Model Marketplace

Qwen3-Open-Source

Will be retired on October 10, 2026View announcement
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Overview

Open-source Qwen3 non-thinking model; compared to the previous version (Qwen3-235B-A22B) shows slight improvements in subjective creativity and model safety.

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
    $0.23Per 1M tokens
  • Output
    $0.92Per 1M tokens

Rate Limits & Context

  • Max Input
    129K
  • Max Output
    32K
  • Context
    131K
  • TPMTokens Per Minute
    1M
  • RPMRequests Per Minute
    600

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="qwen3-235b-a22b-instruct-2507",
    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)