Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
Mellum2-12B-A2.5B-Instruct
27
Verified leaderboard positions: GLM-5.2 #9 · Mellum2-12B-A2.5B-Instruct unranked
Pick GLM-5.2 if you want the stronger benchmark profile. Mellum2-12B-A2.5B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+24.9 difference
Knowledge
+26.3 difference
GLM-5.2
Mellum2-12B-A2.5B-Instruct
$1.4 / $4.4
N/A
N/A
N/A
N/A
N/A
1M
128K
Pick GLM-5.2 if you want the stronger benchmark profile. Mellum2-12B-A2.5B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.2's sharpest advantage is in knowledge, where it averages 67.2 against 40.9. The single biggest benchmark swing on the page is GPQA, 91.2% to 40.9%.
GLM-5.2 is the reasoning model in the pair, while Mellum2-12B-A2.5B-Instruct 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-5.2 gives you the larger context window at 1M, compared with 128K for Mellum2-12B-A2.5B-Instruct.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 27. The biggest single separator in this matchup is GPQA, where the scores are 91.2% and 40.9%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 40.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 37.2. Mellum2-12B-A2.5B-Instruct stays close enough that the answer can still flip depending on your workload.
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