Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
Winner · 2/8 categoriesNemotron 3 Nano 30B
42
2/8 categories1-bit Bonsai 8B· Nemotron 3 Nano 30B
Pick 1-bit Bonsai 8B if you want the stronger benchmark profile. Nemotron 3 Nano 30B only becomes the better choice if knowledge is the priority.
1-bit Bonsai 8B is clearly ahead on the aggregate, 50 to 42. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
1-bit Bonsai 8B's sharpest advantage is in mathematics, where it averages 66 against 61.1. The single biggest benchmark swing on the page is GPQA, 30% to 56%. Nemotron 3 Nano 30B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
1-bit Bonsai 8B gives you the larger context window at 64K, compared with 32K for Nemotron 3 Nano 30B.
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 | 1-bit Bonsai 8B | Nemotron 3 Nano 30B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 38% |
| BrowseComp | — | 43% |
| OSWorld-Verified | — | 39% |
| Coding | ||
| HumanEval | — | 49% |
| SWE-bench Verified | — | 26% |
| SWE-bench Pro | — | 27% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 38% |
| OfficeQA Pro | — | 54% |
| ReasoningNemotron 3 Nano 30B wins | ||
| MuSR | 50% | 52% |
| BBH | — | 72% |
| LongBench v2 | — | 51% |
| MRCRv2 | — | 51% |
| KnowledgeNemotron 3 Nano 30B wins | ||
| GPQA | 30% | 56% |
| MMLU | — | 57% |
| SuperGPQA | — | 54% |
| MMLU-Pro | — | 65% |
| HLE | — | 1% |
| FrontierScience | — | 54% |
| SimpleQA | — | 54% |
| Instruction Following1-bit Bonsai 8B wins | ||
| IFEval | 79.8% | 78% |
| Multilingual | ||
| MGSM | — | 75% |
| MMLU-ProX | — | 70% |
| Mathematics1-bit Bonsai 8B wins | ||
| MATH-500 | 66% | 73% |
| AIME 2023 | — | 57% |
| AIME 2024 | — | 59% |
| AIME 2025 | — | 58% |
| HMMT Feb 2023 | — | 53% |
| HMMT Feb 2024 | — | 55% |
| HMMT Feb 2025 | — | 54% |
| BRUMO 2025 | — | 56% |
1-bit Bonsai 8B is ahead overall, 50 to 42. The biggest single separator in this matchup is GPQA, where the scores are 30% and 56%.
Nemotron 3 Nano 30B has the edge for knowledge tasks in this comparison, averaging 44.5 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
1-bit Bonsai 8B has the edge for math in this comparison, averaging 66 versus 61.1. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Nemotron 3 Nano 30B has the edge for reasoning in this comparison, averaging 51.3 versus 50. Inside this category, MuSR is the benchmark that creates the most daylight between them.
1-bit Bonsai 8B has the edge for instruction following in this comparison, averaging 79.8 versus 78. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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