Free to try Qwen 3.8 Max Web Search API by Alibaba with answers, grounded research, source-aware analysis, and production web-enabled LLM workflows on Flaq AI.
Related Qwen 3.8 Max Models
API Examples
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-3.8-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-3.8-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-3.8-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
}'
Qwen 3.8 Max Web Search Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Qwen 3.8 Max Web Search API (Search-Augmented Answers)
Qwen 3.8 Max Web Search API combines language generation with a web-search workflow for questions that benefit from current or externally sourced information. It helps applications turn a user query into a structured search-and-answer experience through an Alibaba-oriented chat integration on Flaq AI.
Key Features of Qwen 3.8 Max Web Search API
-
Search-Augmented Responses: Use web search as part of a language workflow when the answer depends on information beyond the conversation context.
-
Natural-Language Querying: Accept user questions and research instructions in ordinary language without requiring a separate search syntax.
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Result Synthesis: Organize retrieved information into summaries, comparisons, explanations, or next-step recommendations.
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Context-Aware Follow-Up: Continue a search-oriented conversation with additional questions and focused refinement.
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Structured Response Controls: Use available message, sampling, and response-length controls to fit the output to the application.
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API-Friendly Research Flow: Add web-assisted answers to assistants, support tools, and information products through a chat-oriented endpoint.
How to Use Qwen 3.8 Max Web Search API for Search-Augmented Answers on Flaq AI
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Input: A natural-language question or research instruction with any relevant context and output requirements.
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Search Direction: Clarify the topic, time range, comparison criteria, and level of detail expected in the response.
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Output: A synthesized language response returned through the Flaq AI chat workflow for display or automation.
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Follow-Up: Ask focused questions to refine the search scope, compare findings, or request a different output structure.
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Application Handling: Review search-based answers, handle provider errors, and communicate uncertainty where the information may change.
Best Use Cases for Qwen 3.8 Max Web Search API Integration
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Current Information Research: Help users investigate changing topics, products, organizations, and public information.
-
Market and Competitor Monitoring: Organize external findings into repeatable summaries and comparison workflows.
-
Knowledge Assistants: Give support and internal tools a search-enabled response path for questions outside stored knowledge.
-
Content Preparation: Gather background information, references, and topic context before drafting articles or campaigns.
-
Decision Support: Combine search-oriented retrieval with structured explanations for research and operational planning.
Note Search-based answers should be reviewed for source quality, freshness, completeness, and uncertainty before being used in high-impact decisions or published content.
Qwen 3.8 Max Web Search vs Competitors: Comparative Analysis
-
Qwen 3.8 Max vs. Perplexity Sonar: Perplexity Sonar is designed around answer-focused web research. Qwen 3.8 Max Web Search provides a Qwen-oriented API path for integrating search-assisted answers into broader application workflows.
-
Qwen 3.8 Max vs. Gemini with Grounding: Gemini can combine language generation with Google's search ecosystem. Qwen 3.8 Max is suited to teams building search-assisted conversations around Alibaba services.
-
Qwen 3.8 Max vs. Claude with Web Search: Claude with web search offers careful language synthesis and research interaction. Qwen 3.8 Max provides an alternative for products already standardized on Qwen message workflows.
-
Qwen 3.8 Max vs. OpenAI Web Search: OpenAI web search integrates with OpenAI language products and APIs. Qwen 3.8 Max supports a comparable search-assisted application pattern within the Qwen family.
-
Qwen 3.8 Max vs. Tavily: Tavily focuses on search retrieval for AI applications. Qwen 3.8 Max Web Search combines the search-oriented workflow with language response generation in a single model integration.