Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Gemini 3.1 Flash-Lite is clearly ahead on the aggregate, 55 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 $0.10 input / $0.40 output per 1M tokens for Gemini 3.1 Flash-Lite. That is roughly 1500.0x on output cost alone. o1-pro is the reasoning model in the pair, while Gemini 3.1 Flash-Lite 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. Gemini 3.1 Flash-Lite gives you the larger context window at 1M, compared with 200K for o1-pro.
Pick Gemini 3.1 Flash-Lite if you want the stronger benchmark profile. o1-pro only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Gemini 3.1 Flash-Lite
51.2
o1-pro
79
Gemini 3.1 Flash-Lite
63.1
o1-pro
86
Gemini 3.1 Flash-Lite is ahead overall, 55 to 33. The biggest single separator in this matchup is AIME 2024, where the scores are 65 and 86.
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 51.2. 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 63.1. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.