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 33. 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 38.4. The single biggest benchmark swing on the page is MMLU, 80.1 to 27. LFM2.5-1.2B-Thinking 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-Thinking. That is roughly Infinityx on output cost alone. LFM2.5-1.2B-Thinking is the reasoning model in the pair, while GPT-4.1 nano is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-4.1 nano gives you the larger context window at 1M, compared with 32K for LFM2.5-1.2B-Thinking.
Pick GPT-4.1 nano if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking 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-Thinking
34.1
GPT-4.1 nano
18
LFM2.5-1.2B-Thinking
8.2
GPT-4.1 nano
59.3
LFM2.5-1.2B-Thinking
32.4
GPT-4.1 nano
74.1
LFM2.5-1.2B-Thinking
38.4
GPT-4.1 nano
50.7
LFM2.5-1.2B-Thinking
27
GPT-4.1 nano
83.2
LFM2.5-1.2B-Thinking
72
GPT-4.1 nano
59
LFM2.5-1.2B-Thinking
60.7
GPT-4.1 nano
9.8
LFM2.5-1.2B-Thinking
42.3
GPT-4.1 nano is ahead overall, 49 to 33. The biggest single separator in this matchup is MMLU, where the scores are 80.1 and 27.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 27. 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 8.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for math in this comparison, averaging 42.3 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 38.4. 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 34.1. 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 72. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking 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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