Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1
64
Winner · 2/8 categoriesLFM2.5-350M
~39
0/8 categoriesGPT-4.1· LFM2.5-350M
Pick GPT-4.1 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 is clearly ahead on the aggregate, 64 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1's sharpest advantage is in knowledge, where it averages 63.1 against 23.8. The single biggest benchmark swing on the page is GPQA, 66.3% to 30.6%.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 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 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 | LFM2.5-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 61% | — |
| BrowseComp | 73% | — |
| OSWorld-Verified | 63% | — |
| Coding | ||
| SWE-bench Verified | 54.6% | — |
| SWE-bench Pro | 51% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 70% | — |
| OfficeQA Pro | 78% | — |
| Reasoning | ||
| LongBench v2 | 80% | — |
| MRCRv2 | 82% | — |
| KnowledgeGPT-4.1 wins | ||
| MMLU | 90.2% | — |
| GPQA | 66.3% | 30.6% |
| FrontierScience | 61% | — |
| MMLU-Pro | — | 20.0% |
| Instruction FollowingGPT-4.1 wins | ||
| IFEval | 87.4% | 77.0% |
| Multilingual | ||
| MMLU-ProX | 69% | — |
| Mathematics | ||
| AIME 2024 | 26.4% | — |
GPT-4.1 is ahead overall, 64 to 39. The biggest single separator in this matchup is GPQA, where the scores are 66.3% and 30.6%.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 63.1 versus 23.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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