OpenAI/gpt-5.5-file-analysis

gpt-5.5-file-analysis

OpenAI GPT 5.5 API for deep document reasoning across PDFs, reports, contracts, and spreadsheets. Stable and affordable for professional analysis workflows. Built for free testing and stable API workflows.

File Analysis

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gpt-5.5-file-analysisfile-analysis

Documentation

API Examples

JavaScriptPythoncURL

Submit Example

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 file and extract the key risks.' },
          {
            type: 'file',
            file: {
              filename: 'demo.pdf',
              file_data: 'https://example.com/demo.pdf'
            }
          }
        ]
      }
    ],
    stream: true,
    max_tokens: 4096
  })
});

const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = '';
let assistantText = '';

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  buffer += decoder.decode(value, { stream: true });
  const frames = buffer.split('\n\n');
  buffer = frames.pop() || '';

  for (const frame of frames) {
    const lines = frame.split('\n').filter(Boolean);
    let eventName = 'message';
    const dataLines = [];

    for (const line of lines) {
      if (line.startsWith('event:')) {
        eventName = line.slice(6).trim();
      } else if (line.startsWith('data:')) {
        dataLines.push(line.replace(/^data:\s*/, ''));
      }
    }

    const raw = dataLines.join('\n').trim();
    if (raw === '[DONE]') {
      console.log('\nFinal text:', assistantText);
      continue;
    }

    let payload;
    try {
      payload = JSON.parse(raw);
    } catch {
      continue;
    }

    if (eventName === 'error' || payload.error) {
      const msg = payload.error?.message ?? payload.message ?? 'Chat request failed';
      throw new Error(msg);
    }

    const delta = payload.choices?.[0]?.delta;
    if (delta?.content) {
      assistantText += delta.content;
      console.log(assistantText);
    }
  }
}

Submit Example

import json
import requests

response = requests.post(
    'https://api.flaq.ai/api/v1/chat/completions',
    headers={
        'Authorization': 'Bearer YOUR_API_KEY',
        'Accept': 'text/event-stream',
        'Content-Type': 'application/json',
    },
    json={
        'model': 'gpt-5.5-file-analysis',
        'messages': [
            {
                'role': 'user',
                'content': [
                    {'type': 'text', 'text': 'Summarize this file and extract the key risks.'},
                    {
                        'type': 'file',
                        'file': {
                            'filename': 'demo.pdf',
                            'file_data': 'https://example.com/demo.pdf'
                        }
                    },
                ],
            }
        ],
        'stream': True,
        'max_tokens': 4096,
    },
    stream=True,
)

response.raise_for_status()

event_name = 'message'
assistant_text = ''

for raw_line in response.iter_lines(decode_unicode=True):
    if not raw_line:
        event_name = 'message'
        continue

    if raw_line.startswith('event:'):
        event_name = raw_line.replace('event:', '', 1).strip()
        continue

    if raw_line.startswith('data:'):
        raw_data = raw_line.replace('data:', '', 1).strip()
        if raw_data == '[DONE]':
            print('\nFinal text:', assistant_text)
            continue

        payload = json.loads(raw_data)

        if event_name == 'error' or payload.get('error'):
            error = payload.get('error') or payload
            raise RuntimeError(error.get('message', 'Chat request failed'))

        choices = payload.get('choices') or []
        if choices:
            delta = choices[0].get('delta') or {}
            content = delta.get('content')
            if content:
                assistant_text += content
                print(content, end='', flush=True)

Submit Example

curl -N -X POST "https://api.flaq.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Accept: text/event-stream" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.5-file-analysis",
    "messages": [
      {
        "role": "user",
        "content": [
          { "type": "text", "text": "Summarize this file and extract the key risks." },
          {
            "type": "file",
            "file": {
              "filename": "demo.pdf",
              "file_data": "https://example.com/demo.pdf"
            }
          }
        ]
      }
    ],
    "stream": true,
    "max_tokens": 4096
  }'

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GPT 5.5 File Analysis Pricing

Parameters	Price	Original Price	Discount

Token: input	$5.0000 per 1M input tokens	-	Standard
Token: output	$30.0000 per 1M output tokens	-	Standard

README

Fast & Affordable GPT 5.5 File Analysis API (an advanced OpenAI model for complex reasoning and professional work)

GPT 5.5 File Analysis API on Flaq AI provides OpenAI API access for demanding developer, research, and business workflows that need reliable reasoning and production-ready output. This powerful, reliable, and flexible OpenAI API integration helps developers build file analysis experiences with flexible conversation context, document-aware prompting, supported tool workflows, and route-specific settings on Flaq AI. Built for production workloads, it gives teams a stable way to add advanced AI capability without managing model infrastructure or writing provider-specific plumbing from scratch.

