Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0
62
Winner · 4/8 categoriesGemma 4 E2B
~39
0/8 categoriesDeepSeek Coder 2.0· Gemma 4 E2B
Pick DeepSeek Coder 2.0 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
DeepSeek Coder 2.0 is clearly ahead on the aggregate, 62 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek Coder 2.0's sharpest advantage is in reasoning, where it averages 73.1 against 19.1. The single biggest benchmark swing on the page is BBH, 84% to 21.9%.
DeepSeek Coder 2.0 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 Coder 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 Coder 2.0 | Gemma 4 E2B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 73% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 65% | — |
| CodingDeepSeek Coder 2.0 wins | ||
| HumanEval | 82% | — |
| SWE-bench Verified | 51% | — |
| LiveCodeBench | 45% | 44% |
| SWE-bench Pro | 61% | — |
| Multimodal & GroundedDeepSeek Coder 2.0 wins | ||
| MMMU-Pro | 50% | 44.2% |
| OfficeQA Pro | 69% | — |
| ReasoningDeepSeek Coder 2.0 wins | ||
| MuSR | 76% | — |
| BBH | 84% | 21.9% |
| LongBench v2 | 73% | — |
| MRCRv2 | 71% | 19.1% |
| KnowledgeDeepSeek Coder 2.0 wins | ||
| MMLU | 80% | — |
| GPQA | 79% | 43.4% |
| SuperGPQA | 77% | — |
| MMLU-Pro | 73% | 60% |
| HLE | 14% | — |
| FrontierScience | 72% | — |
| SimpleQA | 78% | — |
| Instruction Following | ||
| IFEval | 86% | — |
| Multilingual | ||
| MGSM | 83% | — |
| MMLU-ProX | 78% | — |
| Mathematics | ||
| AIME 2023 | 81% | — |
| AIME 2024 | 83% | — |
| AIME 2025 | 82% | — |
| HMMT Feb 2023 | 77% | — |
| HMMT Feb 2024 | 79% | — |
| HMMT Feb 2025 | 78% | — |
| BRUMO 2025 | 80% | — |
| MATH-500 | 81% | — |
DeepSeek Coder 2.0 is ahead overall, 62 to 39. The biggest single separator in this matchup is BBH, where the scores are 84% and 21.9%.
DeepSeek Coder 2.0 has the edge for knowledge tasks in this comparison, averaging 61.1 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for coding in this comparison, averaging 52.5 versus 44. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for reasoning in this comparison, averaging 73.1 versus 19.1. Inside this category, BBH is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for multimodal and grounded tasks in this comparison, averaging 58.6 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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