Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 mini
56
Winner · 3/8 categoriesGranite-4.0-350M
~27
0/8 categoriesGPT-4.1 mini· Granite-4.0-350M
Pick GPT-4.1 mini if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if you want the cheaper token bill.
GPT-4.1 mini is clearly ahead on the aggregate, 56 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 mini's sharpest advantage is in multilingual, where it averages 72 against 16.2. The single biggest benchmark swing on the page is MMLU, 87.5% to 36.2%.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Granite-4.0-350M. That is roughly Infinityx on output cost alone. GPT-4.1 mini gives you the larger context window at 1M, compared with 32K for Granite-4.0-350M.
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 | GPT-4.1 mini | Granite-4.0-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 54% | — |
| BrowseComp | 71% | — |
| OSWorld-Verified | 49% | — |
| Coding | ||
| SWE-bench Verified | 23.6% | — |
| SWE-bench Pro | 30% | — |
| HumanEval | — | 38% |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 74% | — |
| Reasoning | ||
| LongBench v2 | 80% | — |
| MRCRv2 | 82% | — |
| BBH | — | 33.3% |
| KnowledgeGPT-4.1 mini wins | ||
| MMLU | 87.5% | 36.2% |
| GPQA | 64.2% | 26.1% |
| FrontierScience | 61% | — |
| MMLU-Pro | — | 14.4% |
| Instruction FollowingGPT-4.1 mini wins | ||
| IFEval | 88.5% | 61.6% |
| MultilingualGPT-4.1 mini wins | ||
| MMLU-ProX | 72% | — |
| MGSM | — | 16.2% |
| Mathematics | ||
| AIME 2024 | 23.1% | — |
GPT-4.1 mini is ahead overall, 56 to 27. The biggest single separator in this matchup is MMLU, where the scores are 87.5% and 36.2%.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 62.3 versus 18.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for instruction following in this comparison, averaging 88.5 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for multilingual tasks in this comparison, averaging 72 versus 16.2. Granite-4.0-350M stays close enough that the answer can still flip depending on your workload.
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