Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3
49
Winner · 2/8 categoriesGemma 4 E2B
~39
1/8 categoriesDeepSeek V3· Gemma 4 E2B
Pick DeepSeek V3 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.
DeepSeek V3 is clearly ahead on the aggregate, 49 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3's sharpest advantage is in reasoning, where it averages 48.7 against 19.1. The single biggest benchmark swing on the page is MMLU-Pro, 75.9% to 60%. Gemma 4 E2B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 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 DeepSeek V3 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 | Gemma 4 E2B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| CodingGemma 4 E2B wins | ||
| LiveCodeBench | 37.6% | 44% |
| SWE-bench Verified | 42% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 44.2% |
| ReasoningDeepSeek V3 wins | ||
| LongBench v2 | 48.7% | — |
| BBH | — | 21.9% |
| MRCRv2 | — | 19.1% |
| KnowledgeDeepSeek V3 wins | ||
| GPQA | 59.1% | 43.4% |
| MMLU-Pro | 75.9% | 60% |
| SimpleQA | 24.9% | — |
| Instruction Following | ||
| IFEval | 86.1% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME 2024 | 39.2% | — |
| MATH-500 | 90.2% | — |
DeepSeek V3 is ahead overall, 49 to 39. The biggest single separator in this matchup is MMLU-Pro, where the scores are 75.9% and 60%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 57.5 versus 54.1. 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 39.2. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
DeepSeek V3 has the edge for reasoning in this comparison, averaging 48.7 versus 19.1. Gemma 4 E2B stays close enough that the answer can still flip depending on your workload.
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