Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o1 is clearly ahead on the aggregate, 51 to 34. 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 28.2. The single biggest benchmark swing on the page is MMLU, 91.8 to 31.
o1 is the reasoning model in the pair, while GPT-OSS 20B 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 GPT-OSS 20B.
Pick o1 if you want the stronger benchmark profile. GPT-OSS 20B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
o1
83.8
GPT-OSS 20B
28.2
o1
41
GPT-OSS 20B
16
o1
74.3
GPT-OSS 20B
33.6
o1
92.2
GPT-OSS 20B
67
o1 is ahead overall, 51 to 34. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 31.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 28.2. 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. 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 33.6. 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.
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