Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Llama 3 70B and o1 finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
o1 is the reasoning model in the pair, while Llama 3 70B 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 Llama 3 70B.
Treat this as a split decision. Llama 3 70B makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; o1 is the better fit if knowledge is the priority or you need the larger 200K context window.
Llama 3 70B
47.2
o1
83.8
Llama 3 70B
26
o1
41
Llama 3 70B
58.8
o1
74.3
Llama 3 70B
77
o1
92.2
Llama 3 70B and o1 are tied on overall score, so the right pick depends on which category matters most for your use case.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 47.2. 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 26. 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 58.8. 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 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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