Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Holo3-122B-A10B
~79
Winner · 1/8 categoriesNemotron Ultra 253B
41
0/8 categoriesHolo3-122B-A10B· Nemotron Ultra 253B
Pick Holo3-122B-A10B if you want the stronger benchmark profile. Nemotron Ultra 253B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Holo3-122B-A10B is clearly ahead on the aggregate, 79 to 41. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Holo3-122B-A10B's sharpest advantage is in agentic, where it averages 78.9 against 46.7. The single biggest benchmark swing on the page is OSWorld-Verified, 78.8% to 45%.
Holo3-122B-A10B is also the more expensive model on tokens at $0.40 input / $3.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Nemotron Ultra 253B. That is roughly Infinityx on output cost alone. Nemotron Ultra 253B is the reasoning model in the pair, while Holo3-122B-A10B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Holo3-122B-A10B gives you the larger context window at 64K, compared with 32K for Nemotron Ultra 253B.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Holo3-122B-A10B | Nemotron Ultra 253B |
|---|---|---|
| AgenticHolo3-122B-A10B wins | ||
| OSWorld-Verified | 78.8% | 45% |
| Terminal-Bench 2.0 | — | 46% |
| BrowseComp | — | 50% |
| Coding | ||
| HumanEval | — | 41% |
| SWE-bench Verified | — | 31% |
| LiveCodeBench | — | 30% |
| SWE-bench Pro | — | 38% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 37% |
| OfficeQA Pro | — | 54% |
| Reasoning | ||
| MuSR | — | 45% |
| BBH | — | 77% |
| LongBench v2 | — | 55% |
| MRCRv2 | — | 56% |
| Knowledge | ||
| MMLU | — | 49% |
| GPQA | — | 48% |
| SuperGPQA | — | 46% |
| MMLU-Pro | — | 63% |
| HLE | — | 11% |
| FrontierScience | — | 49% |
| SimpleQA | — | 47% |
| Instruction Following | ||
| IFEval | — | 78% |
| Multilingual | ||
| MGSM | — | 74% |
| MMLU-ProX | — | 68% |
| Mathematics | ||
| AIME 2023 | — | 49% |
| AIME 2024 | — | 51% |
| AIME 2025 | — | 50% |
| HMMT Feb 2023 | — | 45% |
| HMMT Feb 2024 | — | 47% |
| HMMT Feb 2025 | — | 46% |
| BRUMO 2025 | — | 48% |
| MATH-500 | — | 74% |
Holo3-122B-A10B is ahead overall, 79 to 41. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 78.8% and 45%.
Holo3-122B-A10B has the edge for agentic tasks in this comparison, averaging 78.9 versus 46.7. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
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