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 38. 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 46.6. The single biggest benchmark swing on the page is AIME 2024, 9.8 to 48. LFM2-24B-A2B 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 $0.03 input / $0.12 output per 1M tokens for LFM2-24B-A2B. That is roughly 3.3x on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 32K for LFM2-24B-A2B.
Pick GPT-4.1 nano if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
GPT-4.1 nano
47.4
LFM2-24B-A2B
33.4
GPT-4.1 nano
18
LFM2-24B-A2B
18
GPT-4.1 nano
59.3
LFM2-24B-A2B
41.7
GPT-4.1 nano
74.1
LFM2-24B-A2B
46.6
GPT-4.1 nano
50.7
LFM2-24B-A2B
35.6
GPT-4.1 nano
83.2
LFM2-24B-A2B
68
GPT-4.1 nano
59
LFM2-24B-A2B
61.4
GPT-4.1 nano
9.8
LFM2-24B-A2B
50.4
GPT-4.1 nano is ahead overall, 49 to 38. The biggest single separator in this matchup is AIME 2024, where the scores are 9.8 and 48.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 35.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 nano and LFM2-24B-A2B are effectively tied for coding here, both landing at 18 on average.
LFM2-24B-A2B has the edge for math in this comparison, averaging 50.4 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 46.6. Inside this category, MRCRv2 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 33.4. 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 41.7. 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 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 59. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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