Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro Base
43
o3-mini
56
Pick o3-mini if you want the stronger benchmark profile. DeepSeek V4 Pro 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
+13.8 difference
DeepSeek V4 Pro Base
o3-mini
$null / $null
$1.1 / $4.4
N/A
160 t/s
N/A
7.12s
1M
200K
Pick o3-mini if you want the stronger benchmark profile. DeepSeek V4 Pro 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.
o3-mini is clearly ahead on the provisional aggregate, 56 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini's sharpest advantage is in knowledge, where it averages 77.2 against 63.4.
o3-mini 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 200K for o3-mini.
o3-mini is ahead on BenchLM's provisional leaderboard, 56 to 43.
o3-mini has the edge for knowledge tasks in this comparison, averaging 77.2 versus 63.4. Inside this category, MMLU is the benchmark that creates the most daylight between them.
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