Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
Winner · 3/8 categoriesGPT-4o mini
54
0/8 categoriesGemma 4 31B· GPT-4o mini
Pick Gemma 4 31B if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 31B is clearly ahead on the aggregate, 73 to 54. 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 reasoning, where it averages 66.4 against 49.5. The single biggest benchmark swing on the page is MRCRv2, 66.4% to 50%.
GPT-4o mini is also the more expensive model on tokens at $0.15 input / $0.60 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 is the reasoning model in the pair, while GPT-4o mini 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. Gemma 4 31B gives you the larger context window at 256K, compared with 128K for GPT-4o mini.
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 | GPT-4o mini |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 58% |
| BrowseComp | — | 49% |
| OSWorld-Verified | — | 44% |
| CodingGemma 4 31B wins | ||
| LiveCodeBench | 80% | — |
| HumanEval | — | 87.2% |
| SWE-bench Pro | — | 65% |
| Multimodal & GroundedGemma 4 31B wins | ||
| MMMU-Pro | 76.9% | 66% |
| OfficeQA Pro | — | 53% |
| ReasoningGemma 4 31B wins | ||
| BBH | 74.4% | — |
| MRCRv2 | 66.4% | 50% |
| LongBench v2 | — | 49% |
| Knowledge | ||
| GPQA | 84.3% | — |
| MMLU-Pro | 85.2% | — |
| HLE | 26.5% | — |
| HLE w/o tools | 19.5% | — |
| MMLU | — | 82% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MGSM | — | 87% |
| MMLU-ProX | — | 68% |
| Mathematics | ||
| Coming soon | ||
Gemma 4 31B is ahead overall, 73 to 54. The biggest single separator in this matchup is MRCRv2, where the scores are 66.4% and 50%.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 65. GPT-4o mini 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 49.5. 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 60.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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