GPT 5.5 API

Complete API reference for GPT 5.5 LLM models powered by OpenAI.

Model Variants

This API supports four model variants:

Quick Comparison

GPT 5.5 Text to Text

Endpoint

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POST /api/v1/chat/completions

Request Parameters

Required

Message Support

Optional

Example Request

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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: 'gpt-5.5-text-to-text',
    messages: [
      {
        role: 'user',
        content: 'Write a short launch announcement for a new AI feature.'
      }
    ],
    stream: true,
    max_tokens: 2048
  })
});

GPT 5.5 Image to Text

Endpoint

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POST /api/v1/chat/completions

Request Parameters

Required

Message Support

Optional

Example Request

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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: 'gpt-5.5-image-to-text',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Describe the image and extract any visible text.' },
          {
            type: 'image_url',
            image_url: {
              url: 'https://example.com/sample-image.jpg'
            }
          }
        ]
      }
    ],
    stream: true,
    max_tokens: 2048
  })
});

GPT 5.5 Web Search

Endpoint

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POST /api/v1/chat/completions

Request Parameters

Required

Message Support

Optional

Example Request

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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: 'gpt-5.5-web-search',
    messages: [
      {
        role: 'user',
        content: 'Find recent product launch trends in AI developer tools.'
      }
    ],
    stream: true,
    max_tokens: 2048
  })
});

GPT 5.5 File Analysis

Endpoint

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POST /api/v1/chat/completions

Request Parameters

Required

Message Support

Optional

Example Request

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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: 'gpt-5.5-file-analysis',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Summarize this document and list the key action items.' },
          {
            type: 'file',
            file: {
              filename: 'demo.pdf',
              file_data: 'https://example.com/demo.pdf'
            }
          }
        ]
      }
    ],
    stream: true,
    max_tokens: 2048
  })
});

Response Format

GPT 5.5 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.

Successful Response

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data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1710000000,"model":"gpt-5.5-image-to-text","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1710000000,"model":"gpt-5.5-image-to-text","choices":[{"index":0,"delta":{"content":"The image shows"},"finish_reason":null}]}

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1710000000,"model":"gpt-5.5-image-to-text","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1710000000,"model":"gpt-5.5-image-to-text","choices":[],"usage":{"prompt_tokens":12,"completion_tokens":8,"total_tokens":20}}

data: [DONE]

Error Response

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event: error
data: {"error":{"message":"API requests too frequent, exceeding rate limit","type":"rate_limit_error","code":"1302","param":null}}

Best Practices

1. Keep messages structured: Use messages[] for conversation history instead of flattening context into one prompt.
2. Attach files and images to messages: Put image_url and file parts inside the message that introduced them.
3. Use SSE parsing: Append choices[0].delta.content for incremental display and treat data: [DONE] as successful completion.
4. Send only supported inputs: Match text, image, and file parts to the selected model variant.
5. Store request state locally: Chunks include a completion id, but clients should still route chunks through the active request state.