Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
1/8 categoriesGemma 4 E4B
~47
Winner · 1/8 categories1-bit Bonsai 1.7B· Gemma 4 E4B
Pick Gemma 4 E4B if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 E4B is clearly ahead on the aggregate, 47 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 E4B's sharpest advantage is in knowledge, where it averages 65.6 against 20.7. The single biggest benchmark swing on the page is GPQA, 20.7% to 58.6%. 1-bit Bonsai 1.7B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Gemma 4 E4B 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. Gemma 4 E4B gives you the larger context window at 128K, 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 | Gemma 4 E4B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| LiveCodeBench | — | 52% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 52.6% |
| Reasoning1-bit Bonsai 1.7B wins | ||
| MuSR | 45.1% | — |
| BBH | — | 33.1% |
| MRCRv2 | — | 25.4% |
| KnowledgeGemma 4 E4B wins | ||
| GPQA | 20.7% | 58.6% |
| MMLU-Pro | — | 69.4% |
| Instruction Following | ||
| IFEval | 63% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | 34.4% | — |
Gemma 4 E4B is ahead overall, 47 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 58.6%.
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 65.6 versus 20.7. Inside this category, GPQA 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 25.4. Gemma 4 E4B stays close enough that the answer can still flip depending on your workload.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.