Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o mini
55
Winner · 2/8 categoriesGranite-4.0-H-1B
~43
0/8 categoriesGPT-4o mini· Granite-4.0-H-1B
Pick GPT-4o mini if you want the stronger benchmark profile. Granite-4.0-H-1B only becomes the better choice if you want the cheaper token bill.
GPT-4o mini is clearly ahead on the aggregate, 55 to 43. 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 37.8. The single biggest benchmark swing on the page is MGSM, 87% to 37.8%.
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-H-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-H-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 58% | — |
| BrowseComp | 49% | — |
| OSWorld-Verified | 44% | — |
| Coding | ||
| HumanEval | 87.2% | 74% |
| SWE-bench Pro | 65% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 53% | — |
| Reasoning | ||
| LongBench v2 | 49% | — |
| MRCRv2 | 50% | — |
| BBH | — | 60.4% |
| KnowledgeGPT-4o mini wins | ||
| MMLU | 82% | 59.4% |
| FrontierScience | 62% | — |
| GPQA | — | 29.9% |
| MMLU-Pro | — | 34.0% |
| Instruction Following | ||
| IFEval | — | 77.4% |
| MultilingualGPT-4o mini wins | ||
| MGSM | 87% | 37.8% |
| MMLU-ProX | 68% | — |
| Mathematics | ||
| Coming soon | ||
GPT-4o mini is ahead overall, 55 to 43. The biggest single separator in this matchup is MGSM, where the scores are 87% and 37.8%.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 62 versus 32.6. 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 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.