Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro Base
41
Mellum2-12B-A2.5B-Thinking
59
Pick Mellum2-12B-A2.5B-Thinking if you want the stronger benchmark profile. DeepSeek V4 Pro Base only becomes the better choice if knowledge is the priority or you need the larger 1M context window.
Knowledge
+5.8 difference
DeepSeek V4 Pro Base
Mellum2-12B-A2.5B-Thinking
$null / $null
N/A
N/A
N/A
N/A
N/A
1M
128K
Pick Mellum2-12B-A2.5B-Thinking if you want the stronger benchmark profile. DeepSeek V4 Pro Base only becomes the better choice if knowledge is the priority or you need the larger 1M context window.
Mellum2-12B-A2.5B-Thinking is clearly ahead on the provisional aggregate, 59 to 41. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mellum2-12B-A2.5B-Thinking is the reasoning model in the pair, while DeepSeek V4 Pro Base 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. DeepSeek V4 Pro Base gives you the larger context window at 1M, compared with 128K for Mellum2-12B-A2.5B-Thinking.
Mellum2-12B-A2.5B-Thinking is ahead on BenchLM's provisional leaderboard, 59 to 41.
DeepSeek V4 Pro Base has the edge for knowledge tasks in this comparison, averaging 63.4 versus 57.6. Inside this category, MMLU-Redux is the benchmark that creates the most daylight between them.
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