Qwen3.5-122B-A10B

适合长上下文助手与成本敏感生产路由的均衡型 Qwen MoE。

公开模型详情MoE Transformer

参数规模

122B / 10B active

上下文

256K

最大输出

32K

许可证

Apache 2.0

TTFT

310ms

吞吐

72 tok/s

为什么选它

256K 上下文的 Qwen 路线
上线价格仍在最终确认中

价格与差价

层级标准缓存SiliconFlow节省

在线调用$0.08 / $0.55$0.028暂无暂无

批处理$0.04 / $0.28$0.028暂无暂无

快速开始

OpenAI 兼容接口，切换 base_url 即可。

在 Playground 中试用查看价格页

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.luminapath.tech/v1",
    api_key="BATCHIN_API_KEY"
)

resp = client.chat.completions.create(
    model="qwen3.5-122b",
    messages=[{"role": "user", "content": "Summarize why this model is a fit for my workload."}]
)

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

JavaScript

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.luminapath.tech/v1",
  apiKey: process.env.BATCHIN_API_KEY,
});

const resp = await client.chat.completions.create({
  model: "qwen3.5-122b",
  messages: [{ role: "user", content: "Summarize why this model is a fit for my workload." }],
});

console.log(resp.choices[0]?.message?.content);

cURL

curl https://api.luminapath.tech/v1/chat/completions \
  -H "Authorization: Bearer $BATCHIN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-122b",
    "messages": [{"role":"user","content":"Summarize why this model is a fit for my workload."}]
  }'

规格与行为

架构

MoE Transformer

厂商家族

Qwen

上下文

256K

最大输出

32K

适用场景

qwen

long-context