Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o mini
55
Winner · 2/8 categoriesGranite-4.0-1B
~40
0/8 categoriesGPT-4o mini· Granite-4.0-1B
Pick GPT-4o mini if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice if you want the cheaper token bill.
GPT-4o mini is clearly ahead on the aggregate, 55 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4o mini's sharpest advantage is in multilingual, where it averages 74.7 against 27.5. The single biggest benchmark swing on the page is MGSM, 87% to 27.5%.
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 Granite-4.0-1B. That is roughly Infinityx on output cost alone.
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 | GPT-4o mini | Granite-4.0-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 58% | — |
| BrowseComp | 49% | — |
| OSWorld-Verified | 44% | — |
| Coding | ||
| HumanEval | 87.2% | 73% |
| SWE-bench Pro | 65% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 53% | — |
| Reasoning | ||
| LongBench v2 | 49% | — |
| MRCRv2 | 50% | — |
| BBH | — | 59.7% |
| KnowledgeGPT-4o mini wins | ||
| MMLU | 82% | 59.7% |
| FrontierScience | 62% | — |
| GPQA | — | 29.7% |
| MMLU-Pro | — | 32.9% |
| Instruction Following | ||
| IFEval | — | 78.5% |
| MultilingualGPT-4o mini wins | ||
| MGSM | 87% | 27.5% |
| MMLU-ProX | 68% | — |
| Mathematics | ||
| Coming soon | ||
GPT-4o mini is ahead overall, 55 to 40. The biggest single separator in this matchup is MGSM, where the scores are 87% and 27.5%.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 62 versus 31.7. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for multilingual tasks in this comparison, averaging 74.7 versus 27.5. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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