qwen/qwen3-coder-next
262,144 context · $0.15/M input tokens · $0.80/M output tokens
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...
Kullandıkça öde
Ön ödeme yok, yalnızca kullandığınız kadar ödeyin
API'mizle entegre etmek için aşağıdaki kod örneklerini kullanın:
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)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.
| 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 |
| Token Type | Cost per Million Tokens |
|---|---|
| Input | $0.1 |
| Output | $0.3 |
Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: qwen/qwen3-coder-next
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 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!"}]
}'
qwen/qwen3-coder-next
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...
Giriş
$0.15 /M
Çıkış
$0.8 /M
Bağlam
262K
Maks. Çıkış
66K
Araç Kullanımı
Destekleniyor
Birleşik API'miz aracılığıyla Qwen3 Coder Next'e erişin — OpenAI uyumlu, soğuk başlatma yok, şeffaf fiyatlandırma.
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 istek başına 262K bağlam tokenını ve 66K çıkış tokenını destekler.
Evet. WaveSpeedAI, Qwen3 Coder Next modelini https://llm.wavespeed.ai/v1 adresindeki OpenAI uyumlu endpoint üzerinden sunar. Resmi OpenAI SDK'sını WaveSpeedAI API anahtarınızla bu base URL'ye yöneltin — başka kod değişikliği gerekmez.
WaveSpeedAI'a giriş yapın, Access Keys'te bir API anahtarı oluşturun, ardından yukarıda gösterilen model id ile https://llm.wavespeed.ai/v1/chat/completions adresine bir istek gönderin. Yeni hesaplar Qwen3 Coder Next'i değerlendirmek için ücretsiz krediler alır.