Model Marketplace

Qwen3-Coder-Plus

Will be retired on October 10, 2026View announcement
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Overview

Qwen3-based code generation model with strong coding agent power, excels at tool calling and environment interaction, capable of autonomous programming with outstanding code capability while maintaining general ability. This is a snapshot from 22 July, 2025.

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
    $1Per 1M tokens
  • Output
    $5Per 1M tokens

Rate Limits & Context

  • Max Input
    1M
  • Max Output
    65K
  • Context
    1M
  • TPMTokens Per Minute
    1M
  • RPMRequests Per Minute
    60

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-coder-plus-2025-07-22",
    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)