Model Marketplace

Qwen3-Open-Source

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Overview

The new-generation code generation model in the Qwen3 series delivers performance close to that of Qwen3-Coder-Plus while offering even better capabilities. The model has been optimized with a focus on repository-level understanding, supports multi-turn tool interactions, and enhances its compatibility with agentic coding tools.

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
    $0.3Per 1M tokens
  • Output
    $1.5Per 1M tokens
  • Input
    $0.3Per 1M tokens
  • Output
    $1.5Per 1M tokens

Rate Limits & Context

  • Max Input
    204K
  • Max Output
    65K
  • Context
    262K
  • 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://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="qwen3-coder-next",
    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)