Alibaba/qwen-3.7-max-web-search

qwen-3.7-max-web-search

Free to try Qwen 3.7 Max Web Search API for grounded research, current answers, analysis, and web-aware LLM workflows on 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 Max Models

qwen-3.7-max-text-to-texttext-to-text

qwen-3.7-max-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-max-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-max-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-max-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 Max Web Search Pricing

Parameters	Price	Original Price	Discount

Token: input	$2.2500 per 1M input tokens	$2.5000 per 1M input tokens	90%
Token: output	$6.7500 per 1M output tokens	$7.5000 per 1M output tokens	90%

README

Fast & Affordable Qwen 3.7 Max Web Search API (Alibaba's Search-Enabled Reasoning Model)

Qwen 3.7 Max Web Search API on Flaq AI provides Alibaba model access for current-information workflows that need search-enabled reasoning, source-aware synthesis, and production-ready output. This powerful Qwen API integration helps developers build research assistants, market intelligence tools, content planning systems, and knowledge workflows that can use web search support through a stable API route. It is designed for teams that need stronger reasoning with fresh context.

Key Features of Qwen 3.7 Max Web Search API

Search-Enabled Reasoning: Combine model reasoning with current web context for timely answers, comparisons, and research summaries.
Advanced Research Synthesis: Turn retrieved information into concise briefs, structured comparisons, and actionable analysis.
Conversation-Aware Output: Preserve useful follow-up context across multi-turn research workflows.
Flexible API Controls: Guide response style, depth, and task direction through practical generation controls on Flaq AI.
Production Research Workflow: Build current-information features without assembling provider-specific search and model plumbing from scratch.
Developer-Friendly Integration: Use a stable managed route for web research, content planning, and operational intelligence features.

How to Use Qwen 3.7 Max 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, reasoning depth, and research focus.
Capabilities: Current information lookup, market research, fact checking, competitive analysis, SEO planning, and source-aware research through Alibaba Qwen API integration.

Best Use Cases for Qwen 3.7 Max Web Search API Integration

Market & Competitor Research: Track product launches, feature changes, positioning, and public updates with search-enabled AI.
Current Event Summaries: Generate timely summaries of news, policy changes, funding activity, and industry developments.
SEO & Content Planning: Research live search results, compare topic coverage, and draft informed content outlines.
Fact Checking & Validation: Compare sources and reduce stale-answer risk in knowledge-heavy applications.
Sales & Account Intelligence: Gather public company updates and relevant business context before outreach.

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 Max Web Search vs Competitors: Comparative Analysis

Qwen 3.7 Max Web Search vs. Qwen 3.7 Plus Web Search
Qwen 3.7 Plus Web Search is useful for cost-effective research workflows. Qwen 3.7 Max Web Search is better suited for deeper synthesis, complex comparisons, and demanding current-information tasks.
Qwen 3.7 Max Web Search vs. GPT Web Search
GPT web search routes offer OpenAI-style behavior and broad ecosystem familiarity. Qwen 3.7 Max Web Search provides an Alibaba alternative for teams that want strong multilingual research and managed API access.
Qwen 3.7 Max Web Search vs. Gemini Search Workflows
Gemini is attractive for Google-native use cases. Qwen 3.7 Max Web Search focuses on flexible Alibaba search-enabled reasoning for research products and business automation.
Qwen 3.7 Max Web Search vs. Perplexity-style APIs
Perplexity-style tools emphasize answer engines. Qwen 3.7 Max Web Search gives developers a model route they can embed into broader workflows, dashboards, and custom applications.
Qwen 3.7 Max Web Search vs. Llama with Retrieval
Llama with retrieval gives infrastructure control. Qwen 3.7 Max Web Search removes hosting and retrieval orchestration complexity for teams that want a managed API path.