Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 E2B
26
LFM2.5-230M
17
Pick Gemma 4 E2B if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+33.8 difference
Gemma 4 E2B
LFM2.5-230M
$0 / $0
$0 / $0
N/A
N/A
N/A
N/A
128K
32K
Pick Gemma 4 E2B if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 E2B 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.
Gemma 4 E2B's sharpest advantage is in knowledge, where it averages 54.1 against 20.3. The single biggest benchmark swing on the page is MMLU-Pro, 60% to 20.3%.
Gemma 4 E2B is the reasoning model in the pair, while LFM2.5-230M 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. Gemma 4 E2B gives you the larger context window at 128K, compared with 32K for LFM2.5-230M.
Gemma 4 E2B is ahead on BenchLM's provisional leaderboard, 26 to 17. The biggest single separator in this matchup is MMLU-Pro, where the scores are 60% and 20.3%.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 versus 20.3. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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