DeepSeek V3 vs o1

Pick o1 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

o1 is clearly ahead on the provisional aggregate, 57 to 36. 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 instruction following, where it averages 92.2 against 86.1. The single biggest benchmark swing on the page is GPQA, 59.1% to 75.7%.

o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. That is roughly 54.5x on output cost alone. o1 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 gives you the larger context window at 200K, compared with 128K for DeepSeek V3.