Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o1 finishes one point ahead overall, 51 to 50. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
o1's sharpest advantage is in knowledge, where it averages 83.8 against 43.8. The single biggest benchmark swing on the page is MMLU, 91.8 to 51.
o1 is the reasoning model in the pair, while GPT-OSS 120B 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 120B.
Pick o1 if you want the stronger benchmark profile. GPT-OSS 120B 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 120B
43.8
o1
41
GPT-OSS 120B
32.3
o1
74.3
GPT-OSS 120B
52.6
o1
92.2
GPT-OSS 120B
79
o1 is ahead overall, 51 to 50. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 51.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 43.8. 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 32.3. 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 52.6. 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 79. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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