NVIDIA
NVIDIA Nemotron 3.5 Super-VL开源
NVIDIA high-performance open-weights vision-language MoE optimized for TensorRT-LLM and robot perception workflows.
智商指数
87.8 #30
AAQI 88.0
输出速度
110 t/s
ITL 9.1 ms/tok
混合价格 (3:1)
$0.65 /1M
输入 $0.35/M · 输出 $1.2/M
上下文
512K
≈ 1,280 页 A4 · 最大输出 16,384
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
NVIDIA Nemotron 3.5 Super-VL
Claude Mythos Preview (#1)
Average (68)
缺少直接基准测量时,雷达图各轴由类别得分近似得出 — 详见 评分方法。
优势与取舍
优势所在
- Open-weights VL model with video input
- 110 tok/s on NVIDIA inference stacks
- Tuned for robotics and enterprise agent systems
需要注意的权衡
- Peak throughput requires NVIDIA hardware stack
- Reasoning depth below frontier text-only models
杀手级用例
Robotics perception
Vision-language-action pipelines on Jetson and DGX.
Video analytics agents
Multi-modal sub-agents parsing video, audio and documents.
Self-hosted VL workloads
Enterprise document/video QA with open weights.
开发者快速上手与 API
请先设置
NVIDIA_API_KEY — 获取 API 密钥curl https://integrate.api.nvidia.com/v1/chat/completions \
-H "Authorization: Bearer $NVIDIA_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "nvidia/nemotron-3-5-super-vl",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://integrate.api.nvidia.com/v1",
api_key=os.environ["NVIDIA_API_KEY"],
)
resp = client.chat.completions.create(
model="nvidia/nemotron-3-5-super-vl",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://integrate.api.nvidia.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.NVIDIA_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "nvidia/nemotron-3-5-super-vl",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);官方资源与链接
API 部署
对比托管商与延迟
Groq、Cerebras、Together、DeepInfra、Fireworks、Bedrock 与 Azure 的吞吐量、TTFT 和缓存定价。
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
各榜单得分
分类能力得分
基准测试分项
实时规格与架构
底层解析
120B hybrid MoE, 12B active — vision-language
- Hybrid Mamba-attention MoE stack for 4x predecessor throughput
- TensorRT-LLM optimized; FP8-ready checkpoints
- Native video + image understanding in one model
发布日期
2026年9月29日
架构
120B hybrid MoE, 12B active — vision-language
参数量
120B/12B MoE
开源协议
Open Weights
上下文窗口
512,000 tokens
最大输出
16,384 tokens
模态
text + image + video
缓存价格
读 $0.05 · 写 $0.35 /1M
推理
—
输入价格
$0.35/M
输出价格
$1.2/M
输出速度
110 t/s
首Token延迟
—
数据更新
2026年10月1日
每月账单预估
每月预估支出
$0.00
正在计算相对 GPT-4o 的节省额…
引用与嵌入
GitHub Markdown 徽章
Loading badge markdown...BibTeX
@misc{llmpodium2026nemotron35supervl,
title = {Benchmark Analysis and Intelligence Evaluation of NVIDIA Nemotron 3.5 Super-VL},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/nemotron-3-5-super-vl}
}嵌入评分徽章
将实时 Podium 评分徽章添加到 GitHub 或 Hugging Face README —— 链接回实时排名。
预览
Markdown
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