Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-1B
~40
0/8 categorieso1-pro
45
Winner · 2/8 categoriesGranite-4.0-1B· o1-pro
Pick o1-pro if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
o1-pro is clearly ahead on the aggregate, 45 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-pro's sharpest advantage is in knowledge, where it averages 69.4 against 31.7. The single biggest benchmark swing on the page is GPQA, 29.7% to 79%.
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 Granite-4.0-1B. That is roughly Infinityx on output cost alone. o1-pro is the reasoning model in the pair, while Granite-4.0-1B 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. o1-pro gives you the larger context window at 200K, compared with 128K for Granite-4.0-1B.
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-4.0-1B | o1-pro |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40% |
| BrowseComp | — | 50% |
| OSWorld-Verified | — | 32% |
| Coding | ||
| HumanEval | 73% | — |
| SWE-bench Pro | — | 23% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 48% |
| OfficeQA Pro | — | 49% |
| Reasoning | ||
| BBH | 59.7% | — |
| LongBench v2 | — | 54% |
| MRCRv2 | — | 59% |
| Knowledgeo1-pro wins | ||
| MMLU | 59.7% | — |
| GPQA | 29.7% | 79% |
| MMLU-Pro | 32.9% | — |
| FrontierScience | — | 63% |
| Instruction Following | ||
| IFEval | 78.5% | — |
| Multilingualo1-pro wins | ||
| MGSM | 27.5% | — |
| MMLU-ProX | — | 52% |
| Mathematics | ||
| AIME 2024 | — | 86% |
o1-pro is ahead overall, 45 to 40. The biggest single separator in this matchup is GPQA, where the scores are 29.7% and 79%.
o1-pro has the edge for knowledge tasks in this comparison, averaging 69.4 versus 31.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o1-pro has the edge for multilingual tasks in this comparison, averaging 52 versus 27.5. Granite-4.0-1B stays close enough that the answer can still flip depending on your workload.
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