Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
82
Mellum2-12B-A2.5B-Instruct
27
Verified leaderboard positions: GLM-5.1 #28 · Mellum2-12B-A2.5B-Instruct unranked
Pick GLM-5.1 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
+23.7 difference
Knowledge
+11.4 difference
GLM-5.1
Mellum2-12B-A2.5B-Instruct
$1.4 / $4.4
N/A
N/A
N/A
N/A
N/A
203K
128K
Pick GLM-5.1 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-5.1 is clearly ahead on the provisional aggregate, 82 to 27. 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 coding, where it averages 60.9 against 37.2.
GLM-5.1 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-5.1 gives you the larger context window at 203K, compared with 128K for Mellum2-12B-A2.5B-Instruct.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 82 to 27.
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 40.9. Inside this category, GPQA-D is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for coding in this comparison, averaging 60.9 versus 37.2. Mellum2-12B-A2.5B-Instruct 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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