Nemotron 3 Super

面向检索密集型工作流、智能体与记忆型产品的超长上下文模型。

1M context公开模型详情MoE Transformer

参数规模

120B / 12B active

上下文

最大输出

32K

许可证

NVIDIA Open

TTFT

310ms

吞吐

72 tok/s

为什么选它

100 万 Token 上下文
当前 BatchIn 公开目录独家

价格与差价

层级标准缓存SiliconFlow节省

在线调用$0.04 / $0.15$0.014暂无暂无

批处理$0.02 / $0.07$0.014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="nemotron-3-super",
    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: "nemotron-3-super",
  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": "nemotron-3-super",
    "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

BatchIn Exclusive

上下文

最大输出

32K

适用场景

exclusive

long-context