Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Llama 3.1 405B is clearly ahead on the aggregate, 65 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini is the reasoning model in the pair, while Llama 3.1 405B 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 Llama 3.1 405B.
Pick Llama 3.1 405B if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
Llama 3.1 405B
58.7
o3-mini
82.1
Llama 3.1 405B
48.3
o3-mini
49.3
Llama 3.1 405B
70.6
o3-mini
87.3
Llama 3.1 405B
86
o3-mini
93.9
Llama 3.1 405B is ahead overall, 65 to 56. The biggest single separator in this matchup is MMLU, where the scores are 70 and 86.9.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 58.7. 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 48.3. 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 70.6. 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 86. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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