NVIDIA Nemotron 3.5 Super-VLOffen
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-Achsen sind aus Kategorie-Scores approximiert, wo direkte Benchmark-Messungen fehlen — siehe Methodik.
Stärken & Trade-offs
Wo das Modell gewinnt
- Open-weights VL model with video input
- 110 tok/s on NVIDIA inference stacks
- Tuned for robotics and enterprise agent systems
Worauf achten
- Peak throughput requires NVIDIA hardware stack
- Reasoning depth below frontier text-only models
Killer-Anwendungsfälle
Developer-Quickstart & API
NVIDIA_API_KEY — API-Schlüssel holencurl 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);Offizielle Ressourcen & Links
Hosts & Latenz vergleichen
Durchsatz, TTFT und Cache-Preise bei Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock und Azure.
Ergebnisse nach Quelle
Kategorie-Punkte
Benchmark-Ergebnisse
Live-Spezifikationen & Architektur
- Hybrid Mamba-attention MoE stack for 4x predecessor throughput
- TensorRT-LLM optimized; FP8-ready checkpoints
- Native video + image understanding in one model
Geschätzte Monatsrechnung
Zitieren & Einbetten
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}
}Score-Badge einbetten
Fügen Sie Ihrem GitHub- oder Hugging-Face-README ein Live-Podium-Score-Badge hinzu — es verlinkt auf das Live-Ranking.
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