Model Marketplace

Qwen-Flash-Character

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Overview

The Qwen Role-Playing Model Series is specifically optimized for muti-language anthropomorphic interaction scenarios. It demonstrates advanced capabilities in character consistency maintenance, context-aware dialogue progression, and empathetic engagement, enabling precise personalized character embodiment. This version significantly enhances Japanese linguistic localization (including dialects and honorifics), human-like role-playing authenticity, narrative coherence control, and scenario-based cognitive intelligence.

Input

Text

Output

Text

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

  • Input
    $0.05Per 1M tokens
  • Output
    $0.4Per 1M tokens
  • Input(Implicit Cache)
    $0.01Per 1M tokens

Rate Limits & Context

  • Max Input
    8K
  • Max Output
    4K
  • Context
    8K
  • TPMTokens Per Minute
    500K
  • RPMRequests Per Minute
    120

API Reference

Call API
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import os
from openai import OpenAI

client = OpenAI(
    # If the environment variable is not set, replace it with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="qwen-flash-character",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who are you?"},
    ],
    stream=True
)
for chunk in completion:
    print(chunk.choices[0].delta.content, end="", flush=True)