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

Yayın tarihi: 2026-02-04

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

Fiyatlandırma

Kullandıkça öde

Ön ödeme yok, yalnızca kullandığınız kadar ödeyin

Giriş$0.15 / M Tokens

Çıkış$0.80 / M Tokens

Cache Read$0.07 / M Tokens

Modeli dene

qwen/qwen3-coder-next

Çevrimiçi

Merhaba! Yardımcı bir yapay zeka asistanıyım. Size nasıl yardımcı olabilirim?

Bu modeli yerel bir coding agent içinde kullanmaya hazır mısınız?Agent setup

API Kullanımı

API'mizle entegre etmek için aşağıdaki kod örneklerini kullanın:

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;
}

Model Tanıtımı

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

Bilgi

Sağlayıcıalibaba

Türllm

Desteklenen İşlevsellik

Giriş

Metin

Çıkış

Metin

Bağlam262,144

Maks. Çıkış65,536

Vision-

Function Calling✓ Destekleniyor

API Erişim Kılavuzu

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

API Endpointchat/completions

Model IDqwen/qwen3-coder-next

Qwen3 Coder Next hakkında sık sorulan sorular

Qwen3 Coder Next API ücreti ne kadar?+

WaveSpeedAI fiyatlandırması: milyon giriş tokenı başına $0.15 ve milyon çıkış tokenı başına $0.80. Prompt caching ve toplu işleme ayrı faturalanır ve uzun, tekrar eden yüklerde etkin maliyeti düşürür.

Qwen3 Coder Next'in bağlam penceresi nedir?+

Qwen3 Coder Next istek başına 262K bağlam tokenını ve 66K çıkış tokenını destekler.

Qwen3 Coder Next OpenAI uyumlu mu?+

WaveSpeedAI, Qwen3 Coder Next modelini https://llm.wavespeed.ai/v1 adresindeki OpenAI uyumlu Chat Completions arayüzü üzerinden sunar. Çoğu OpenAI SDK istemcisinde base URL ve API anahtarını değiştirmek yeterlidir; isteğe bağlı alanlar modele bağlıdır.

Qwen3 Coder Next'e nasıl başlarım?+

WaveSpeedAI’a giriş yapın, Access Keys bölümünde bir API anahtarı oluşturun ve yukarıdaki model id ile https://llm.wavespeed.ai/v1/chat/completions adresine istek gönderin. Güncel kullanılabilirlik, özellikler ve fiyatlar için model kataloğunu kontrol edin.