Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 nano
44
Winner · 2/8 categoriesLFM2.5-350M
~39
0/8 categoriesGPT-4.1 nano· LFM2.5-350M
Pick GPT-4.1 nano if you want the stronger benchmark profile. LFM2.5-350M only becomes the better choice if you want the cheaper token bill.
GPT-4.1 nano is clearly ahead on the aggregate, 44 to 39. 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 knowledge, where it averages 50.7 against 23.8. The single biggest benchmark swing on the page is GPQA, 50.3% to 30.6%.
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 LFM2.5-350M. That is roughly Infinityx on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 32K for LFM2.5-350M.
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 | LFM2.5-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 43% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 42% | — |
| Coding | ||
| SWE-bench Pro | 18% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 53% | — |
| OfficeQA Pro | 67% | — |
| Reasoning | ||
| LongBench v2 | 75% | — |
| MRCRv2 | 73% | — |
| KnowledgeGPT-4.1 nano wins | ||
| MMLU | 80.1% | — |
| GPQA | 50.3% | 30.6% |
| FrontierScience | 51% | — |
| MMLU-Pro | — | 20.0% |
| Instruction FollowingGPT-4.1 nano wins | ||
| IFEval | 83.2% | 77.0% |
| Multilingual | ||
| MMLU-ProX | 59% | — |
| Mathematics | ||
| AIME 2024 | 9.8% | — |
GPT-4.1 nano is ahead overall, 44 to 39. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 30.6%.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 23.8. Inside this category, GPQA 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 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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