Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
69
LFM2.5-VL-450M
0
Pick GLM-4.7 if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+39.0 difference
GLM-4.7
LFM2.5-VL-450M
$0 / $0
$0 / $0
82 t/s
N/A
1.10s
N/A
200K
128K
Pick GLM-4.7 if you want the stronger benchmark profile. LFM2.5-VL-450M 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, 69 to 0. 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 21.6. The single biggest benchmark swing on the page is MMLU-Pro, 84.3% to 19.3%.
GLM-4.7 is the reasoning model in the pair, while LFM2.5-VL-450M 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 128K for LFM2.5-VL-450M.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 69 to 0. The biggest single separator in this matchup is MMLU-Pro, where the scores are 84.3% and 19.3%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 21.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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