Kimi
Moonshot AI’s conversational assistant with a 256K-token context window, strong bilingual Chinese-English support, and notably low API pricing. Built on a 1-trillion-parameter Mixture-of-Experts architecture with open-weight model releases.
Overview
Kimi is the consumer-facing AI assistant from Moonshot AI, a Beijing-based company founded in 2023 and valued at approximately $18 billion as of March 2026. The platform runs on Kimi K2.6 – a 1-trillion-parameter Mixture-of-Experts model released April 20, 2026, which activates only 32 billion parameters per request, keeping inference costs low. Its 256K-token context window is larger than GPT-4o (128K) and comparable to Claude 3.5 Sonnet, making it practical for long-document analysis. The API is OpenAI SDK-compatible and priced at $0.60/$2.50 per million input/output tokens, significantly below OpenAI and Anthropic’s comparable offerings. Kimi also offers an open-source coding CLI (Kimi Code) and Agent Swarm, which coordinates up to 300 parallel sub-agents for parallelisable tasks. For teams with compliance requirements, Moonshot AI’s Chinese jurisdiction is a relevant consideration alongside the technical capabilities.
Core Features
256K-Token Context Window
Kimi K2.6 supports a 256K-token context window, larger than GPT-4o’s 128K and on par with Claude 3.5 Sonnet’s 200K (for reference, 256K tokens covers roughly 200,000 words of English text, or a full-length novel). This makes it practical for analysing long contracts, research papers, or large codebases without splitting content across sessions. Context length is a genuine differentiator at Kimi’s price point, though Google Gemini 3.1 Pro offers a larger 2M-token window via API for users who need to go further.
Mixture-of-Experts Architecture
Kimi K2.6 uses a 1-trillion-parameter MoE design with only 32 billion parameters activated per forward pass (across 384 experts, 8 selected per token). This means you get the knowledge capacity of a large dense model while Moonshot pays the inference cost of a ~32B model, the main reason API pricing is so low. The tradeoff is that MoE models can underperform dense models on tasks requiring sustained, uniform reasoning across all parameters.
Web Search & Browsing
Real-time web search with source synthesis. Kimi can browse multiple pages, cross-check facts, and compile reports with citations, bridging knowledge cutoff gaps with live data. Web search is available on the free tier. Note: each search call costs $0.005 on the API, in addition to token costs for search results added to context.
Vision & Document Analysis
Upload and analyse images, PDFs, Word documents, Excel spreadsheets, and presentation files. Supports table extraction, chart reading, screenshot analysis, and diagram interpretation. K2.6 is natively multimodal, though it ranks #26 out of 115 models on multimodal benchmarks, capable for standard document work but not a specialist vision model.
Coding & Kimi Code CLI
Write, debug, and explain code across major programming languages including Python, JavaScript, Go, and Rust. K2.6 scores 58.6% on SWE-Bench Pro, above GPT-5.4 (57.7%) on that benchmark. Kimi Code, an open-source CLI launched January 2026, brings Kimi into the terminal, competing directly with Claude Code. Agent Swarm coordinates up to 300 parallel sub-agents on K2.6 (up from 100 on K2.5), cutting execution time on parallelisable tasks like batch testing and large refactors.Kimi+ Custom Agents
Create and share specialised AI agents with custom knowledge bases, instructions, and tool integrations. Browse the Kimi+ marketplace for pre-built agents spanning academic research, legal analysis, creative writing, and business intelligence. Available on paid tiers.
Use Cases
Long Document Analysis
Upload long contracts, annual reports, academic theses, or technical manuals for analysis within a single session. The 256K context window handles documents that shorter-context models must split into chunks, avoiding the coherence loss that comes with fragmented processing.
Chinese-English Bilingual Work
Translate, localise, and create content in both Chinese and English. Kimi was originally built for Chinese-language users and maintains strong performance in both languages. Useful for cross-border business communications, academic collaboration, and content targeting audiences in both markets. Performance on Chinese-language tasks is generally stronger than Western-first models.
