Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 nano
26
LFM2.5-230M
17
Pick GPT-4.1 nano if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill.
Knowledge
+30.0 difference
Inst. Following
+11.5 difference
GPT-4.1 nano
LFM2.5-230M
$0.1 / $0.4
$0 / $0
181 t/s
N/A
0.63s
N/A
1M
32K
Pick GPT-4.1 nano if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill.
GPT-4.1 nano is clearly ahead on the provisional aggregate, 26 to 17. 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 knowledge, where it averages 50.3 against 20.3. The single biggest benchmark swing on the page is IFEval, 83.2% to 71.7%.
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 LFM2.5-230M. That is roughly Infinityx on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 32K for LFM2.5-230M.
GPT-4.1 nano is ahead on BenchLM's provisional leaderboard, 26 to 17. The biggest single separator in this matchup is IFEval, where the scores are 83.2% and 71.7%.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.3 versus 20.3. LFM2.5-230M stays close enough that the answer can still flip depending on your workload.
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 71.7. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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