Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
74
LFM2.5-230M
17
Verified leaderboard positions: GLM-5.1 #30 · LFM2.5-230M unranked
Pick GLM-5.1 if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+32.0 difference
GLM-5.1
LFM2.5-230M
$1.4 / $4.4
$0 / $0
N/A
N/A
N/A
N/A
203K
32K
Pick GLM-5.1 if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5.1 is clearly ahead on the provisional aggregate, 74 to 17. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in knowledge, where it averages 52.3 against 20.3.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 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. GLM-5.1 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-5.1 gives you the larger context window at 203K, compared with 32K for LFM2.5-230M.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 74 to 17.
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 20.3. LFM2.5-230M stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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