Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 nano
27
LFM2.5-8B-A1B
50
Pick LFM2.5-8B-A1B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if instruction following is the priority or you need the larger 1M context window.
Inst. Following
+3.7 difference
GPT-4.1 nano
LFM2.5-8B-A1B
$0.1 / $0.4
$0 / $0
181 t/s
N/A
0.63s
N/A
1M
128K
Pick LFM2.5-8B-A1B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if instruction following is the priority or you need the larger 1M context window.
LFM2.5-8B-A1B is clearly ahead on the provisional aggregate, 50 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
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 LFM2.5-8B-A1B. That is roughly Infinityx on output cost alone. LFM2.5-8B-A1B 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 128K for LFM2.5-8B-A1B.
LFM2.5-8B-A1B is ahead on BenchLM's provisional leaderboard, 50 to 27. The biggest single separator in this matchup is IFEval, where the scores are 83.2% and 91.8%.
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 79.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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