Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
Winner · 3/8 categorieso1-pro
45
1/8 categoriesGemma 4 31B· o1-pro
Pick Gemma 4 31B if you want the stronger benchmark profile. o1-pro only becomes the better choice if knowledge is the priority.
Gemma 4 31B is clearly ahead on the aggregate, 73 to 45. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 31B's sharpest advantage is in coding, where it averages 80 against 23. The single biggest benchmark swing on the page is MMMU-Pro, 76.9% to 48%. o1-pro does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o1-pro is also the more expensive model on tokens at $150.00 input / $600.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 31B. That is roughly Infinityx on output cost alone. Gemma 4 31B gives you the larger context window at 256K, compared with 200K for o1-pro.
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 | Gemma 4 31B | o1-pro |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40% |
| BrowseComp | — | 50% |
| OSWorld-Verified | — | 32% |
| CodingGemma 4 31B wins | ||
| LiveCodeBench | 80% | — |
| SWE-bench Pro | — | 23% |
| Multimodal & GroundedGemma 4 31B wins | ||
| MMMU-Pro | 76.9% | 48% |
| OfficeQA Pro | — | 49% |
| ReasoningGemma 4 31B wins | ||
| BBH | 74.4% | — |
| MRCRv2 | 66.4% | 59% |
| LongBench v2 | — | 54% |
| Knowledgeo1-pro wins | ||
| GPQA | 84.3% | 79% |
| MMLU-Pro | 85.2% | — |
| HLE | 26.5% | — |
| HLE w/o tools | 19.5% | — |
| FrontierScience | — | 63% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MMLU-ProX | — | 52% |
| Mathematics | ||
| AIME 2024 | — | 86% |
Gemma 4 31B is ahead overall, 73 to 45. The biggest single separator in this matchup is MMMU-Pro, where the scores are 76.9% and 48%.
o1-pro has the edge for knowledge tasks in this comparison, averaging 69.4 versus 61.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 23. o1-pro stays close enough that the answer can still flip depending on your workload.
Gemma 4 31B has the edge for reasoning in this comparison, averaging 66.4 versus 56.3. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 48.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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