Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categorieso3-pro
67
Winner · 4/8 categories1-bit Bonsai 1.7B· o3-pro
Pick o3-pro if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
o3-pro is clearly ahead on the aggregate, 67 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-pro's sharpest advantage is in mathematics, where it averages 89.8 against 34.4. The single biggest benchmark swing on the page is GPQA, 20.7% to 89%.
o3-pro is the reasoning model in the pair, while 1-bit Bonsai 1.7B 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. o3-pro gives you the larger context window at 200K, compared with 32K for 1-bit Bonsai 1.7B.
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 1.7B | o3-pro |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 69% |
| BrowseComp | — | 76% |
| OSWorld-Verified | — | 68% |
| Coding | ||
| HumanEval | — | 80% |
| LiveCodeBench | — | 44% |
| SWE-bench Pro | — | 55% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 70% |
| OfficeQA Pro | — | 79% |
| Reasoningo3-pro wins | ||
| MuSR | 45.1% | 84% |
| BBH | — | 89% |
| LongBench v2 | — | 81% |
| MRCRv2 | — | 81% |
| Knowledgeo3-pro wins | ||
| GPQA | 20.7% | 89% |
| MMLU | — | 88% |
| SuperGPQA | — | 87% |
| HLE | — | 26% |
| FrontierScience | — | 77% |
| SimpleQA | — | 86% |
| Instruction Followingo3-pro wins | ||
| IFEval | 63% | 82% |
| Multilingual | ||
| MGSM | — | 83% |
| MMLU-ProX | — | 80% |
| Mathematicso3-pro wins | ||
| MATH-500 | 34.4% | 89% |
| AIME 2023 | — | 90% |
| AIME 2024 | — | 92% |
| AIME 2025 | — | 91% |
| HMMT Feb 2023 | — | 86% |
| HMMT Feb 2024 | — | 88% |
| HMMT Feb 2025 | — | 87% |
| BRUMO 2025 | — | 89% |
o3-pro is ahead overall, 67 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 89%.
o3-pro has the edge for knowledge tasks in this comparison, averaging 66.8 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o3-pro has the edge for math in this comparison, averaging 89.8 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
o3-pro has the edge for reasoning in this comparison, averaging 81.8 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
o3-pro has the edge for instruction following in this comparison, averaging 82 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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