Moonshot AI API

Kimi K2.6 is Moonshot AI's latest open-weight multimodal model, built on a 1-trillion-parameter mixture-of-experts architecture with a 256K context window. It excels at agentic coding and long-horizon execution, supporting sustained autonomous workflows with 4,000+ tool calls across languages like Rust, Go, and Python. On key benchmarks, it scores 58.6 on SWE-Bench Pro, 54.0 on HLE with Tools, and 50.0 on Toolathlon — competitive with GPT-5.4 and Claude Opus 4.6 on coding and agent tasks, though trailing them on pure reasoning. The model accepts text, image, and video input, supports both thinking and non-thinking modes, and offers an OpenAI-compatible API. It's a strong pick for developers building multi-step agentic workflows and complex software engineering pipelines.

Kimi K2.5 is Moonshot AI's most capable open-source model, a natively multimodal (vision + text) trillion-parameter MoE with 32B active parameters released in January 2026. Built through continual pretraining on ~15 trillion mixed visual and text tokens atop the K2 base, it supports both thinking and instant modes with a 256K context window. It scored 76.8% on SWE-bench Verified, 96.1% on AIME 2025, and 50.2% on Humanity's Last Exam with tools — outperforming Claude Opus 4.5 and GPT-5.2 on the latter. Its standout feature is Agent Swarm, which coordinates up to 100 parallel sub-agents for complex tasks. K2.5 excels at vision-to-code generation, frontend development from screenshots, and large-scale agentic workflows, making it a strong choice for developers building multimodal AI agents.

Kimi K2 Thinking is Moonshot AI's reasoning-enhanced variant of Kimi K2, trained to interleave step-by-step chain-of-thought with dynamic tool calls. It supports up to 200–300 sequential tool calls without drift, enabling deep autonomous research, coding, and analysis workflows. It achieves 71.3% on SWE-bench Verified, 44.9% on Humanity's Last Exam (with tools), 60.2% on BrowseComp, and 99.1% on AIME 2025 (with Python) — placing it among the top open-source thinking models. It uses native INT4 quantization and a 256K context window. K2 Thinking is designed for complex, multi-step tasks where extended reasoning and sustained tool orchestration matter more than low-latency responses.

Kimi K2 0905 is Moonshot AI's September 2025 update to the original Kimi K2, delivering enhanced coding performance and improved tool-calling reliability. It shares the same 1-trillion-parameter MoE architecture with 32B active parameters but doubles the context window from 128K to 256K tokens. Key improvements include stronger frontend development capabilities — producing cleaner, more polished UI code for frameworks like React, Vue, and Angular — along with better integration across popular agent scaffolds. It scored 53.7% Pass@1 on LiveCodeBench. This version is ideal for developers who want K2's agentic strengths with improved real-world coding quality and longer context support for large codebases.

Kimi K2 is a trillion-parameter Mixture-of-Experts model by Moonshot AI, activating 32 billion parameters per token. Designed as a non-thinking model optimized for agentic capabilities, it excels at tool use, code generation, and autonomous problem-solving with a 128K token context window. On benchmarks, K2 scored 65.8% on SWE-bench Verified, 75.1% on GPQA-Diamond, 49.5% on AIME 2025, and 66.1 on Tau2-bench — surpassing most open- and closed-source models in non-thinking settings. It ranked as the #1 open-source model on the LMSYS Arena leaderboard upon release in July 2025. K2 is well suited for developers building AI agents and tool-calling pipelines who need strong coding and reasoning without extended thinking overhead.

Kimi Dev 72B is a 72-billion-parameter coding model by Moonshot AI, purpose-built for software engineering tasks like bug fixing, code generation, and unit test creation. It is based on the Qwen 2.5-72B architecture and fine-tuned with large-scale reinforcement learning on real-world GitHub issues and pull requests. The model achieved 60.4% on SWE-bench Verified, setting a state-of-the-art result among open-source models at the time of its June 2025 release. It uses a two-stage framework — file localization followed by precise code editing — that mirrors how human developers approach issue resolution. Kimi Dev 72B is a strong pick for automated code repair and test generation workflows where a specialized coding model outperforms general-purpose alternatives.

