Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 2.5 Pro
65
Winner · 4/8 categoriesGemma 4 E2B
~39
0/8 categoriesGemini 2.5 Pro· Gemma 4 E2B
Pick Gemini 2.5 Pro 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.
Gemini 2.5 Pro is clearly ahead on the aggregate, 65 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 2.5 Pro's sharpest advantage is in reasoning, where it averages 61.8 against 19.1. The single biggest benchmark swing on the page is MRCRv2, 83% to 19.1%.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $5.00 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 Gemini 2.5 Pro 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. Gemini 2.5 Pro gives you the larger context window at 1M, compared with 128K for Gemma 4 E2B.
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 | Gemini 2.5 Pro | Gemma 4 E2B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 61% | — |
| BrowseComp | 72% | — |
| OSWorld-Verified | 55% | — |
| CodingGemini 2.5 Pro wins | ||
| HumanEval | 75% | — |
| SWE-bench Verified | 63.8% | — |
| LiveCodeBench | 37% | 44% |
| SWE-bench Pro | 44% | — |
| Multimodal & GroundedGemini 2.5 Pro wins | ||
| MMMU-Pro | 86% | 44.2% |
| OfficeQA Pro | 84% | — |
| VideoMMMU | 83.6% | — |
| ReasoningGemini 2.5 Pro wins | ||
| MuSR | 79% | — |
| BBH | 81% | 21.9% |
| LongBench v2 | 80% | — |
| MRCRv2 | 83% | 19.1% |
| ARC-AGI-2 | 4.9% | — |
| KnowledgeGemini 2.5 Pro wins | ||
| MMLU | 83% | — |
| GPQA | 83% | 43.4% |
| SuperGPQA | 81% | — |
| MMLU-Pro | 76% | 60% |
| HLE | 18.8% | — |
| FrontierScience | 70% | — |
| SimpleQA | 81% | — |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MGSM | 84% | — |
| MMLU-ProX | 82% | — |
| Mathematics | ||
| AIME 2023 | 84% | — |
| AIME 2024 | 92% | — |
| AIME 2025 | 85% | — |
| HMMT Feb 2023 | 80% | — |
| HMMT Feb 2024 | 82% | — |
| HMMT Feb 2025 | 81% | — |
| BRUMO 2025 | 83% | — |
| MATH-500 | 84% | — |
Gemini 2.5 Pro is ahead overall, 65 to 39. The biggest single separator in this matchup is MRCRv2, where the scores are 83% and 19.1%.
Gemini 2.5 Pro has the edge for knowledge tasks in this comparison, averaging 63.9 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemini 2.5 Pro has the edge for coding in this comparison, averaging 45.9 versus 44. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Gemini 2.5 Pro has the edge for reasoning in this comparison, averaging 61.8 versus 19.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Gemini 2.5 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 85.1 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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