Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-350M
~24
0/8 categorieso1
63
Winner · 3/8 categoriesGranite-4.0-H-350M· o1
Pick o1 if you want the stronger benchmark profile. Granite-4.0-H-350M 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 is clearly ahead on the aggregate, 63 to 24. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1's sharpest advantage is in multilingual, where it averages 77 against 14.7. The single biggest benchmark swing on the page is MMLU, 35.0% to 91.8%.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Granite-4.0-H-350M. That is roughly Infinityx on output cost alone. o1 is the reasoning model in the pair, while Granite-4.0-H-350M 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 gives you the larger context window at 200K, compared with 32K for Granite-4.0-H-350M.
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-350M | o1 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 66% |
| BrowseComp | — | 72% |
| OSWorld-Verified | — | 60% |
| Coding | ||
| HumanEval | 39% | — |
| SWE-bench Verified | — | 41% |
| SWE-bench Pro | — | 50% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 68% |
| OfficeQA Pro | — | 74% |
| Reasoning | ||
| BBH | 33.1% | — |
| LongBench v2 | — | 79% |
| MRCRv2 | — | 77% |
| Knowledgeo1 wins | ||
| MMLU | 35.0% | 91.8% |
| GPQA | 24.1% | 75.7% |
| MMLU-Pro | 12.1% | — |
| FrontierScience | — | 65% |
| Instruction Followingo1 wins | ||
| IFEval | 55.4% | 92.2% |
| Multilingualo1 wins | ||
| MGSM | 14.7% | — |
| MMLU-ProX | — | 77% |
| Mathematics | ||
| AIME 2024 | — | 74.3% |
o1 is ahead overall, 63 to 24. The biggest single separator in this matchup is MMLU, where the scores are 35.0% and 91.8%.
o1 has the edge for knowledge tasks in this comparison, averaging 69.3 versus 16.4. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 55.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
o1 has the edge for multilingual tasks in this comparison, averaging 77 versus 14.7. Granite-4.0-H-350M stays close enough that the answer can still flip depending on your workload.
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