Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
1/8 categorieso1-pro
45
Winner · 3/8 categoriesGemma 4 E2B· o1-pro
Pick o1-pro if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if coding is the priority or you want the cheaper token bill.
o1-pro is clearly ahead on the aggregate, 45 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-pro's sharpest advantage is in reasoning, where it averages 56.3 against 19.1. The single biggest benchmark swing on the page is MRCRv2, 19.1% to 59%. Gemma 4 E2B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
o1-pro is also the more expensive model on tokens at $150.00 input / $600.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 E2B. That is roughly Infinityx on output cost alone. o1-pro gives you the larger context window at 200K, compared with 128K for Gemma 4 E2B.
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 | Gemma 4 E2B | o1-pro |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40% |
| BrowseComp | — | 50% |
| OSWorld-Verified | — | 32% |
| CodingGemma 4 E2B wins | ||
| LiveCodeBench | 44% | — |
| SWE-bench Pro | — | 23% |
| Multimodal & Groundedo1-pro wins | ||
| MMMU-Pro | 44.2% | 48% |
| OfficeQA Pro | — | 49% |
| Reasoningo1-pro wins | ||
| BBH | 21.9% | — |
| MRCRv2 | 19.1% | 59% |
| LongBench v2 | — | 54% |
| Knowledgeo1-pro wins | ||
| GPQA | 43.4% | 79% |
| MMLU-Pro | 60% | — |
| FrontierScience | — | 63% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MMLU-ProX | — | 52% |
| Mathematics | ||
| AIME 2024 | — | 86% |
o1-pro is ahead overall, 45 to 39. The biggest single separator in this matchup is MRCRv2, where the scores are 19.1% and 59%.
o1-pro has the edge for knowledge tasks in this comparison, averaging 69.4 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 E2B has the edge for coding in this comparison, averaging 44 versus 23. o1-pro stays close enough that the answer can still flip depending on your workload.
o1-pro has the edge for reasoning in this comparison, averaging 56.3 versus 19.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
o1-pro has the edge for multimodal and grounded tasks in this comparison, averaging 48.5 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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