Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
1/8 categoriesGemma 4 E2B
~39
1/8 categories1-bit Bonsai 1.7B· Gemma 4 E2B
Treat this as a split decision. 1-bit Bonsai 1.7B makes more sense if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Gemma 4 E2B is the better fit if knowledge is the priority or you need the larger 128K context window.
1-bit Bonsai 1.7B and Gemma 4 E2B finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
Gemma 4 E2B 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 E2B 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 E2B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| LiveCodeBench | — | 44% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 44.2% |
| Reasoning1-bit Bonsai 1.7B wins | ||
| MuSR | 45.1% | — |
| BBH | — | 21.9% |
| MRCRv2 | — | 19.1% |
| KnowledgeGemma 4 E2B wins | ||
| GPQA | 20.7% | 43.4% |
| MMLU-Pro | — | 60% |
| Instruction Following | ||
| IFEval | 63% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | 34.4% | — |
1-bit Bonsai 1.7B and Gemma 4 E2B are tied on overall score, so the right pick depends on which category matters most for your use case.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 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 19.1. Gemma 4 E2B stays close enough that the answer can still flip depending on your workload.
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