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NVIDIA: Nemotron 3.5 Super VL

nemotron-3-5-super-vl
  • Open weights
  • 1,048,576 context
0/27axes covered

How to run it

Run it yourself

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nvidia/NVIDIA-Nemotron-3.5-Super-VL"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Use it hosted

API model IDnvidia/nemotron-3.5-super-vl
Context length1,048,576 tokens
Max output235,929 tokens
Acceptstext, image, video
Input price$0.10 per 1M tokens
Cached input price$0.08 per 1M tokens
Output price$0.40 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogit biasLogprobsMax tokensMin-pPresence penaltyReasoningRepetition penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTool choiceTool callingTop-kTop logprobsTop-p

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"nvidia/nemotron-3.5-super-vl","messages":[{"role":"user","content":"Hello"}]}'

View on OpenRouter →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

No benchmark we track has published a result for this model yet, so there is no score card — only the run guide above. It will appear here once any board we ingest scores it.

How the leaderboards work →