Qwen3-Coder-30B-A3B-Instruct

Qwen3-Open-Source

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Text Generation

Overview

Text Generation

The Qwen3-based code generation model, inheriting the coding agent capabilities of Qwen3-Coder-480B-A35B-Instruct, achieves State-of-the-Art (SOTA) coding performance among models of comparable size.

Input

Text

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Input
$0.45Per 1M tokens
Output
$2.25Per 1M tokens

Input
$0.45Per 1M tokens
Output
$2.25Per 1M tokens

Context

262.14K

Max Input

204.80K

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-30b-a3b-instruct",
    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-30b-a3b-instruct",
    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)