Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
0/8 categoriesGPT-4o mini
54
Winner · 3/8 categoriesGemma 4 E2B· GPT-4o mini
Pick GPT-4o mini if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
GPT-4o mini is clearly ahead on the aggregate, 54 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4o mini's sharpest advantage is in reasoning, where it averages 49.5 against 19.1. The single biggest benchmark swing on the page is MRCRv2, 19.1% to 50%.
GPT-4o mini is also the more expensive model on tokens at $0.15 input / $0.60 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. Gemma 4 E2B is the reasoning model in the pair, while GPT-4o mini 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.
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 | GPT-4o mini |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 58% |
| BrowseComp | — | 49% |
| OSWorld-Verified | — | 44% |
| CodingGPT-4o mini wins | ||
| LiveCodeBench | 44% | — |
| HumanEval | — | 87.2% |
| SWE-bench Pro | — | 65% |
| Multimodal & GroundedGPT-4o mini wins | ||
| MMMU-Pro | 44.2% | 66% |
| OfficeQA Pro | — | 53% |
| ReasoningGPT-4o mini wins | ||
| BBH | 21.9% | — |
| MRCRv2 | 19.1% | 50% |
| LongBench v2 | — | 49% |
| Knowledge | ||
| GPQA | 43.4% | — |
| MMLU-Pro | 60% | — |
| MMLU | — | 82% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MGSM | — | 87% |
| MMLU-ProX | — | 68% |
| Mathematics | ||
| Coming soon | ||
GPT-4o mini is ahead overall, 54 to 39. The biggest single separator in this matchup is MRCRv2, where the scores are 19.1% and 50%.
GPT-4o mini has the edge for coding in this comparison, averaging 65 versus 44. Gemma 4 E2B stays close enough that the answer can still flip depending on your workload.
GPT-4o mini has the edge for reasoning in this comparison, averaging 49.5 versus 19.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for multimodal and grounded tasks in this comparison, averaging 60.2 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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