Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash Base
31
o1
58
Pick o1 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
+23.5 difference
DeepSeek V4 Flash Base
o1
$null / $null
$15 / $60
N/A
98 t/s
N/A
32.29s
1M
200K
Pick o1 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.
o1 is clearly ahead on the provisional aggregate, 58 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1's sharpest advantage is in knowledge, where it averages 75.7 against 52.2.
o1 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 200K for o1.
o1 is ahead on BenchLM's provisional leaderboard, 58 to 31.
o1 has the edge for knowledge tasks in this comparison, averaging 75.7 versus 52.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
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