Free to try Qwen 3.8 Max API by Alibaba with text reasoning, writing, coding, analysis, and scalable production workflows through Flaq AI's unified API.
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-text-to-text',
messages: [
{
role: 'user',
content: 'Explain the concept of attention mechanisms in neural networks.'
}
],
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-text-to-text',
'messages': [
{
'role': 'user',
'content': 'Explain the concept of attention mechanisms in neural networks.',
}
],
'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-text-to-text",
"messages": [
{
"role": "user",
"content": "Explain the concept of attention mechanisms in neural networks."
}
],
"stream": true,
"max_tokens": 2048,
"top_p": 0.9,
"top_k": 50
}'
Qwen 3.8 Max Text to Text Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Qwen 3.8 Max Text-to-Text API (Advanced Language Intelligence)
Qwen 3.8 Max Text-to-Text API provides a capable language endpoint for reasoning, writing, coding, and structured information work. Its text-only message workflow fits assistants, automation systems, and product experiences that need flexible language generation through an Alibaba-oriented API integration on Flaq AI.
Key Features of Qwen 3.8 Max Text-to-Text API
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Advanced Text Reasoning: Work through analytical questions, multi-step instructions, and structured problem-solving tasks.
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Writing and Transformation: Draft, rewrite, summarize, translate, and organize content for editorial and business workflows.
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Multi-Turn Messages: Retain relevant conversation context for assistants and longer-running application interactions.
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Structured Prompting: Separate system guidance, user requests, and supporting context to make recurring workflows easier to control.
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Flexible Response Controls: Use the available context, sampling, and response-length controls for different product experiences.
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API-Friendly Integration: Connect text requests and generated responses to user-facing applications and server-side automation.
How to Use Qwen 3.8 Max Text-to-Text API for Language Workflows on Flaq AI
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Input: Text instructions, system guidance, user context, and any relevant conversation history.
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Prompt Structure: Define the task, required output format, constraints, and supporting information in a clear message flow.
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Output: Generated text returned through the Flaq AI chat workflow for display, automation, or review.
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Generation Controls: Adjust the available response-length and sampling parameters according to the use case.
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Application Handling: Validate outputs, handle errors, and preserve context when continuing a multi-turn interaction.
Best Use Cases for Qwen 3.8 Max Text-to-Text API Integration
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Coding and Documentation: Assist with implementation, debugging, technical explanations, and structured engineering documents.
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Research Synthesis: Turn complex notes and source material into summaries, comparisons, and organized reports.
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Content Production: Draft, edit, localize, and repurpose content for different audiences and channels.
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Customer and Knowledge Assistants: Support user questions with structured responses and contextual follow-up.
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Workflow Automation: Add language reasoning to business processes that need repeatable text generation and transformation.
Note Review generated content for accuracy, safety, and policy compliance before using it in customer-facing or high-impact workflows.
Qwen 3.8 Max Text-to-Text vs Competitors: Comparative Analysis
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Qwen 3.8 Max vs. Claude Opus 5: Claude Opus 5 emphasizes deep reasoning and polished writing. Qwen 3.8 Max offers an Alibaba-oriented alternative for structured language workflows and automation.
-
Qwen 3.8 Max vs. GPT-5: GPT-5 provides broad reasoning and multimodal capabilities. Qwen 3.8 Max is focused here on text-first integration, structured prompts, and message-based application workflows.
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Qwen 3.8 Max vs. Gemini 2.5 Pro: Gemini 2.5 Pro combines language reasoning with Google's ecosystem. Qwen 3.8 Max provides another path for teams building around Alibaba model integrations.
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Qwen 3.8 Max vs. GLM 5.2: GLM 5.2 is a capable text reasoning model with application-friendly controls. Qwen 3.8 Max differentiates through its Qwen family integration and broad text workflow coverage.
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Qwen 3.8 Max vs. DeepSeek V3: DeepSeek V3 is popular for language and coding tasks. Qwen 3.8 Max is suitable for teams that need structured language generation inside Qwen-oriented products and services.