Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 nano
44
Winner · 3/8 categoriesGranite-4.0-H-350M
~24
0/8 categoriesGPT-4.1 nano· Granite-4.0-H-350M
Pick GPT-4.1 nano if you want the stronger benchmark profile. Granite-4.0-H-350M only becomes the better choice if you want the cheaper token bill.
GPT-4.1 nano is clearly ahead on the aggregate, 44 to 24. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 nano's sharpest advantage is in multilingual, where it averages 59 against 14.7. The single biggest benchmark swing on the page is MMLU, 80.1% to 35.0%.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 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. GPT-4.1 nano gives you the larger context window at 1M, 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 | GPT-4.1 nano | Granite-4.0-H-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 43% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 42% | — |
| Coding | ||
| SWE-bench Pro | 18% | — |
| HumanEval | — | 39% |
| Multimodal & Grounded | ||
| MMMU-Pro | 53% | — |
| OfficeQA Pro | 67% | — |
| Reasoning | ||
| LongBench v2 | 75% | — |
| MRCRv2 | 73% | — |
| BBH | — | 33.1% |
| KnowledgeGPT-4.1 nano wins | ||
| MMLU | 80.1% | 35.0% |
| GPQA | 50.3% | 24.1% |
| FrontierScience | 51% | — |
| MMLU-Pro | — | 12.1% |
| Instruction FollowingGPT-4.1 nano wins | ||
| IFEval | 83.2% | 55.4% |
| MultilingualGPT-4.1 nano wins | ||
| MMLU-ProX | 59% | — |
| MGSM | — | 14.7% |
| Mathematics | ||
| AIME 2024 | 9.8% | — |
GPT-4.1 nano is ahead overall, 44 to 24. The biggest single separator in this matchup is MMLU, where the scores are 80.1% and 35.0%.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 16.4. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 55.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for multilingual tasks in this comparison, averaging 59 versus 14.7. Granite-4.0-H-350M stays close enough that the answer can still flip depending on your workload.
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.