Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
0/8 categorieso3-mini
65
Winner · 3/8 categories1-bit Bonsai 8B· o3-mini
Pick o3-mini if you want the stronger benchmark profile. 1-bit Bonsai 8B 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-mini is clearly ahead on the aggregate, 65 to 50. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini's sharpest advantage is in knowledge, where it averages 70.5 against 30. The single biggest benchmark swing on the page is GPQA, 30% to 77.2%.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 8B. That is roughly Infinityx on output cost alone. o3-mini is the reasoning model in the pair, while 1-bit Bonsai 8B 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-mini gives you the larger context window at 200K, compared with 64K for 1-bit Bonsai 8B.
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 | o3-mini |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 67% |
| BrowseComp | — | 74% |
| OSWorld-Verified | — | 61% |
| Coding | ||
| SWE-bench Verified | — | 49.3% |
| SWE-bench Pro | — | 57% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 73% |
| OfficeQA Pro | — | 76% |
| Reasoningo3-mini wins | ||
| MuSR | 50% | — |
| LongBench v2 | — | 82% |
| MRCRv2 | — | 80% |
| Knowledgeo3-mini wins | ||
| GPQA | 30% | 77.2% |
| MMLU | — | 86.9% |
| FrontierScience | — | 66% |
| Instruction Followingo3-mini wins | ||
| IFEval | 79.8% | 93.9% |
| Multilingual | ||
| MMLU-ProX | — | 73% |
| Mathematics | ||
| MATH-500 | 66% | — |
| AIME 2024 | — | 87.3% |
o3-mini is ahead overall, 65 to 50. The biggest single separator in this matchup is GPQA, where the scores are 30% and 77.2%.
o3-mini has the edge for knowledge tasks in this comparison, averaging 70.5 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o3-mini has the edge for reasoning in this comparison, averaging 81.1 versus 50. 1-bit Bonsai 8B stays close enough that the answer can still flip depending on your workload.
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 79.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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