Qwen-Turbo

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ReasoningText Generation

Overview

ReasoningText Generation

The Turbo model of the Qwen3 series. It effectively integrates thinking mode and non-thinking mode, allowing for mode switching during conversations. Its reasoning capabilities rival those of QwQ-32B with a smaller parameter size, while its general capabilities significantly surpass those of Qwen2.5-Turbo, achieving the SOTA level in the same scale within the industry.

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

Web Search

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

feature.funeTuning

Pricing

Input
$0.05Per 1M tokens
Output
$0.2Per 1M tokens
Input(Thinking)
$0.05Per 1M tokens
Output(Thinking)
$0.5Per 1M tokens
Input(Implicit Cache)
$0.01Per 1M tokens
Input(Thinking Implicit Cache)
$0.01Per 1M tokens
Thinking Output(Batch File)
$0.25Per 1M tokens
Input(Batch File)
$0.025Per 1M tokens
Output(Batch File)
$0.1Per 1M tokens
Input(Thinking Batch File)
$0.025Per 1M tokens

toc.rateLimitsAndContext

Max Input
98.30K
Max Output
8.19K
RPMRequests Per Minute
600
TPMTokens Per Minute
5M
Context
131.07K

API Reference

Get API Key

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import os
from dashscope import Generation
import dashscope
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"},
]
response = Generation.call(
    # 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"),
    model="qwen-turbo",
    messages=messages,
    result_format="message",
    # Enable deep thinking
    enable_thinking=True,
)

if response.status_code == 200:
    # Print thinking process
    print("=" * 20 + "Thinking process" + "=" * 20)
    print(response.output.choices[0].message.reasoning_content)
    
    # Print response
    print("=" * 20 + "Full response" + "=" * 20)
    print(response.output.choices[0].message.content)
else:
    print(f"HTTP return code: {response.status_code}")
    print(f"Error code: {response.code}")
    print(f"Error message: {response.message}")