Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 26B A4B
64
1/8 categorieso3-mini
65
Winner · 3/8 categoriesGemma 4 26B A4B· o3-mini
Pick o3-mini if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if coding is the priority or you want the cheaper token bill.
o3-mini finishes one point ahead overall, 65 to 64. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
o3-mini's sharpest advantage is in reasoning, where it averages 81.1 against 44.1. The single biggest benchmark swing on the page is MRCRv2, 44.1% to 80%. Gemma 4 26B A4B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 26B A4B. That is roughly Infinityx on output cost alone. Gemma 4 26B A4B gives you the larger context window at 256K, compared with 200K for o3-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 26B A4B | o3-mini |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 67% |
| BrowseComp | — | 74% |
| OSWorld-Verified | — | 61% |
| CodingGemma 4 26B A4B wins | ||
| LiveCodeBench | 77.1% | — |
| SWE-bench Verified | — | 49.3% |
| SWE-bench Pro | — | 57% |
| Multimodal & Groundedo3-mini wins | ||
| MMMU-Pro | 73.8% | 73% |
| OfficeQA Pro | — | 76% |
| Reasoningo3-mini wins | ||
| BBH | 64.8% | — |
| MRCRv2 | 44.1% | 80% |
| LongBench v2 | — | 82% |
| Knowledgeo3-mini wins | ||
| GPQA | 82.3% | 77.2% |
| MMLU-Pro | 82.6% | — |
| HLE | 17.2% | — |
| HLE w/o tools | 8.7% | — |
| MMLU | — | 86.9% |
| FrontierScience | — | 66% |
| Instruction Following | ||
| IFEval | — | 93.9% |
| Multilingual | ||
| MMLU-ProX | — | 73% |
| Mathematics | ||
| AIME 2024 | — | 87.3% |
o3-mini is ahead overall, 65 to 64. The biggest single separator in this matchup is MRCRv2, where the scores are 44.1% and 80%.
o3-mini has the edge for knowledge tasks in this comparison, averaging 70.5 versus 56.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 26B A4B has the edge for coding in this comparison, averaging 77.1 versus 54.1. o3-mini stays close enough that the answer can still flip depending on your workload.
o3-mini has the edge for reasoning in this comparison, averaging 81.1 versus 44.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
o3-mini has the edge for multimodal and grounded tasks in this comparison, averaging 74.4 versus 73.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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