Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
66
LFM2.5-230M
17
Verified leaderboard positions: GLM-5 #25 · LFM2.5-230M unranked
Pick GLM-5 if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill.
Knowledge
+50.4 difference
Inst. Following
+20.9 difference
GLM-5
LFM2.5-230M
$1 / $3.2
$0 / $0
74 t/s
N/A
1.64s
N/A
200K
32K
Pick GLM-5 if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill.
GLM-5 is clearly ahead on the provisional aggregate, 66 to 17. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in knowledge, where it averages 70.7 against 20.3. The single biggest benchmark swing on the page is MMLU-Pro, 85.7% to 20.3%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 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 gives you the larger context window at 200K, compared with 32K for LFM2.5-230M.
GLM-5 is ahead on BenchLM's provisional leaderboard, 66 to 17. The biggest single separator in this matchup is MMLU-Pro, where the scores are 85.7% and 20.3%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 20.3. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 71.7. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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