Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1 is clearly ahead on the aggregate, 68 to 27. 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 reasoning, where it averages 78.1 against 30.1. The single biggest benchmark swing on the page is MMLU, 91.8 to 24.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 3B. That is roughly Infinityx on output cost alone. o1 is the reasoning model in the pair, while Ministral 3 3B 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 Ministral 3 3B.
Pick o1 if you want the stronger benchmark profile. Ministral 3 3B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
o1
65.4
Ministral 3 3B
22.9
o1
48.4
Ministral 3 3B
6.2
o1
70.7
Ministral 3 3B
30.4
o1
78.1
Ministral 3 3B
30.1
o1
69.6
Ministral 3 3B
24.5
o1
92.2
Ministral 3 3B
67
o1
77
Ministral 3 3B
59.7
o1
74.3
Ministral 3 3B
36
o1 is ahead overall, 68 to 27. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 24.
o1 has the edge for knowledge tasks in this comparison, averaging 69.6 versus 24.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o1 has the edge for coding in this comparison, averaging 48.4 versus 6.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
o1 has the edge for math in this comparison, averaging 74.3 versus 36. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o1 has the edge for reasoning in this comparison, averaging 78.1 versus 30.1. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
o1 has the edge for agentic tasks in this comparison, averaging 65.4 versus 22.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
o1 has the edge for multimodal and grounded tasks in this comparison, averaging 70.7 versus 30.4. Inside this category, MMMU-Pro 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.
o1 has the edge for multilingual tasks in this comparison, averaging 77 versus 59.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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