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Nvidia · model passport

Nvidia Nemotron 3 Ultra 550b A55b Nvfp4

nvidia-nemotron-3-ultra-550b-a55b-nvfp4
  • Open weights
  • OpenMDW-1.1 licence
  • 302.8B params
  • Released 2026-06-03(HF repo created)
5/27axes covered
Overall · LMArena
1,426#109 of 396
Best percentile
72.9Coding
Cohort standing
59.6median 59.5

What the data says

Benchmarked on 5 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 32.2 percentile points between its best and worst axis — 72.9 pct on Coding versus 40.7 pct on Factuality Arena.

  • Coding72.9 pct1,473Verify →
  • Overall72.7 pct1,426Verify →
  • Hard prompts71.5 pct1,444Verify →
  • Instruction following69.2 pct1,406Verify →
  • Factuality Arena40.7 pct1,431Verify →
  • WebDev ArenaNot benchmarked
  • Vision 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
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot 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-Ultra-550B-A55B-NVFP4
Parameters302.8B
ArchitectureNemotronHForCausalLM
LicenceOpenMDW-1.1

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4"
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 →

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.

  • nvidia-nemotron-3-ultra-550b-a55b-nvfp4 stands at 59.6 (raw mean 65.4) — measured on 5 of 27 axes.

    standingtemplateVerify →

  • nvidia-nemotron-3-ultra-550b-a55b-nvfp4 stands at 59.6 versus a cohort median of 59.5 across 211 models released within 180 days — above 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 →