Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o3-mini is clearly ahead on the aggregate, 56 to 34. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini's sharpest advantage is in knowledge, where it averages 82.1 against 28.2. The single biggest benchmark swing on the page is MMLU, 86.9 to 31.
o3-mini 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. o3-mini gives you the larger context window at 200K, compared with 128K for GPT-OSS 20B.
Pick o3-mini 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.
o3-mini
82.1
GPT-OSS 20B
28.2
o3-mini
49.3
GPT-OSS 20B
16
o3-mini
87.3
GPT-OSS 20B
33.6
o3-mini
93.9
GPT-OSS 20B
67
o3-mini is ahead overall, 56 to 34. The biggest single separator in this matchup is MMLU, where the scores are 86.9 and 31.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 28.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o3-mini has the edge for coding in this comparison, averaging 49.3 versus 16. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o3-mini has the edge for math in this comparison, averaging 87.3 versus 33.6. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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