Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
Winner · 1/8 categoriesGemma 3 27B
34
3/8 categories1-bit Bonsai 1.7B· Gemma 3 27B
Pick 1-bit Bonsai 1.7B if you want the stronger benchmark profile. Gemma 3 27B only becomes the better choice if knowledge is the priority.
1-bit Bonsai 1.7B is clearly ahead on the aggregate, 39 to 34. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
1-bit Bonsai 1.7B's sharpest advantage is in reasoning, where it averages 45.1 against 44.4. The single biggest benchmark swing on the page is GPQA, 20.7% to 44%. Gemma 3 27B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
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 | Gemma 3 27B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 29% |
| BrowseComp | — | 42% |
| OSWorld-Verified | — | 35% |
| Coding | ||
| HumanEval | — | 37% |
| SWE-bench Verified | — | 16% |
| LiveCodeBench | — | 15% |
| SWE-bench Pro | — | 17% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 39% |
| OfficeQA Pro | — | 45% |
| Reasoning1-bit Bonsai 1.7B wins | ||
| MuSR | 45.1% | 41% |
| BBH | — | 62% |
| LongBench v2 | — | 47% |
| MRCRv2 | — | 44% |
| KnowledgeGemma 3 27B wins | ||
| GPQA | 20.7% | 44% |
| MMLU | — | 45% |
| SuperGPQA | — | 42% |
| MMLU-Pro | — | 50% |
| HLE | — | 3% |
| FrontierScience | — | 42% |
| SimpleQA | — | 43% |
| Instruction FollowingGemma 3 27B wins | ||
| IFEval | 63% | 67% |
| Multilingual | ||
| MGSM | — | 64% |
| MMLU-ProX | — | 60% |
| MathematicsGemma 3 27B wins | ||
| MATH-500 | 34.4% | 56% |
| AIME 2023 | — | 45% |
| AIME 2024 | — | 47% |
| AIME 2025 | — | 46% |
| HMMT Feb 2023 | — | 41% |
| HMMT Feb 2024 | — | 43% |
| HMMT Feb 2025 | — | 42% |
| BRUMO 2025 | — | 44% |
1-bit Bonsai 1.7B is ahead overall, 39 to 34. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 44%.
Gemma 3 27B has the edge for knowledge tasks in this comparison, averaging 35.2 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 3 27B has the edge for math in this comparison, averaging 47.8 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
1-bit Bonsai 1.7B has the edge for reasoning in this comparison, averaging 45.1 versus 44.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Gemma 3 27B has the edge for instruction following in this comparison, averaging 67 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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