Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.2
61
Winner · 3/8 categoriesGemma 4 E2B
~39
1/8 categoriesDeepSeek V3.2· Gemma 4 E2B
Pick DeepSeek V3.2 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
DeepSeek V3.2 is clearly ahead on the aggregate, 61 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2's sharpest advantage is in reasoning, where it averages 49 against 19.1. The single biggest benchmark swing on the page is MRCRv2, 70% to 19.1%. Gemma 4 E2B does hit back in knowledge, 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 DeepSeek V3.2 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 | DeepSeek V3.2 | Gemma 4 E2B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 60% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 55% | — |
| Claw-Eval | 51.0% | — |
| DeepPlanning | 19.0% | — |
| VITA-Bench | 18.5% | — |
| CodingDeepSeek V3.2 wins | ||
| HumanEval | 76% | — |
| SWE-bench Verified | 45% | — |
| SWE-Rebench | 60.9% | — |
| React Native Evals | 69% | — |
| LiveCodeBench | — | 44% |
| Multimodal & GroundedDeepSeek V3.2 wins | ||
| MMMU-Pro | 61% | 44.2% |
| OfficeQA Pro | 72% | — |
| ReasoningDeepSeek V3.2 wins | ||
| LongBench v2 | 69% | — |
| MRCRv2 | 70% | 19.1% |
| ARC-AGI-2 | 4% | — |
| BBH | — | 21.9% |
| KnowledgeGemma 4 E2B wins | ||
| GPQA | 83% | 43.4% |
| HLE | 11% | — |
| FrontierScience | 72% | — |
| MMLU-Pro | — | 60% |
| Instruction Following | ||
| IFEval | 85% | — |
| Multilingual | ||
| MGSM | 84% | — |
| MMLU-ProX | 81% | — |
| Mathematics | ||
| AIME 2023 | 84% | — |
| AIME 2024 | 86% | — |
| AIME 2025 | 85% | — |
| HMMT Feb 2023 | 80% | — |
| MATH-500 | 81% | — |
DeepSeek V3.2 is ahead overall, 61 to 39. The biggest single separator in this matchup is MRCRv2, where the scores are 70% and 19.1%.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 versus 48. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 56.1 versus 44. Gemma 4 E2B stays close enough that the answer can still flip depending on your workload.
DeepSeek V3.2 has the edge for reasoning in this comparison, averaging 49 versus 19.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for multimodal and grounded tasks in this comparison, averaging 66 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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