Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
57
Mellum2-12B-A2.5B-Thinking
59
Pick Mellum2-12B-A2.5B-Thinking if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+9.0 difference
DeepSeek V3.2
Mellum2-12B-A2.5B-Thinking
$0.28 / $0.42
N/A
35 t/s
N/A
3.75s
N/A
128K
128K
Pick Mellum2-12B-A2.5B-Thinking if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Mellum2-12B-A2.5B-Thinking has the cleaner provisional overall profile here, landing at 59 versus 57. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Mellum2-12B-A2.5B-Thinking's sharpest advantage is in coding, where it averages 69.9 against 60.9.
Mellum2-12B-A2.5B-Thinking is the reasoning model in the pair, while DeepSeek V3.2 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.
Mellum2-12B-A2.5B-Thinking is ahead on BenchLM's provisional leaderboard, 59 to 57.
Mellum2-12B-A2.5B-Thinking has the edge for coding in this comparison, averaging 69.9 versus 60.9. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
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