Kimi 2.7 API

Complete API reference for Kimi 2.7 LLM models powered by Moonshot AI.

Model Variants

This API supports two model variants:

Quick Comparison

Kimi 2.7 Text to Text

Endpoint

httpCopy
POST /api/v1/chat/completions

Request Parameters

Required

Message Support

Optional

Example Request

javascriptCopy
const response = await fetch('https://api.flaq.ai/api/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Accept': 'text/event-stream',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'kimi-2.7-text-to-text',
    messages: [
      {
        role: 'user',
        content: 'Write a concise Java beginner tutorial outline.'
      }
    ],
    stream: true,
    max_tokens: 2000,
    top_p: 0.9,
    top_k: 50
  })
});

Kimi 2.7 Image to Text

Endpoint

httpCopy
POST /api/v1/chat/completions

Request Parameters

Required

Message Support

Optional

Example Request

javascriptCopy
const response = await fetch('https://api.flaq.ai/api/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Accept': 'text/event-stream',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'kimi-2.7-image-to-text',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'What is roughly shown in this image?' },
          {
            type: 'image_url',
            image_url: {
              url: 'https://example.com/sample-image.jpg'
            }
          }
        ]
      }
    ],
    stream: true,
    max_tokens: 300
  })
});

Response Format

Kimi 2.7 LLM models return OpenAI-compatible completion responses. With stream: true, the response is Server-Sent Events; with stream: false, the response is a single JSON object.

Best Practices

• Use concise system instructions for task-specific behavior.
• For image understanding, pair a clear question with image attachment content in messages[].content.
• Tune top_p and top_k only when you need stricter or more varied output.