NVIDIA Nemotron 3.5 Super-VLOpen
NVIDIA high-performance open-weights vision-language MoE optimized for TensorRT-LLM and robot perception workflows.
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- Open-weights VL model with video input
- 110 tok/s on NVIDIA inference stacks
- Tuned for robotics and enterprise agent systems
Trade-offs to watch
- Peak throughput requires NVIDIA hardware stack
- Reasoning depth below frontier text-only models
Killer Use Cases
Developer Quickstart & API
NVIDIA_API_KEY first — get an API keycurl 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);Official Resources & Links
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
Scores by Source
Category Scores
Benchmark Results
Head-to-head Live Matchups
Live Specs & Architecture
- Hybrid Mamba-attention MoE stack for 4x predecessor throughput
- TensorRT-LLM optimized; FP8-ready checkpoints
- Native video + image understanding in one model
Estimated monthly bill
Cite & embed
Loading badge markdown...@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}
}Embed Score Badge
Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.
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