Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-1B
~43
0/8 categorieso3-pro
67
Winner · 3/8 categoriesGranite-4.0-H-1B· o3-pro
Pick o3-pro if you want the stronger benchmark profile. Granite-4.0-H-1B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
o3-pro is clearly ahead on the aggregate, 67 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-pro's sharpest advantage is in multilingual, where it averages 81.1 against 37.8. The single biggest benchmark swing on the page is GPQA, 29.9% to 89%.
o3-pro is the reasoning model in the pair, while Granite-4.0-H-1B 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. o3-pro gives you the larger context window at 200K, compared with 128K for Granite-4.0-H-1B.
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 | Granite-4.0-H-1B | o3-pro |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 69% |
| BrowseComp | — | 76% |
| OSWorld-Verified | — | 68% |
| Coding | ||
| HumanEval | 74% | 80% |
| LiveCodeBench | — | 44% |
| SWE-bench Pro | — | 55% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 70% |
| OfficeQA Pro | — | 79% |
| Reasoning | ||
| BBH | 60.4% | 89% |
| MuSR | — | 84% |
| LongBench v2 | — | 81% |
| MRCRv2 | — | 81% |
| Knowledgeo3-pro wins | ||
| MMLU | 59.4% | 88% |
| GPQA | 29.9% | 89% |
| MMLU-Pro | 34.0% | — |
| SuperGPQA | — | 87% |
| HLE | — | 26% |
| FrontierScience | — | 77% |
| SimpleQA | — | 86% |
| Instruction Followingo3-pro wins | ||
| IFEval | 77.4% | 82% |
| Multilingualo3-pro wins | ||
| MGSM | 37.8% | 83% |
| MMLU-ProX | — | 80% |
| Mathematics | ||
| AIME 2023 | — | 90% |
| AIME 2024 | — | 92% |
| AIME 2025 | — | 91% |
| HMMT Feb 2023 | — | 86% |
| HMMT Feb 2024 | — | 88% |
| HMMT Feb 2025 | — | 87% |
| BRUMO 2025 | — | 89% |
| MATH-500 | — | 89% |
o3-pro is ahead overall, 67 to 43. The biggest single separator in this matchup is GPQA, where the scores are 29.9% and 89%.
o3-pro has the edge for knowledge tasks in this comparison, averaging 66.8 versus 32.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o3-pro has the edge for instruction following in this comparison, averaging 82 versus 77.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
o3-pro has the edge for multilingual tasks in this comparison, averaging 81.1 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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