Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E4B
~47
Winner · 2/8 categoriesGPT-4.1 nano
44
2/8 categoriesGemma 4 E4B· GPT-4.1 nano
Pick Gemma 4 E4B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if reasoning is the priority or you need the larger 1M context window.
Gemma 4 E4B has the cleaner overall profile here, landing at 47 versus 44. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Gemma 4 E4B's sharpest advantage is in coding, where it averages 52 against 18. The single biggest benchmark swing on the page is MRCRv2, 25.4% to 73%. GPT-4.1 nano does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 E4B. That is roughly Infinityx on output cost alone. Gemma 4 E4B is the reasoning model in the pair, while GPT-4.1 nano 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. GPT-4.1 nano gives you the larger context window at 1M, compared with 128K for Gemma 4 E4B.
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 E4B | GPT-4.1 nano |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 43% |
| BrowseComp | — | 62% |
| OSWorld-Verified | — | 42% |
| CodingGemma 4 E4B wins | ||
| LiveCodeBench | 52% | — |
| SWE-bench Pro | — | 18% |
| Multimodal & GroundedGPT-4.1 nano wins | ||
| MMMU-Pro | 52.6% | 53% |
| OfficeQA Pro | — | 67% |
| ReasoningGPT-4.1 nano wins | ||
| BBH | 33.1% | — |
| MRCRv2 | 25.4% | 73% |
| LongBench v2 | — | 75% |
| KnowledgeGemma 4 E4B wins | ||
| GPQA | 58.6% | 50.3% |
| MMLU-Pro | 69.4% | — |
| MMLU | — | 80.1% |
| FrontierScience | — | 51% |
| Instruction Following | ||
| IFEval | — | 83.2% |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME 2024 | — | 9.8% |
Gemma 4 E4B is ahead overall, 47 to 44. The biggest single separator in this matchup is MRCRv2, where the scores are 25.4% and 73%.
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 65.6 versus 50.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 E4B has the edge for coding in this comparison, averaging 52 versus 18. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
GPT-4.1 nano has the edge for reasoning in this comparison, averaging 74.1 versus 25.4. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for multimodal and grounded tasks in this comparison, averaging 59.3 versus 52.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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