- MMLU-Pro 78.3
- AIME25 (no tools) 89.1
- GPQA (no tools) 73
- LiveCodeBench (v6 2025-08–2025-05) 68.3
- SciCode (subtask) 33.3
- HLE (no tools) 10.6
- MiniF2F pass@1 50
- Terminal Bench (hard subset) 8.5
- SWE-Bench (OpenHands) 38.8
- TauBench V2 (Average) of Airline 48.0 / Retail 56.9 / Telecom 42.2 49
- BFCL v4 53.8
- IFBench (prompt) 71.5
- Scale AI Multi Challenge 38.5
- Arena-Hard-V2 (Average); Hard Prompt 72.1, Creative Writing 63.2 67.7
- AA-LCR 35.9
- RULER-100@1M 86.3
- MMLU-ProX (avg over langs) 59.5
- WMT24++ (en->xx) 86.2
Nemotron 3 Nano 30B-A3B
Nemotron 3 Nano 30B-A3B places in the top three on 2 benchmarks.
Nemotron 3 Nano 30B-A3B
Open weights Small
- IFBench 71.5% #38 /80
- SciCode 33.3% #40 /49
- AA-LCR (Artificial Analysis Long Context Reasoning) 35.9% #35 /38
Where it ranks
Its best score on each benchmark, and where that ranks among every model with a score there. We rank each model once, at its best setting.
Knowledge and language
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| WMT24++ | 86.2% | #3 of 15 | Lab model card | |
| IFStruct | 86.8% | #3 of 10 | Official leaderboard | |
| IFBench | 71.5% | #38 of 80 | Lab model card | |
| BullshitBench | 27.3% | #52 of 107 | Official leaderboard | |
| Vectara Hallucination Leaderboard (2025 dataset) | 9.60% | #56 of 104 | Official leaderboard | |
| MMLU-Pro | 78.3% | #91 of 167 | Lab model card | |
| Arena-Hard v2.0 | 67.7% | #14 of 24 | Lab model card | |
| BullshitBench V2 | 28% | #80 of 129 | Official leaderboard | |
| RULER | 86.3% | #27 of 42 | Lab model card | |
| LMArena (Elo) | 1,314 | #217 of 335 | Official leaderboard | |
| MMLU-ProX | 59.5% | #20 of 23 | Lab model card | |
| MultiChallenge | 38.5% | #37 of 42 | Lab model card | |
| SEA-HELM | 46.9 | #63 of 71 | Official leaderboard | |
| Humanity's Last Exam | 10.6% | #139 of 154 | Lab model card | |
| AA-LCR (Artificial Analysis Long Context Reasoning) | 35.9% | #35 of 38 | Lab model card |
Math and science
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| AIME 2025 | 89.1% | #55 of 143 | Lab model card | |
| GPQA Diamond | 73% | #161 of 302 | Lab model card | |
| miniF2F | 50% | #5 of 5 | Lab model card |
Coding
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| LiveCodeBench v6 | 68.3% | #61 of 90 | Lab model card | |
| SciCode | 33.3% | #40 of 49 | Lab model card | |
| SWE-bench Verified | 38.8% | #147 of 155 | Lab model card |
Agents and tools
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| LLM Chess | -200 | #93 of 123 | Official leaderboard | |
| Terminal-Bench Hard | 8.51% | #10 of 13 | Lab model card | |
| BFCL V4 | 53.8% | #24 of 30 | Lab model card | |
| tau2-bench | 49% | #72 of 79 | Lab model card | |
| GAIA | 0% | #64 of 64 | Official leaderboard |
Safety and security
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| BioTIER Permit | 99.6% | #28 of 69 | Official leaderboard | |
| Human Pathogen Capabilities Test v2 (HPCT v2) | 36.7% | #59 of 71 | Official leaderboard | |
| Agentic BAIM-LLM Evaluation (ABLE) | 24.2% | #15 of 18 | Official leaderboard | |
| BioTIER Refuse | 20.2% | #60 of 69 | Official leaderboard | |
| Human Pathogen Capabilities Test | 36.2% | #75 of 86 | Official leaderboard | |
| World-Class Bio | 25.1% | #66 of 74 | Official leaderboard | |
| Molecular Biology Capabilities Test | 38.5% | #81 of 90 | Official leaderboard | |
| nocot-bench (NCRI) | 47.3 | #170 of 185 | Official leaderboard | |
| ABC Bench Fragment Design | 42.6% | #26 of 27 | Official leaderboard | |
| ABC Bench Liquid-Handling Robot | 0% | #34 of 34 | Official leaderboard | |
| ABC Bench Screening Evasion | 22.4% | #17 of 17 | Official leaderboard |
Its lab's numbers put it at 133.3, number 247 of 404
The Self-Reported Index fits one score to the 17 capability results NVIDIA reported for this model, on the scale of the Epoch Capabilities Index. Labs choose which benchmarks to show and how to run them, so read it as the lab's claim, not an independent measurement. The 90% interval runs from 130.8 to 135.7. How we compute it.
- AIME 2025 89.1
- RULER 86.3
- WMT24++ 86.2
- MMLU-Pro 78.3
- GPQA Diamond 73
- IFBench 71.5
- LiveCodeBench v6 68.3
- Arena-Hard v2.0 67.7
- MMLU-ProX 59.5
- BFCL V4 53.8
- tau2-bench 49
- SWE-bench Verified 38.8
- MultiChallenge 38.5
- AA-LCR (Artificial Analysis Long Context Reasoning) 35.9
- SciCode 33.3
- Humanity's Last Exam 10.6
- Terminal-Bench Hard 8.51
Its lab's number next to a leaderboard
Where the benchmark's own leaderboard, or Epoch AI's run, also has this model. Gaps mostly come from different harnesses, effort levels and task subsets. Every pair.
Its launch post
The benchmarks the lab chose to headline, with the scores it gave.
Its system card
Every result in the longer documents the lab published with it.
Capabilities
- MMLU-Pro 78.3
- AIME 2025 89.1
- GPQA Diamond 73
- LiveCodeBench v6 68.3
- SciCode 33.3
- Humanity's Last Exam 10.6
- miniF2F 50
- Terminal-Bench Hard 8.51
- SWE-bench Verified 38.8
- tau2-bench 49
- BFCL V4 53.8
- IFBench 71.5
- MultiChallenge 38.5
- Arena-Hard v2.0 67.7
- AA-LCR (Artificial Analysis Long Context Reasoning) 35.9
- RULER 86.3
- MMLU-ProX 59.5
- WMT24++ 86.2