Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 nano is clearly ahead on the aggregate, 49 to 30. 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 reasoning, where it averages 74.1 against 32.1. The single biggest benchmark swing on the page is MMLU, 80.1 to 26. LFM2.5-1.2B-Instruct does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
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 LFM2.5-1.2B-Instruct. That is roughly Infinityx on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 32K for LFM2.5-1.2B-Instruct.
Pick GPT-4.1 nano if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
GPT-4.1 nano
47.4
LFM2.5-1.2B-Instruct
25.7
GPT-4.1 nano
18
LFM2.5-1.2B-Instruct
7.2
GPT-4.1 nano
59.3
LFM2.5-1.2B-Instruct
32.4
GPT-4.1 nano
74.1
LFM2.5-1.2B-Instruct
32.1
GPT-4.1 nano
50.7
LFM2.5-1.2B-Instruct
26
GPT-4.1 nano
83.2
LFM2.5-1.2B-Instruct
80
GPT-4.1 nano
59
LFM2.5-1.2B-Instruct
60.7
GPT-4.1 nano
9.8
LFM2.5-1.2B-Instruct
37
GPT-4.1 nano is ahead overall, 49 to 30. The biggest single separator in this matchup is MMLU, where the scores are 80.1 and 26.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for coding in this comparison, averaging 18 versus 7.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Instruct has the edge for math in this comparison, averaging 37 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for reasoning in this comparison, averaging 74.1 versus 32.1. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for agentic tasks in this comparison, averaging 47.4 versus 25.7. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for multimodal and grounded tasks in this comparison, averaging 59.3 versus 32.4. Inside this category, OfficeQA Pro 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 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Instruct has the edge for multilingual tasks in this comparison, averaging 60.7 versus 59. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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