, Go, etc.) work without modification. This compatibility eliminates migration friction and allows developers to test Kimi's performance and pricing with minimal integration effort.
What are Kimi's benchmark scores and how do they compare?
Kimi K2.5 achieves exceptional scores: MMLU 92.0 (general knowledge), HumanEval 99.0 (coding—highest on any leaderboard), MATH-500 98.0 (mathematics), GPQA Diamond 87.6, and Chatbot Arena 1447. These scores place it among the top frontier models and exceed many closed-source models from OpenAI and Google, while costing significantly less per token. K2 (text-only) scores 78.6 MMLU and 94.5 HumanEval, still competitive with most open-source models.