Free to try Qwen Plus Character API for richer persona-driven text roleplay, profile, memory, and stable Alibaba LLM workflows.
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',
'x-session': 'your-session-id'
},
body: JSON.stringify({
model: 'qwen-plus-character',
messages: [
{
role: 'system',
content: 'You are a witty high-school student who speaks with humor and warmth.'
},
{
role: 'user',
content: 'Remember that I prefer lightly sweet milk tea.'
}
],
stream: true,
max_tokens: 1200,
temperature: 0.8,
seed: 12345,
profile: 'You are a witty high-school student who remembers user preferences and stays in character.',
enable_long_term_memory: true,
memory_entries: 50,
skip_save_types: []
})
});
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',
'x-session': 'your-session-id',
},
json={
'model': 'qwen-plus-character',
'messages': [
{
'role': 'system',
'content': 'You are a witty high-school student who speaks with humor and warmth.',
},
{
'role': 'user',
'content': 'Remember that I prefer lightly sweet milk tea.',
},
],
'stream': True,
'max_tokens': 1200,
'temperature': 0.8,
'seed': 12345,
'profile': 'You are a witty high-school student who remembers user preferences and stays in character.',
'enable_long_term_memory': True,
'memory_entries': 50,
'skip_save_types': [],
},
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" \
-H "x-session: your-session-id" \
-d '{
"model": "qwen-plus-character",
"messages": [
{
"role": "system",
"content": "You are a witty high-school student who speaks with humor and warmth."
},
{
"role": "user",
"content": "Remember that I prefer lightly sweet milk tea."
}
],
"stream": true,
"max_tokens": 1200,
"temperature": 0.8,
"seed": 12345,
"profile": "You are a witty high-school student who remembers user preferences and stays in character.",
"enable_long_term_memory": true,
"memory_entries": 50,
"skip_save_types": []
}'
Qwen Plus Character Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Professional Qwen Plus Character API (Alibaba Character Roleplay LLM)
Qwen Plus Character API on Flaq AI provides Alibaba character roleplay LLM access for developers building persona-driven chat, companion apps, storytelling tools, and memory-aware dialogue workflows. This Qwen API integration helps teams create consistent text conversations with reusable character profiles, multi-turn context, and optional long-term memory through a managed production route. It is designed for products that need richer character dialogue, stable personality behavior, and affordable LLM access at scale.
Key Features of Qwen Plus Character API
- Consistent Character Dialogue: Generate text responses that stay aligned with a defined persona, tone, background, and conversational role.
- Long-Term Memory Workflows: Build roleplay or companion experiences that reuse memory through
enable_long_term_memory,profile, and a user-definedx-sessionvalue. - Prompt-Guided Persona Control: Use system messages for request-level role instructions and use
profileas the reusable character profile when long-term memory is enabled. - Group Chat Simulation: Simulate multi-speaker roleplay by writing speaker names manually at the start of message content and using a final assistant prefix with
partial: true. - High-Quality Text Output: Produce polished dialogue for storytelling, AI companions, interactive fiction, support personas, and character-driven product features.
- Flexible Conversation Context: Maintain useful multi-turn context for interactive applications that need coherent character behavior over time.
- Developer-Friendly API Integration: Add character LLM workflows to applications through a stable Flaq AI route without managing model infrastructure.
How to Use Qwen Plus Character API for Character Chat on Flaq AI
- Input: Text chat messages, system persona instructions, optional long-term memory settings, and optional assistant
partialcontinuation messages. - Output: Text responses for character dialogue, roleplay, storytelling, and persona-driven chat experiences.
- Conversation Control: Supports prompt-based direction for tone, personality, backstory, response style, short-term context, and long-term memory session continuity.
- Capabilities: Character roleplay, persona-consistent text generation, multi-turn dialogue, long-term memory workflows, group-chat simulation, partial assistant continuation, and interactive storytelling through Alibaba Qwen API access.
For group-chat simulation, speaker names are not inferred from profile. Add names manually at the beginning of each message, then append a final assistant message such as Ling Lu: with partial: true so the model continues as that character.
Best Use Cases for Qwen Plus Character API Integration
- AI Companions & Roleplay: Power character chat products that need stable personality, memory-aware responses, and engaging dialogue.
- Interactive Storytelling: Build narrative apps, fiction tools, and game dialogue systems with reusable character profiles.
- Customer-Facing Personas: Create branded assistants, support characters, and community bots with controlled tone and behavior.
- Education & Simulation: Run role-based practice conversations for language learning, training, coaching, and scenario rehearsal.
- Personalized Chat Products: Build user-facing tools that adapt character dialogue to ongoing context and session preferences.
Note Please ensure prompts, character profiles, memory usage, and generated text comply with Alibaba and Flaq AI safety requirements. If an error occurs, revise the request, profile, or memory settings and try again.
Qwen Plus Character vs Competitors: Comparative Analysis
- Qwen Plus Character vs. Qwen Flash Character
Qwen Flash Character prioritizes faster and more affordable roleplay iteration. Qwen Plus Character is positioned for richer character dialogue and more polished persona-driven responses. - Qwen Plus Character vs. GPT Character Chat GPT models provide broad general-purpose chat behavior. Qwen Plus Character focuses on Alibaba-powered roleplay workflows with character profiles and memory-aware conversation patterns.
- Qwen Plus Character vs. Claude Character Chat Claude is strong for careful writing and long-form dialogue. Qwen Plus Character gives teams a dedicated Qwen route for interactive character personas and scalable chat products.
- Qwen Plus Character vs. Kimi Character Workflows Kimi is useful for general language reasoning and writing. Qwen Plus Character focuses on persona consistency, roleplay dialogue, and memory-enabled character experiences.
- Qwen Plus Character vs. Qwen General Chat General Qwen chat models are suited for broad text tasks. Qwen Plus Character is tuned for character-style conversations where identity, tone, and session continuity matter.