Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
2/8 categoriesZ-1
44
Winner · 2/8 categoriesGemma 4 E2B· Z-1
Pick Z-1 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if coding is the priority or you want the stronger reasoning-first profile.
Z-1 is clearly ahead on the aggregate, 44 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Z-1's sharpest advantage is in reasoning, where it averages 54.2 against 19.1. The single biggest benchmark swing on the page is BBH, 21.9% to 74%. Gemma 4 E2B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Gemma 4 E2B is the reasoning model in the pair, while Z-1 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 | Z-1 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 39% |
| BrowseComp | — | 49% |
| OSWorld-Verified | — | 41% |
| CodingGemma 4 E2B wins | ||
| LiveCodeBench | 44% | 22% |
| HumanEval | — | 44% |
| SWE-bench Verified | — | 33% |
| SWE-bench Pro | — | 30% |
| Multimodal & GroundedZ-1 wins | ||
| MMMU-Pro | 44.2% | 46% |
| OfficeQA Pro | — | 56% |
| ReasoningZ-1 wins | ||
| BBH | 21.9% | 74% |
| MRCRv2 | 19.1% | 57% |
| MuSR | — | 48% |
| LongBench v2 | — | 56% |
| KnowledgeGemma 4 E2B wins | ||
| GPQA | 43.4% | — |
| MMLU-Pro | 60% | 64% |
| MMLU | — | 52% |
| SuperGPQA | — | 49% |
| HLE | — | 6% |
| FrontierScience | — | 51% |
| SimpleQA | — | 50% |
| Instruction Following | ||
| IFEval | — | 80% |
| Multilingual | ||
| MGSM | — | 74% |
| MMLU-ProX | — | 72% |
| Mathematics | ||
| AIME 2023 | — | 52% |
| AIME 2024 | — | 54% |
| AIME 2025 | — | 53% |
| HMMT Feb 2023 | — | 48% |
| HMMT Feb 2024 | — | 50% |
| HMMT Feb 2025 | — | 49% |
| BRUMO 2025 | — | 51% |
| MATH-500 | — | 73% |
Z-1 is ahead overall, 44 to 39. The biggest single separator in this matchup is BBH, where the scores are 21.9% and 74%.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 versus 42.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 E2B has the edge for coding in this comparison, averaging 44 versus 27.6. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Z-1 has the edge for reasoning in this comparison, averaging 54.2 versus 19.1. Inside this category, BBH is the benchmark that creates the most daylight between them.
Z-1 has the edge for multimodal and grounded tasks in this comparison, averaging 50.5 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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