Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
2/8 categoriesGPT-4o
50
Winner · 2/8 categoriesGemma 4 E2B· GPT-4o
Pick GPT-4o 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.
GPT-4o is clearly ahead on the aggregate, 50 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4o's sharpest advantage is in reasoning, where it averages 62.3 against 19.1. The single biggest benchmark swing on the page is BBH, 21.9% to 82%. Gemma 4 E2B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 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. Gemma 4 E2B is the reasoning model in the pair, while GPT-4o 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 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 49% |
| OSWorld-Verified | — | 48% |
| CodingGemma 4 E2B wins | ||
| LiveCodeBench | 44% | 38% |
| HumanEval | — | 58% |
| SWE-bench Verified | — | 20% |
| SWE-bench Pro | — | 29% |
| Multimodal & GroundedGPT-4o wins | ||
| MMMU-Pro | 44.2% | — |
| OfficeQA Pro | — | 70% |
| VideoMMMU | — | 61.2% |
| ReasoningGPT-4o wins | ||
| BBH | 21.9% | 82% |
| MRCRv2 | 19.1% | 63% |
| MuSR | — | 62% |
| LongBench v2 | — | 62% |
| KnowledgeGemma 4 E2B wins | ||
| GPQA | 43.4% | 66% |
| MMLU-Pro | 60% | 64% |
| MMLU | — | 66% |
| HLE | — | 1% |
| FrontierScience | — | 58% |
| Instruction Following | ||
| IFEval | — | 82% |
| Multilingual | ||
| MMLU-ProX | — | 72% |
| Mathematics | ||
| AIME 2023 | — | 66% |
| AIME 2024 | — | 68% |
| AIME 2025 | — | 67% |
| HMMT Feb 2023 | — | 62% |
| HMMT Feb 2024 | — | 64% |
| BRUMO 2025 | — | 65% |
GPT-4o is ahead overall, 50 to 39. The biggest single separator in this matchup is BBH, where the scores are 21.9% and 82%.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 versus 43.6. 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 30.4. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-4o has the edge for reasoning in this comparison, averaging 62.3 versus 19.1. Inside this category, BBH is the benchmark that creates the most daylight between them.
GPT-4o has the edge for multimodal and grounded tasks in this comparison, averaging 70 versus 44.2. Gemma 4 E2B stays close enough that the answer can still flip depending on your workload.
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