Key Features of GPT 5.5 File Analysis API

Multi-File Document Understanding: Analyze uploaded files in one workflow and connect details across reports, briefs, specifications, and records.
Long-Context File Reasoning: Preserve document details across larger inputs while producing clear summaries, comparisons, and next-step recommendations.
Flexible API Controls: Use conversation context, document-aware prompting, supported tool workflows, and route-specific settings on Flaq AI to guide simple requests, deep analysis, and production automation.
Production Tool Support: Connect supported tool workflows, retrieval, automation, and advanced application features without surfacing low-level provider settings.
Cost-Effective Integration: Build file analysis features through Flaq AI with clear routing, stable model pages, and durable API messaging for production workflows.
Developer-Friendly Workflow: Use natural-language instructions, structured prompts, and route-specific inputs to move quickly from prototype to production.

How to Use GPT 5.5 File Analysis API for Document Analysis on Flaq AI

Input: Uploaded files plus natural-language instructions for extraction, summarization, comparison, or reasoning.
Output: Document summaries, extracted insights, comparisons, tables, decisions, or structured analysis from uploaded files.
Route Configuration: Designed for multi-document analysis workflows with file input support and flexible follow-up context.
Configuration: flexible conversation context, document-aware prompting, supported tool workflows, and route-specific settings on Flaq AI.
Capabilities: PDF and document analysis, spreadsheet-style reasoning, contract review, report summarization, and multi-file comparison through powerful, reliable, and flexible OpenAI API integration.

Best Use Cases for GPT 5.5 File Analysis API Integration

Research Report Summaries: Condense long reports, white papers, and briefing documents into executive-ready takeaways.
Contract & Policy Review: Compare clauses, flag important obligations, and summarize risks for legal, finance, or operations teams.
Data & Spreadsheet Analysis: Inspect structured files, find trends, and explain tables in plain language for business users.
Knowledge Base Processing: Turn uploaded manuals, docs, and support articles into searchable answers and reusable summaries.
Multi-Document Comparison: Compare versions, proposals, requirements, or vendor documents with consistent criteria and clean output.

Note Please ensure your prompts, uploaded files, and application workflows comply with OpenAI safety and usage guidelines. If an error occurs, review the input for restricted content, simplify the request, and try again.

GPT 5.5 File Analysis vs Competitors: Comparative Analysis

GPT 5.5 vs. Claude Opus 4.7 Claude Opus 4.7 is strong for careful long-horizon reasoning and Claude-style agent workflows. GPT 5.5 offers OpenAI-native behavior, broad application tooling, and a familiar API path for teams already building on the OpenAI ecosystem.
GPT 5.5 vs. Claude Sonnet 4.6 Claude Sonnet 4.6 focuses on a fast balance of intelligence and latency. GPT 5.5 is a strong choice when developers want OpenAI model behavior, flexible chat workflows, and integrated file analysis capabilities on Flaq AI.
GPT 5.5 vs. Gemini Gemini models are attractive for Google-native multimodal and workspace-adjacent use cases. GPT 5.5 differentiates with OpenAI-style reasoning, dependable text quality, and production-friendly integration.
GPT 5.5 vs. DeepSeek Reasoner DeepSeek Reasoner is valued for affordable reasoning experiments. GPT 5.5 provides a managed API experience for professional file analysis workflows, especially where reliability and ecosystem support matter.
GPT 5.5 vs. Llama Llama models give teams open-model flexibility and deployment control. GPT 5.5 removes infrastructure overhead and delivers a scalable API for developers who need fast integration and consistent output quality.