NVIDIA

NVIDIA Nemotron 3.5 Super-VLOpen

NVIDIA high-performance open-weights vision-language MoE optimized for TensorRT-LLM and robot perception workflows.

#30released Sep 29, 2026·Open Weights·120B/12B MoE
textimagevideo512K ctx
Intelligence
87.8 #30
AAQI 88.0
Output Speed
110 t/s
ITL 9.1 ms/tok
Blended price (3:1)
$0.65 /1M
In $0.35/M · Out $1.2/M
Context
512K
≈ 1,280 A4 pages · max out 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)
Coding (83)Mathematics (84)Hard Science (GPQA) (86)Agentic & Tool Use (81)Knowledge (MMLU) (85)Vision / Multimodal (96)

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

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.

Developer Quickstart & API

Set NVIDIA_API_KEY first — get an API key
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."}]
  }'

Official Resources & Links

API deployment

Compare hosts & latency

Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.

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Scores by Source

Head-to-head Live Matchups

Live Specs & Architecture

Under the hood
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
Release date
Sep 29, 2026
Architecture
120B hybrid MoE, 12B active — vision-language
Parameters
120B/12B MoE
License
Open Weights
Context Window
512,000 tokens
Max Output
16,384 tokens
Modalities
text + image + video
Cache Pricing
read $0.05 · write $0.35 /1M
Reasoning
—
Input Price
$0.35/M
Output Price
$1.2/M
Output Speed
110 t/s
Time to First Token
—
Data Updated
Oct 1, 2026

Estimated monthly bill

Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...

Cite & embed

GitHub Markdown badge
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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}
}

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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NVIDIA Nemotron 3.5 Super-VL LLMPodium Score
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