Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
0/8 categorieso3
65
Winner · 4/8 categories1-bit Bonsai 4B· o3
Pick o3 if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
o3 is clearly ahead on the aggregate, 65 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3's sharpest advantage is in knowledge, where it averages 67.4 against 28.7. The single biggest benchmark swing on the page is GPQA, 28.7% to 87%.
o3 is also the more expensive model on tokens at $10.00 input / $40.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 4B. That is roughly Infinityx on output cost alone. o3 is the reasoning model in the pair, while 1-bit Bonsai 4B 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 gives you the larger context window at 200K, compared with 32K for 1-bit Bonsai 4B.
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 4B | o3 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 71% |
| BrowseComp | — | 75% |
| OSWorld-Verified | — | 65% |
| Coding | ||
| HumanEval | — | 78% |
| SWE-bench Verified | — | 71.7% |
| LiveCodeBench | — | 40% |
| SWE-bench Pro | — | 58% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 70% |
| OfficeQA Pro | — | 75% |
| Reasoningo3 wins | ||
| MuSR | 41.4% | 82% |
| BBH | — | 86% |
| LongBench v2 | — | 82% |
| MRCRv2 | — | 81% |
| ARC-AGI-2 | — | 3% |
| Knowledgeo3 wins | ||
| GPQA | 28.7% | 87% |
| MMLU | — | 86% |
| SuperGPQA | — | 85% |
| MMLU-Pro | — | 75% |
| HLE | — | 24% |
| FrontierScience | — | 77% |
| SimpleQA | — | 84% |
| Instruction Followingo3 wins | ||
| IFEval | 69.6% | 85% |
| Multilingual | ||
| MGSM | — | 83% |
| MMLU-ProX | — | 80% |
| Mathematicso3 wins | ||
| MATH-500 | 65.8% | 88% |
| AIME 2023 | — | 88% |
| AIME 2024 | — | 90% |
| AIME 2025 | — | 89% |
| HMMT Feb 2023 | — | 84% |
| HMMT Feb 2024 | — | 86% |
| HMMT Feb 2025 | — | 85% |
| BRUMO 2025 | — | 87% |
o3 is ahead overall, 65 to 44. The biggest single separator in this matchup is GPQA, where the scores are 28.7% and 87%.
o3 has the edge for knowledge tasks in this comparison, averaging 67.4 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o3 has the edge for math in this comparison, averaging 88.1 versus 65.8. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
o3 has the edge for reasoning in this comparison, averaging 62 versus 41.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
o3 has the edge for instruction following in this comparison, averaging 85 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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