Qwen-Turbo

Copy success!

Add to Compare

Overview

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

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
$0.2Per 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

Rate Limits & Context

Max Input
98K
Max Output
8K
Context
131K
TPMTokens Per Minute
5M
RPMRequests Per Minute
600

API Reference

Call API

Copy success!

123456789101112131415161718192021222324252627282930

from openai import OpenAI
import os

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",
)

messages = [{"role": "user", "content": "Who are you"}]
completion = client.chat.completions.create(
    model="qwen-turbo",  # You can replace this with another deep thinking models
    messages=messages,
    extra_body={"enable_thinking": True},
    stream=True
)
is_answering = False  # Indicates whether the response phase has started
print("\n" + "=" * 20 + "Thinking process" + "=" * 20)
for chunk in completion:
    if not chunk.choices:
        continue
    delta = chunk.choices[0].delta
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Full response" + "=" * 20)
            is_answering = True
        print(delta.content, end="", flush=True)