Model Marketplace

Qwen-Plus-Character

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Overview

The role-playing model of the Qwen series. This is a dynamically updated version, and notifications will be provided in advance for any model updates. It is suitable for anthropomorphic role-playing and has optimized capabilities in following predefined character instructions, advancing conversations, and demonstrating active listening and empathy. Additionally, it supports the deep restoration of personalized characters.

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
    $0.5Per 1M tokens
  • Output
    $1.4Per 1M tokens
  • Input(Implicit Cache)
    $0.1Per 1M tokens

Rate Limits & Context

  • Max Input
    32K
  • Max Output
    4K
  • Context
    32K
  • 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://maas.qwencloudapi.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="qwen-plus-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)