Qwen3-Coder-Plus
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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.
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 docsFunction Calling
Use function calling to connect large language models with external tools and systems.View docsCache
Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docsStructured Outputs
Structured Outputs help ensure the model returns a JSON string in the expected format.View docsPricing
- Input$1Per 1M tokens
- Output$5Per 1M tokens
- Input(Implicit Cache)$0.2Per 1M tokens
- Explicit Cache Creation$1.25Per 1M tokens
- Explicit Cache Read$0.1Per 1M tokens
Rate Limits & Context
- Max Input997K
- Max Output65K
- Context1M
- TPMTokens Per Minute2M
- RPMRequests Per Minute2K
API Reference
Call APICopy success!
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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",
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)