Qwen3-Coder-Plus-2025-09-23

Qwen3-Coder-Plus

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Text Generation

Overview

Text Generation

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 23 September , 2025.Compared to the previous version (snapshot from July 22), it demonstrates improved robustness in downstream task performance and tool invocation, along with enhanced code security.

Input

Text

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Input
$1Per 1M tokens
Output
$5Per 1M tokens

Input
$1Per 1M tokens
Output
$5Per 1M tokens

Context

Max Input

997.95K

Max Output

65.53K

Rate Limits

RPMRequests Per Minute
600
TPMTokens Per Minute
1M

API Reference

Get API Key

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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-plus-2025-09-23",
    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)

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-plus-2025-09-23",
    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)