Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 is clearly ahead on the aggregate, 65 to 33. 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 coding, where it averages 51.7 against 8.2. The single biggest benchmark swing on the page is MMLU, 90.2 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 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 LFM2.5-1.2B-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 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 gives you the larger context window at 1M, compared with 32K for LFM2.5-1.2B-Thinking.
Pick GPT-4.1 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
64.7
LFM2.5-1.2B-Thinking
34.1
GPT-4.1
51.7
LFM2.5-1.2B-Thinking
8.2
GPT-4.1
73.6
LFM2.5-1.2B-Thinking
32.4
GPT-4.1
80.9
LFM2.5-1.2B-Thinking
38.4
GPT-4.1
63.3
LFM2.5-1.2B-Thinking
27
GPT-4.1
87.4
LFM2.5-1.2B-Thinking
72
GPT-4.1
69
LFM2.5-1.2B-Thinking
60.7
GPT-4.1
26.4
LFM2.5-1.2B-Thinking
42.3
GPT-4.1 is ahead overall, 65 to 33. The biggest single separator in this matchup is MMLU, where the scores are 90.2 and 27.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 63.3 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 51.7 versus 8.2. Inside this category, SWE-bench Verified 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 26.4. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for reasoning in this comparison, averaging 80.9 versus 38.4. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for agentic tasks in this comparison, averaging 64.7 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for multimodal and grounded tasks in this comparison, averaging 73.6 versus 32.4. Inside this category, MMMU-Pro 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 72. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for multilingual tasks in this comparison, averaging 69 versus 60.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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