Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o finishes one point ahead overall, 56 to 55. 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.
GPT-4o's sharpest advantage is in agentic, where it averages 51.2 against 48.4. The single biggest benchmark swing on the page is SWE-bench Verified, 20 to 37. Ministral 3 14B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 14B. That is roughly Infinityx on output cost alone.
Pick GPT-4o if you want the stronger benchmark profile. Ministral 3 14B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-4o
51.2
Ministral 3 14B
48.4
GPT-4o
32.2
Ministral 3 14B
33
GPT-4o
72.2
Ministral 3 14B
70.5
GPT-4o
64.6
Ministral 3 14B
63.6
GPT-4o
47.4
Ministral 3 14B
50.1
GPT-4o
82
Ministral 3 14B
80
GPT-4o
75.5
Ministral 3 14B
76.8
GPT-4o
71.8
Ministral 3 14B
69.7
GPT-4o is ahead overall, 56 to 55. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 20 and 37.
Ministral 3 14B has the edge for knowledge tasks in this comparison, averaging 50.1 versus 47.4. Inside this category, HLE is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for coding in this comparison, averaging 33 versus 32.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-4o has the edge for math in this comparison, averaging 71.8 versus 69.7. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-4o has the edge for reasoning in this comparison, averaging 64.6 versus 63.6. Inside this category, BBH is the benchmark that creates the most daylight between them.
GPT-4o has the edge for agentic tasks in this comparison, averaging 51.2 versus 48.4. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4o has the edge for multimodal and grounded tasks in this comparison, averaging 72.2 versus 70.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-4o has the edge for instruction following in this comparison, averaging 82 versus 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for multilingual tasks in this comparison, averaging 76.8 versus 75.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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