Cost-Sensitive API Workloads
At $0.60/$2.50 per million input/output tokens, Kimi K2.6 is significantly cheaper than GPT-5.4 and Claude Sonnet 4.6. The API is OpenAI SDK-compatible, switching requires only a base URL change. Automatic context caching reduces input costs by 75% on repeated context. For high-volume applications, the cost difference is substantial: a SaaS app processing 100M tokens/month would pay roughly $310 with Kimi vs $4,000+ with GPT-5.4.
Research Synthesis & Literature Review
Process multiple research papers in a single session, extracting methodologies, comparing findings, and identifying gaps. The long context window means dozens of papers can be loaded simultaneously rather than queried sequentially. Useful for academic researchers and analysts who regularly work with large document sets.
Pricing
- Kimi K2.6 access (rate-limited)
- 256K-token context window
- Standard response speed
- Web search capability
- Document upload (limited daily quota)
- Kimi+ agent marketplace access
- Full Kimi K2.6 access (higher limits)
- Priority response speed
- Deep Research access
- Kimi Code (coding CLI) access
- Advanced document analysis
- Kimi+ custom agents
- Slides & Websites generation tools
- Everything in Moderato
- Agent Swarm: up to 300 parallel sub-agents
- More Kimi Code credits per month
- Kimi Claw cloud deployment (higher tiers)
- Larger Professional Data quotas
- API: $0.60/$2.50 per 1M tokens (separate from membership)
- 75% automatic cache discount on repeated context
Why Choose Kimi?
Kimi’s main practical advantages are API pricing and context length at that price point. Its primary trade-offs are a younger ecosystem, a Chinese-first interface, and less established enterprise support than OpenAI or Anthropic. The right fit depends heavily on your workload type and compliance requirements.
Larger Context Window Than Most Comparable-Price Models
At 256K tokens, Kimi K2.6’s context window is 2× GPT-4o’s 128K and larger than Claude 3.5 Sonnet’s 200K, at a lower price point than both. For workflows involving long documents, research papers, or large codebases, the longer context reduces the need to split and re-summarise content across multiple sessions. Google Gemini 3.1 Pro offers a larger 2M-token API context for users who need more.
Stronger Chinese-Language Performance Than Western-First Models
Kimi was originally built for Chinese-language users and has maintained strong bilingual performance as it expanded internationally. For workflows that require Chinese-English translation, localisation, or content creation targeting Chinese audiences, Kimi generally outperforms Western-first models like GPT or Claude on Chinese-specific tasks, based on publicly available benchmark comparisons.
API Pricing Significantly Below OpenAI and Anthropic
At $0.60/$2.50 per million input/output tokens, Kimi K2.6 API pricing is 4–17× cheaper than GPT-5.4 and 5–6× cheaper than Claude Sonnet 4.6. The API is OpenAI SDK-compatible, requiring only a base URL change to switch. For production applications processing high token volumes, the cost difference is material. Automatic context caching further reduces repeated input costs by 75%.
Open-Weight Model with Self-Hosting Option
K2.6 weights are available on Hugging Face under a Modified MIT License, allowing self-hosting via vLLM, SGLang, KTransformers, or TensorRT-LLM. This is a meaningful option for teams with data-residency requirements or those wanting to avoid per-token API costs at scale. ChatGPT and Claude do not offer open weights; this positions Kimi alongside DeepSeek as one of the few frontier-quality open-weight models.
Product Details
Pros
- 256K-token context window, larger than GPT-4o and Claude 3.5 Sonnet at this price
- API 4–17× cheaper than GPT-5.4; OpenAI SDK-compatible
- Open-weight model (K2.6) – self-hostable under Modified MIT
- Strong Chinese-English bilingual capability
- Kimi Code CLI for terminal-based coding workflows
- Agent Swarm: up to 300 parallel sub-agents on K2.6
- 75% automatic context caching discount on API
Cons
- Smaller third-party ecosystem than OpenAI or Anthropic
- Moonshot AI is a Chinese company, relevant for teams with data-residency or geopolitical compliance requirements
- Below GPT-5.4 on GPQA-Diamond and AIME 2026 math benchmarks
- Multimodal capability is limited (ranked #26/115 on vision benchmarks)
- International subscription starts at $19/mo (Moderato), not the $8 sometimes cited, which is a China-region price
- API and membership billed separately, easy to confuse for new users
- Enterprise features and SLA less mature than OpenAI or Anthropic