Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Qwen3.5 397B is clearly ahead on the aggregate, 71 to 33. 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 $0.00 input / $0.00 output per 1M tokens for Qwen3.5 397B. That is roughly Infinityx on output cost alone. o1-pro is the reasoning model in the pair, while Qwen3.5 397B 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 Qwen3.5 397B.
Pick Qwen3.5 397B 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.
Qwen3.5 397B
67.7
o1-pro
79
Qwen3.5 397B
81.9
o1-pro
86
Qwen3.5 397B is ahead overall, 71 to 33. The biggest single separator in this matchup is GPQA, where the scores are 82 and 79.
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 67.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o1-pro has the edge for math in this comparison, averaging 86 versus 81.9. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
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