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Nvidia · model passport

NVIDIA: Nemotron 3 Nano 30B A3B

nemotron-3-nano-30b-a3b
  • Open weights
  • 31.6B params
  • 262,144 context
  • Released 2025-12-04(HF repo created)
1/27axes covered
Best percentile
48.6HHEM Hallucination (Vectara)
Cohort standing
49.6median 53.9

Thin coverage — read with caution

This model appears on only 1 of 27 benchmark axes considered. The overall standing has been shrunk toward the neutral 50 to reflect the small sample, and no weaknesses have been claimed — a low percentile on 2 axes is a coverage artifact, not a measured weakness.

What the data says

Benchmarked on 1 of 27 tracked axes. Every figure below is the model's own published result on that board.

On only 1 board(s), a ranked list of strengths would be false authority — the per-axis figures are shown below instead, without a verdict.

  • HHEM Hallucination (Vectara)48.6 pct90.4%Verify →
  • OverallNot benchmarked
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • LiveBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked
  • CodingNot benchmarked
  • Instruction followingNot benchmarked
  • Hard promptsNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorynvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Parameters31.6B
ArchitectureNemotronHForCausalLM

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
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-nano-30b-a3b
Context length262,144 tokens
Max output235,929 tokens
Input price$0.06 per 1M tokens
Output price$0.24 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogit biasLogprobsMax tokensPresence 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-nano-30b-a3b","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.

  • Nemotron-3-Nano-30B-A3B stands at 49.6 (raw mean 48.6) — measured on only 1 of 27 axes, so the standing is shrunk toward the neutral 50.

    standingtemplateVerify →

  • Nemotron-3-Nano-30B-A3B stands at 49.6 versus a cohort median of 53.9 across 121 models released within 180 days — below the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →