Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
36
o1-pro
29
Pick DeepSeek V3 if you want the stronger benchmark profile. o1-pro only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
Knowledge
+9.0 difference
DeepSeek V3
o1-pro
$0.27 / $1.1
$150 / $600
N/A
N/A
N/A
N/A
128K
200K
Pick DeepSeek V3 if you want the stronger benchmark profile. o1-pro only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
DeepSeek V3 is clearly ahead on the provisional aggregate, 36 to 29. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-pro is also the more expensive model on tokens at $150.00 input / $600.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. That is roughly 545.5x on output cost alone. o1-pro is the reasoning model in the pair, while DeepSeek V3 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. o1-pro gives you the larger context window at 200K, compared with 128K for DeepSeek V3.
DeepSeek V3 is ahead on BenchLM's provisional leaderboard, 36 to 29. The biggest single separator in this matchup is GPQA, where the scores are 59.1% and 79%.
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 70. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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