Moonshot V1 8K Vision Preview is a multimodal variant of Moonshot AI's V1 model that accepts both image and text inputs within an 8,000-token context window. It can interpret screenshots, charts, UI mockups, and photos, returning text-based analysis. This makes it useful for tasks like image captioning, visual Q&A, and lightweight document understanding where the source material includes visual elements. As a preview model, it may see changes before a stable release. The API follows the OpenAI-compatible content array format with image_url blocks, making integration straightforward for developers already using similar patterns.

Moonshot V1 32K Vision Preview is a multimodal model from Moonshot AI that processes both images and text within a 32,000-token context window. It extends the base 32K model with the ability to interpret visual inputs — including screenshots, diagrams, charts, and scanned documents — and return text-based responses. This is useful for workflows that combine visual context with moderate-length text, such as analyzing annotated documents or explaining UI designs. As a preview release, the vision capabilities may evolve. The API accepts the standard OpenAI-compatible content array format for multimodal inputs.

Moonshot V1 128K Vision Preview is Moonshot AI's largest-context multimodal model in the V1 series, supporting both image and text inputs within a 128,000-token context window. It combines the long-context strength of the 128K text model with visual understanding capabilities. This makes it well-suited for processing large multimodal documents — think lengthy reports with embedded charts, multi-page scanned PDFs, or extensive UI review sessions. As a preview model, vision features may be refined over time. The API uses the standard OpenAI-compatible format for multimodal content, making it a drop-in addition to existing workflows.

Moonshot V1 Auto is a smart routing layer from Moonshot AI that automatically selects the most cost-efficient context window — 8K, 32K, or 128K — based on the token count of each request. It uses the same underlying Moonshot V1 model as the fixed-context variants, so there is no difference in output quality. The routing simply ensures you're billed at the lowest applicable tier for each call, eliminating the need to manually choose a context size or overpay for unused capacity. Usage is identical to the other Moonshot V1 models — just set the model ID to `moonshot-v1-auto` and the platform handles the rest. Ideal for applications with variable-length inputs.

Moonshot V1 8K is a general-purpose text generation model from Moonshot AI, the Beijing-based company behind the Kimi assistant. It supports an 8,000-token context window, making it the most lightweight option in the Moonshot V1 family. All Moonshot V1 models share the same underlying capabilities — the only difference is the maximum context length. This variant is best suited for short-form tasks like single-turn Q&A, classification, and concise summaries where you want to minimize token costs. The API is OpenAI-compatible, so you can integrate it by swapping the base URL and API key in any existing OpenAI SDK setup. The model handles both English and Chinese well.

Moonshot V1 32K is a general-purpose text generation model from Moonshot AI with a 32,000-token context window. It sits in the middle of the Moonshot V1 family, balancing context capacity with cost. All Moonshot V1 variants share the same model quality — only the context length differs. The 32K window is well-suited for multi-turn conversations, medium-length document summarization, and tasks where inputs and outputs together exceed 8K tokens but don't require the full 128K capacity. The API is fully OpenAI-compatible, supporting streaming, tool calling, and standard chat completion parameters. The model performs well in both English and Chinese.

Moonshot V1 128K is a long-context text generation model from Moonshot AI, offering a 128,000-token context window. Moonshot AI was one of the first companies to ship native 128K-token context support when the Kimi chatbot launched in 2023. This variant is designed for tasks that demand large input windows: processing entire codebases, analyzing lengthy legal or financial documents, or maintaining very long conversation histories. It shares the same model quality as the 8K and 32K variants — context length is the only differentiator. The API is OpenAI-compatible and supports streaming, tool calling, and context caching for reduced latency and cost on repeated prompts.