Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek LLM 2.0
57
Winner · 2/8 categoriesGemma 4 E2B
~39
1/8 categoriesDeepSeek LLM 2.0· Gemma 4 E2B
Pick DeepSeek LLM 2.0 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.
DeepSeek LLM 2.0 is clearly ahead on the aggregate, 57 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek LLM 2.0's sharpest advantage is in multimodal & grounded, where it averages 64.5 against 44.2. The single biggest benchmark swing on the page is BBH, 81% to 21.9%. 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 DeepSeek LLM 2.0 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 LLM 2.0 | Gemma 4 E2B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 57% | — |
| CodingGemma 4 E2B wins | ||
| HumanEval | 73% | — |
| SWE-bench Verified | 46% | — |
| LiveCodeBench | 39% | 44% |
| SWE-bench Pro | 46% | — |
| Multimodal & GroundedDeepSeek LLM 2.0 wins | ||
| MMMU-Pro | 60% | 44.2% |
| OfficeQA Pro | 70% | — |
| Reasoning | ||
| BBH | 81% | 21.9% |
| MRCRv2 | — | 19.1% |
| KnowledgeDeepSeek LLM 2.0 wins | ||
| MMLU | 79% | — |
| GPQA | 78% | 43.4% |
| SuperGPQA | 76% | — |
| MMLU-Pro | 72% | 60% |
| HLE | 12% | — |
| FrontierScience | 67% | — |
| SimpleQA | 77% | — |
| Instruction Following | ||
| IFEval | 85% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME 2023 | 80% | — |
| AIME 2024 | 82% | — |
| AIME 2025 | 81% | — |
| HMMT Feb 2023 | 76% | — |
| HMMT Feb 2024 | 78% | — |
| HMMT Feb 2025 | 77% | — |
| MATH-500 | 83% | — |
DeepSeek LLM 2.0 is ahead overall, 57 to 39. The biggest single separator in this matchup is BBH, where the scores are 81% and 21.9%.
DeepSeek LLM 2.0 has the edge for knowledge tasks in this comparison, averaging 59.1 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 E2B has the edge for coding in this comparison, averaging 44 versus 43.3. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for multimodal and grounded tasks in this comparison, averaging 64.5 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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