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...
按量付费
无需预付费用,仅按实际使用量付费
使用以下代码示例接入我们的 API:
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...
输入
$0.15 /M
输出
$0.8 /M
上下文
262K
最大输出
66K
工具调用
支持
通过我们的统一 API 接入 Qwen3 Coder Next — 兼容 OpenAI、无冷启动、透明计费。
WaveSpeedAI 定价:输入每百万 token $0.15,输出每百万 token $0.80。Prompt 缓存和批处理单独计费,可显著降低长上下文、高重复任务的实际成本。
Qwen3 Coder Next 单次请求最多支持 262K 上下文 token,输出最多 66K token。
是的。WaveSpeedAI 通过 https://llm.wavespeed.ai/v1 的 OpenAI 兼容端点提供 Qwen3 Coder Next。把官方 OpenAI SDK 的 base URL 指向该地址,使用 WaveSpeedAI 的 API Key 即可,无需任何其他代码改动。
登录 WaveSpeedAI,在 Access Keys 中生成 API Key,使用上方显示的 model id 向 https://llm.wavespeed.ai/v1/chat/completions 发送请求。新账户可获得免费额度,用于试用 Qwen3 Coder Next。