OpenAI/gpt-6-sol-web-search

gpt-6-sol-web-search

Try GPT 6 Sol API by OpenAI for web search, online research, and answers using current information through Flaq AI's stable, affordable API with streaming.

Web Search

Related Gpt 6 Sol Models

gpt-6-sol-text-to-texttext-to-text

gpt-6-sol-image-to-textimage-to-text

gpt-6-sol-web-searchweb-search

gpt-6-sol-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-6-sol-web-search',
    messages: [
      {
        role: 'user',
        content: 'Find the latest public information about Flaq AI and summarize it for a product brief.'
      }
    ],
    stream: true,
    max_tokens: 2048
  })
});

if (!response.ok) {
  const errorBody = await response.json().catch(() => null);
  throw new Error(errorBody?.error?.message ?? errorBody?.message ?? `HTTP ${response.status}`);
}

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(/\r?\n\r?\n/);
  buffer = frames.pop() || '';

  for (const frame of frames) {
    const lines = frame.split(/\r?\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-6-sol-web-search',
        'messages': [
            {
                'role': 'user',
                'content': 'Find the latest public information about Flaq AI and summarize it for a product brief.',
            }
        ],
        'stream': True,
        'max_tokens': 2048,
    },
    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-6-sol-web-search",
    "messages": [
      {
        "role": "user",
        "content": "Find the latest public information about Flaq AI and summarize it for a product brief."
      }
    ],
    "stream": true,
    "max_tokens": 2048
  }'

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GPT 6 Sol Web Search Pricing

Parameters	Price	Original Price	Discount

Token: input	$2.0000 per 1M input tokens	-	Standard
Token: output	$10.0000 per 1M output tokens	-	Standard

README

In-Depth Research GPT 6 Sol Web Search API (OpenAI Evidence-Based Web Retrieval)

GPT 6 Sol Web Search API combines GPT-6 Sol reasoning with web retrieval for research and market analysis on Flaq AI. This web search API integration helps developers and research teams investigate changing topics, compare public sources, and prepare briefings from conversation messages. Teams should check important claims against the original sources before relying on the result.

Key Features of GPT 6 Sol Web Search API

Source-Aware Web Synthesis: Combine current web retrieval with GPT 6 Sol reasoning to organize findings into briefings for review against the original sources.
Multi-Source Comparison: Compare public sources to identify agreements, conflicting statements, and emerging developments that need closer review.
Multimodal Research Inquiries: Attach supported images or reference documents to your search prompt to provide supplementary visual or contextual evidence.
Multi-Turn Exploration: Include relevant earlier messages when refining, expanding, or redirecting a research question across requests.
Structured Knowledge Delivery: Organize unstructured web data into executive summaries, comparison tables, chronological timelines, or competitive teardowns.
Real-Time Streaming Output: Stream search-grounded analytical summaries progressively via server-sent events to power interactive research tools.

How to Use GPT 6 Sol Web Search API for In-Depth Research on Flaq AI

Input: A research topic or analytical query submitted through conversation messages, optionally accompanied by supported images and documents for additional context.
Output: Research summaries and comparisons based on retrieved public information and any supplied attachments.
Output Length: Optionally set a maximum output token count for short updates or detailed research summaries. No default maximum is specified for this parameter; model output limits still apply.
Streaming: Receive the generated research response incrementally for immediate user presentation.
Capabilities: Web retrieval, source comparison, research synthesis, and multimodal inquiry exploration via Flaq AI API integration.

Best Use Cases for GPT 6 Sol Web Search API Integration

Competitive Landscape and Market Scans: Track competitor product launches, pricing shifts, public announcements, and market trends across global industries.
Regulatory and Legal Policy Tracking: Summarize public announcements and compare sources to identify changes that warrant expert review.
Technical and Scientific Literature Scans: Gather current research breakthroughs, technical releases, and documentation updates, distilling them into actionable notes.
Fact-Checking and Diligence Workflows: Compare business claims, press coverage, or executive statements with retrieved sources and attached diligence memos for further verification.
Interactive Research Copilots: Build autonomous research assistants that formulate multi-step search strategies, summarize findings, and respond to follow-up inquiries.

Note Ensure prompts, attached assets, and retrieval tasks comply with OpenAI's usage policies, Flaq AI terms, and applicable web terms of service. If an error occurs, narrow the query scope, eliminate restricted terms or attachments, and try again.

GPT 6 Sol Web Search vs Competitors: Comparative Analysis

GPT 6 Sol Web Search vs. GPT 6 Astra Web Search
GPT 6 Astra is OpenAI's flagship choice for its most demanding reasoning work. GPT 6 Sol Web Search offers a balance of research capability and cost for recurring production workflows.
GPT 6 Sol Web Search vs. GPT 6 Luna Web Search
GPT 6 Luna Web Search suits cost-sensitive, high-volume queries. GPT 6 Sol Web Search is a choice when the workflow calls for longer comparisons and closer examination of conflicting sources.
GPT 6 Sol Web Search vs. Search-First Answer Engines
Search-first answer engines often emphasize links and short answers. GPT 6 Sol Web Search can organize retrieved material into longer explanations and comparisons shaped by the user's question.
GPT 6 Sol Web Search vs. Gemini Grounded Search
Gemini grounded search models are tailored for Google ecosystem integrations and native web tools. GPT 6 Sol Web Search offers an OpenAI-native retrieval solution with robust support for mixed text, image, and document context through Flaq AI's unified chat completions API.