Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o1 is clearly ahead on the aggregate, 51 to 33. 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 83.8 against 27. The single biggest benchmark swing on the page is MMLU, 91.8 to 29.
o1 is the reasoning model in the pair, while Mistral 8x7B v0.2 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 32K for Mistral 8x7B v0.2.
Pick o1 if you want the stronger benchmark profile. Mistral 8x7B v0.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
o1
83.8
Mistral 8x7B v0.2
27
o1
41
Mistral 8x7B v0.2
16.3
o1
74.3
Mistral 8x7B v0.2
31.9
o1
92.2
Mistral 8x7B v0.2
67
o1 is ahead overall, 51 to 33. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 29.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o1 has the edge for coding in this comparison, averaging 41 versus 16.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o1 has the edge for math in this comparison, averaging 74.3 versus 31.9. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 67. Inside this category, IFEval 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.