Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash Base
29
Mellum2-12B-A2.5B-Thinking
59
Pick Mellum2-12B-A2.5B-Thinking if you want the stronger benchmark profile. DeepSeek V4 Flash Base only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+5.4 difference
DeepSeek V4 Flash 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 Flash Base only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Mellum2-12B-A2.5B-Thinking is clearly ahead on the provisional aggregate, 59 to 29. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mellum2-12B-A2.5B-Thinking's sharpest advantage is in knowledge, where it averages 57.6 against 52.2.
Mellum2-12B-A2.5B-Thinking is the reasoning model in the pair, while DeepSeek V4 Flash 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 Flash 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 29.
Mellum2-12B-A2.5B-Thinking has the edge for knowledge tasks in this comparison, averaging 57.6 versus 52.2. Inside this category, MMLU-Redux is the benchmark that creates the most daylight between them.
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