OpenAI/gpt-6-luna-web-search

gpt-6-luna-web-search

Try GPT 6 Luna API by OpenAI for web search, research, and answers informed by online sources through Flaq AI's stable, affordable API with streaming responses.

Web Search

Related Gpt 6 Luna Models

gpt-6-luna-text-to-texttext-to-text

gpt-6-luna-image-to-textimage-to-text

gpt-6-luna-web-searchweb-search

gpt-6-luna-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-luna-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-luna-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-luna-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 Luna Web Search Pricing

Parameters	Price	Original Price	Discount

Token: input	$0.1000 per 1M input tokens	-	Standard
Token: output	$0.5000 per 1M output tokens	-	Standard

README

Fast & Cost-Effective GPT 6 Luna Web Search API (OpenAI Rapid Web Research and Retrieval)

GPT 6 Luna Web Search API on Flaq AI combines a cost-efficient GPT-6 model with web retrieval for source-aware answers and timely research. Built for high-volume customer queries, news feeds, and market monitoring, this web search API integration helps developers retrieve public information, compare sources, and summarize findings for review.

Key Features of GPT 6 Luna Web Search API

Web-Grounded Retrieval: Retrieve and synthesize current public information for customer-facing chat tools and research workflows.
Cost-Conscious Search Workflows: Use GPT 6 Luna for focused, high-volume queries when efficiency is a priority.
Concise Fact Synthesis: Convert diverse search snippets and online references into crisp, factual summaries without unnecessary fluff.
Multimodal Context Support: Attach supported screenshots or reference documents alongside the search prompt to guide retrieval based on specific visual or textual artifacts.
Continuous Multi-Turn Follow-Up: Ask iterative questions and refine search inquiries across successive messages while preserving conversation context.
Low-Latency Streaming Output: Stream search results and synthesized answers incrementally via server-sent events for immediate interactive rendering.

How to Use GPT 6 Luna Web Search API for Rapid Web Research on Flaq AI

Input: A user query or research topic submitted in conversation messages, optionally supplemented by supported images or documents.
Output: Concise answers and summaries based on retrieved public information; users should check important claims against the original sources.
Output Length: Optionally set a maximum output token count for short bullet summaries or detailed overviews. No default maximum is specified for this parameter; model output limits still apply.
Streaming: Deliver the search synthesis progressively through server-sent events for instant user display.
Capabilities: Rapid web retrieval, live fact checking, multi-turn search refinement, and multimodal context integration via Flaq AI API integration.

Best Use Cases for GPT 6 Luna Web Search API Integration

Real-Time Customer Support: Equip customer service bots with the ability to look up current shipping rates, store hours, recent policies, and public service notices.
Trending News & Event Monitoring: Summarize breaking news stories, event updates, and trending topics rapidly for media feeds and social aggregators.
Product Price and Availability Checks: Quickly check public retail websites for price points, product revisions, and inventory announcements.
Source-Aware Factual Q&A: Help educational platforms and search widgets answer questions with retrieved sources that readers can check.
Competitive Announcement Review: Summarize public product announcements and compare changes when the application supplies new search requests.

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 Luna Web Search vs Competitors: Comparative Analysis

GPT 6 Luna Web Search vs. GPT 6 Sol Web Search
GPT 6 Sol Web Search suits workflows that call for longer comparisons across public sources. GPT 6 Luna Web Search is positioned for focused, cost-sensitive queries at higher volume.
GPT 6 Luna Web Search vs. GPT 5.6 Luna Web Search
GPT 5.6 Luna Web Search is an earlier option for search-backed answers. GPT 6 Luna Web Search brings the newer GPT-6 family to focused research queries; compare source relevance and answer quality on your own queries before migrating.
GPT 6 Luna Web Search vs. Search-First Answer Engines
Search-first answer engines often return rigid snippets and heavy citation links. GPT 6 Luna Web Search delivers a conversational, fully coherent synthesis tailored directly to the user's prompt format and system instructions.
GPT 6 Luna Web Search vs. Gemini Grounded Search
Gemini grounded search models are designed around the Google search ecosystem. GPT 6 Luna Web Search provides an OpenAI-native search option with native chat completion conventions, multi-turn message handling, and competitive pricing on Flaq AI.