Moonshot AI API provides programmatic access to the Kimi series of large language models (LLMs), developed by Moonshot AI. It is designed to empower developers with advanced natural language processing capabilities, specifically focusing on long-context understanding, complex reasoning, and multimodal integration. The API is built with a high degree of compatibility with industry-standard interfaces, notably the OpenAI SDK, allowing for seamless integration into existing AI infrastructures.

The core of the Moonshot AI platform is the Kimi K2.5 model, which supports context windows up to 256,000 tokens. This makes it one of the most capable models for processing massive documents, extensive codebases, and multi-turn conversations without losing context.

What is the Moonshot AI API?

The Moonshot AI API is an HTTP-based interface that allows applications to interact with Kimi models for text generation, image analysis, and complex reasoning tasks. The API endpoint is located at https://api.moonshot.ai/v1, and it utilizes the standard Chat Completions format.

Key highlights of the API include:

  • Long Context Windows: Support for up to 256k tokens in a single request.
  • OpenAI Compatibility: Developers can use the standard OpenAI Python or Node.js libraries by simply changing the base_url and api_key.
  • Reasoning Capabilities: Advanced models like Kimi K2 Thinking provide intermediate "chain-of-thought" reasoning steps.
  • Native Multimodality: Support for text, image, and video inputs to handle visual understanding tasks.

Exploring the Kimi Model Matrix

The Moonshot AI ecosystem offers a variety of models tailored for different performance and cost requirements. Understanding the distinctions between these models is crucial for optimizing application performance.

Kimi K2.5: The Flagship Multimodal Model

Kimi K2.5 represents the pinnacle of Moonshot AI's research. It is a Mixture-of-Experts (MoE) architecture model that excels in agentic tasks, coding, and general intelligence.

  • Context Capacity: 256,000 tokens.
  • Strengths: Best-in-class performance in Chinese and English, vision capabilities, and high-speed output.
  • Use Case: Complex autonomous agents, large-scale data synthesis, and multimodal chatbots.

Kimi K2 Thinking: Deep Reasoning Engine

For tasks requiring meticulous logic—such as mathematical proofs, complex code debugging, or strategic planning—Kimi K2 Thinking is the preferred choice.

  • Reasoning Mode: It generates internal "thoughts" before providing the final answer. These thoughts are returned in a specific reasoning_content field.
  • Token Management: It requires a larger max_completion_tokens setting (recommended ≥ 16,000) to accommodate the detailed reasoning process.

Moonshot-v1 Series: Balanced Efficiency

The Moonshot-v1 series is categorized by context length, providing a cost-effective solution for standard text generation tasks.

  • v1-8k: Optimized for short interactions and quick responses.
  • v1-32k: Suitable for summarizing long articles or medium-length documents.
  • v1-128k: Designed for extremely long inputs, such as entire books or legal contracts.

How to Get Started with Moonshot AI API

Integration is straightforward due to the API's adherence to standard protocols. Developers can use the official Kimi Open Platform to manage API keys and monitor usage.

Authentication and Setup

To authenticate, you must include your API key in the HTTP header of every request: Authorization: Bearer $MOONSHOT_API_KEY

For Python developers, the integration looks like this: