Qwen3 Coder Next | Alibaba Open-Weight Coding API

Name: Qwen3 Coder Next API
Brand: qwen
Price: 0.15 USD
Availability: InStock

qwen/qwen3-coder-next

출시일: 2026-02-04

262,144 context · $0.15/M input tokens · $0.80/M output tokens

가격

사용량 기반 과금

선결제 없이 사용한 만큼만 지불

입력$0.15 / M Tokens

출력$0.80 / M Tokens

Cache Read$0.07 / M Tokens

모델 사용해 보기

qwen/qwen3-coder-next

온라인

안녕하세요! 도움이 되는 AI 어시스턴트입니다. 무엇을 도와드릴까요?

이 모델을 로컬 coding agent에서 사용할 준비가 되었나요?Agent 설정

API 사용법

다음 코드 예시를 사용해 API와 연동하세요:

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 2,
});

try {
  const response = await client.chat.completions.create({
    model: 'qwen/qwen3-coder-next',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 2,
});

try {
  const response = await client.chat.completions.create({
    model: 'qwen/qwen3-coder-next',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

모델 소개

Qwen qwen3-coder-next

Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows

Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per token, delivering performance comparable to models with 10 to 20x higher active compute, which makes it well suited for cost-sensitive, always-on agent deployment.

The model is trained with a strong agentic focus and performs reliably on long-horizon coding tasks, complex tool usage, and recovery from execution failures. With a native 256k context window, it integrates cleanly into real-world CLI and IDE environments and adapts well to common agent scaffolds used by modern coding tools. The model operates exclusively in non-thinking mode and does not emit <think> blocks, simplifying integration for production coding agents.

Why It Looks Great

Large Language Model architecture for efficient processing
262144 context window for long document handling
Competitive pricing at $0.1/$0.3 per million tokens

Key Features

Context Window: 262144 tokens
Max Output: 65536 tokens
Vision: Supported
Function Calling: Supported

Specifications

Specification	Value
Provider	Qwen
Model Type	Large Language Model (LLM)
Architecture	N/A
Context Window	262144 tokens
Max Output	65536 tokens
Input	Text
Output	Text
Vision	Supported
Function Calling	Supported

Pricing

Token Type	Cost per Million Tokens
Input	$0.1
Output	$0.3

How to Use

Write your prompt — describe the task, provide context, and specify desired output format.
Submit — the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: qwen/qwen3-coder-next

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="qwen/qwen3-coder-next",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "qwen/qwen3-coder-next",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: qwen/qwen3-coder-next
Provider: Qwen

정보

제공자alibaba

유형llm

지원 기능

입력

텍스트

출력

텍스트

컨텍스트262,144

최대 출력65,536

Vision-

Function Calling✓ 지원

API 접근 가이드

Base URLhttps://llm.wavespeed.ai/v1

API 엔드포인트chat/completions

모델 IDqwen/qwen3-coder-next

Qwen3 Coder Next에 대해 자주 묻는 질문

Qwen3 Coder Next API 비용은 얼마인가요?+

WaveSpeedAI 가격: 입력 토큰 100만 개당 $0.15, 출력 토큰 100만 개당 $0.80. 프롬프트 캐싱과 배치 처리는 별도로 청구되며 긴 반복 작업에서 실질 비용을 줄여 줍니다.

Qwen3 Coder Next의 컨텍스트 윈도우는 얼마나 되나요?+

Qwen3 Coder Next은 요청당 최대 262K 컨텍스트 토큰과 최대 66K 출력 토큰을 지원합니다.

Qwen3 Coder Next은 OpenAI 호환인가요?+

WaveSpeedAI는 https://llm.wavespeed.ai/v1의 OpenAI 호환 Chat Completions 인터페이스를 통해 Qwen3 Coder Next을 제공합니다. 대부분의 OpenAI SDK 클라이언트는 base URL과 API 키를 변경해 사용할 수 있으며, 선택 필드는 모델에 따라 다릅니다.

Qwen3 Coder Next을 어떻게 시작하나요?+

WaveSpeedAI에 로그인하고 Access Keys에서 API 키를 만든 다음, 위에 표시된 모델 ID로 https://llm.wavespeed.ai/v1/chat/completions에 요청을 보내세요. 제공 여부, 기능 및 가격은 최신 모델 카탈로그를 확인하세요.

가격