Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 3 27B
35
1/8 categoriesGemma 4 E4B
~47
Winner · 3/8 categoriesGemma 3 27B· Gemma 4 E4B
Pick Gemma 4 E4B if you want the stronger benchmark profile. Gemma 3 27B only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 E4B is clearly ahead on the aggregate, 47 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 E4B's sharpest advantage is in coding, where it averages 52 against 16. The single biggest benchmark swing on the page is LiveCodeBench, 15% to 52%. Gemma 3 27B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Gemma 4 E4B is the reasoning model in the pair, while Gemma 3 27B 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 E4B gives you the larger context window at 128K, compared with 32K for Gemma 3 27B.
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 3 27B | Gemma 4 E4B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 29% | — |
| BrowseComp | 42% | — |
| OSWorld-Verified | 35% | — |
| CodingGemma 4 E4B wins | ||
| HumanEval | 37% | — |
| SWE-bench Verified | 16% | — |
| LiveCodeBench | 15% | 52% |
| SWE-bench Pro | 17% | — |
| Multimodal & GroundedGemma 4 E4B wins | ||
| MMMU-Pro | 39% | 52.6% |
| OfficeQA Pro | 45% | — |
| ReasoningGemma 3 27B wins | ||
| MuSR | 41% | — |
| BBH | 62% | 33.1% |
| LongBench v2 | 47% | — |
| MRCRv2 | 44% | 25.4% |
| KnowledgeGemma 4 E4B wins | ||
| MMLU | 45% | — |
| GPQA | 44% | 58.6% |
| SuperGPQA | 42% | — |
| MMLU-Pro | 50% | 69.4% |
| HLE | 3% | — |
| FrontierScience | 42% | — |
| SimpleQA | 43% | — |
| Instruction Following | ||
| IFEval | 67% | — |
| Multilingual | ||
| MGSM | 64% | — |
| MMLU-ProX | 60% | — |
| Mathematics | ||
| AIME 2023 | 45% | — |
| AIME 2024 | 47% | — |
| AIME 2025 | 46% | — |
| HMMT Feb 2023 | 41% | — |
| HMMT Feb 2024 | 43% | — |
| HMMT Feb 2025 | 42% | — |
| BRUMO 2025 | 44% | — |
| MATH-500 | 56% | — |
Gemma 4 E4B is ahead overall, 47 to 35. The biggest single separator in this matchup is LiveCodeBench, where the scores are 15% and 52%.
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 65.6 versus 35.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 E4B has the edge for coding in this comparison, averaging 52 versus 16. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Gemma 3 27B has the edge for reasoning in this comparison, averaging 44.4 versus 25.4. Inside this category, BBH is the benchmark that creates the most daylight between them.
Gemma 4 E4B has the edge for multimodal and grounded tasks in this comparison, averaging 52.6 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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