NVIDIA
NVIDIA Nemotron 3.5 Super-VLAperto
NVIDIA high-performance open-weights vision-language MoE optimized for TensorRT-LLM and robot perception workflows.
Intelligenza
87.8 #30
AAQI 88.0
Velocità Uscita
110 t/s
ITL 9.1 ms/tok
Prezzo combinato (3:1)
$0.65 /1M
Input $0.35/M · Output $1.2/M
Contesto
512K
≈ 1,280 pagine A4 · output max 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)
Gli assi radar sono approssimati dai punteggi di categoria dove mancano misurazioni dirette — vedi Metodologia.
Punti di forza e compromessi
Dove vince
- Open-weights VL model with video input
- 110 tok/s on NVIDIA inference stacks
- Tuned for robotics and enterprise agent systems
A cosa fare attenzione
- Peak throughput requires NVIDIA hardware stack
- Reasoning depth below frontier text-only models
Casi d'uso killer
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.
Quickstart per sviluppatori e API
Imposta prima
NVIDIA_API_KEY — ottieni una chiave APIcurl 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);Risorse e link ufficiali
Deployment API
Confronta host e latenza
Throughput, TTFT e prezzi cache su Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Punteggi per Fonte
Punteggi per Categoria
Risultati dei Benchmark
Specifiche live e architettura
Sotto il cofano
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
Data di Rilascio
29 set 2026
Architettura
120B hybrid MoE, 12B active — vision-language
Parametri
120B/12B MoE
Licenza
Open Weights
Finestra di Contesto
512,000 tokens
Output max
16,384 tokens
Modalità
text + image + video
Prezzi cache
lettura $0.05 · scrittura $0.35 /1M
Ragionamento
—
Prezzo Ingresso
$0.35/M
Prezzo Uscita
$1.2/M
Velocità Uscita
110 t/s
Tempo al Primo Token
—
Dati aggiornati
1 ott 2026
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
Badge Markdown GitHub
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}
}Incorpora il badge del punteggio
Aggiungi un badge Podium Score live al tuo README su GitHub o Hugging Face — rimanda alla classifica live.
Anteprima
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