Alibaba/qwen-3.7-plus-web-search

qwen-3.7-plus-web-search

Free to try Qwen 3.7 Plus Web Search API for affordable research, current answers, summaries, and web-aware LLM workflows through Flaq AI. Use stable GPT, Claude, and other LLM APIs for agents, chatbots, research, coding, automation, and production-ready intelligent applications.

Web Search

Related Qwen 3.7 Plus Models

qwen-3.7-plus-text-to-texttext-to-text

qwen-3.7-plus-web-searchweb-search

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: 'qwen-3.7-plus-web-search',
    messages: [
      {
        role: 'user',
        content: 'What are the latest developments in quantum computing this year?'
      }
    ],
    stream: true,
    max_tokens: 2048,
    top_p: 0.9,
    top_k: 50
  })
});

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': 'qwen-3.7-plus-web-search',
        'messages': [
            {
                'role': 'user',
                'content': 'What are the latest developments in quantum computing this year?',
            }
        ],
        'stream': True,
        'max_tokens': 2048,
        'top_p': 0.9,
        'top_k': 50,
    },
    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": "qwen-3.7-plus-web-search",
    "messages": [
      {
        "role": "user",
        "content": "What are the latest developments in quantum computing this year?"
      }
    ],
    "stream": true,
    "max_tokens": 2048,
    "top_p": 0.9,
    "top_k": 50
  }'

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Qwen 3.7 Plus Web Search Pricing

Parameters	Price	Original Price	Discount

Token: input	$0.3600 per 1M input tokens	$0.4000 per 1M input tokens	90%
Token: output	$1.4400 per 1M output tokens	$1.6000 per 1M output tokens	90%

README

Fast & Affordable Qwen 3.7 Plus Web Search API (Alibaba's Efficient Research Model)

Qwen 3.7 Plus Web Search API on Flaq AI provides affordable Alibaba model access for search-enabled workflows that need current information, concise synthesis, and reliable production behavior. This efficient Qwen API integration helps developers build research assistants, SEO tools, market monitoring features, and support workflows with web search support through a stable managed route. It is a practical fit for teams that need fresh context at scale.

Key Features of Qwen 3.7 Plus Web Search API

Current Information Lookup: Use web search support to answer questions that may depend on recent events, products, or public updates.
Cost-Effective Research Automation: Build high-volume search-enabled applications with an affordable Alibaba model route on Flaq AI.
Research-Ready Summaries: Convert retrieved context into clear answers, briefs, and comparisons for business or technical teams.
Conversation-Aware Output: Support follow-up questions and iterative research with useful context retention.
Flexible API Controls: Guide response style, length, and research focus through practical generation controls.
Production-Friendly Integration: Add web search capability without managing separate model and search infrastructure.

How to Use Qwen 3.7 Plus Web Search API for Real-Time Web Research on Flaq AI

Input: Natural language research questions, current-event prompts, domain-focused tasks, and optional conversation context.
Output: Fresh, source-aware answers and summaries generated with web search support through Flaq AI.
Route Configuration: Designed for search-enabled research workflows with follow-up context and current-information lookup.
Configuration: Supports practical controls for response style, length, and task direction.
Capabilities: Current information lookup, SEO research, competitor monitoring, fact checking, sales intelligence, and content planning through affordable Alibaba Qwen API integration.

Best Use Cases for Qwen 3.7 Plus Web Search API Integration

SEO & Content Planning: Research live topic coverage, collect angles, and draft content briefs for publishing teams.
Market Monitoring: Track public updates, competitor changes, and product announcements with affordable search-enabled AI.
Support Knowledge Updates: Help support teams answer questions that depend on changing public information.
Sales Research: Gather company, product, and industry context before outreach or account planning.
Current Event Summaries: Produce concise summaries of recent news, policy changes, and industry developments.

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

Qwen 3.7 Plus Web Search vs Competitors: Comparative Analysis

Qwen 3.7 Plus Web Search vs. Qwen 3.7 Max Web Search
Qwen 3.7 Max Web Search is better for deep synthesis and demanding analysis. Qwen 3.7 Plus Web Search prioritizes affordability and efficient throughput for everyday research workflows.
Qwen 3.7 Plus Web Search vs. GPT Web Search
GPT web search routes offer OpenAI-native behavior. Qwen 3.7 Plus Web Search provides a cost-effective Alibaba route for teams building search-enabled products on Flaq AI.
Qwen 3.7 Plus Web Search vs. Gemini Search Workflows
Gemini is strong for Google ecosystem use cases. Qwen 3.7 Plus Web Search focuses on affordable, managed web research for business apps and content workflows.
Qwen 3.7 Plus Web Search vs. Perplexity-style APIs
Perplexity-style products emphasize direct answer experiences. Qwen 3.7 Plus Web Search is designed for developers embedding research into custom API-driven workflows.
Qwen 3.7 Plus Web Search vs. Llama with Retrieval
Llama with retrieval offers infrastructure flexibility. Qwen 3.7 Plus Web Search avoids retrieval setup and model hosting for teams that want a managed API.