Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
68
LFM2.5-230M
17
Pick GLM-4.7 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
+40.3 difference
GLM-4.7
LFM2.5-230M
$0 / $0
$0 / $0
82 t/s
N/A
1.10s
N/A
200K
32K
Pick GLM-4.7 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.
GLM-4.7 is clearly ahead on the provisional aggregate, 68 to 17. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in knowledge, where it averages 60.6 against 20.3. The single biggest benchmark swing on the page is MMLU-Pro, 84.3% to 20.3%.
GLM-4.7 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. GLM-4.7 gives you the larger context window at 200K, compared with 32K for LFM2.5-230M.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 68 to 17. The biggest single separator in this matchup is MMLU-Pro, where the scores are 84.3% and 20.3%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 20.3. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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