Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Ministral 3 14B (Reasoning) is clearly ahead on the aggregate, 60 to 52. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Ministral 3 14B (Reasoning)'s sharpest advantage is in reasoning, where it averages 69.2 against 49.4. The single biggest benchmark swing on the page is SWE-bench Pro, 36 to 65. GPT-4o mini does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-4o mini is also the more expensive model on tokens at $0.15 input / $0.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 14B (Reasoning). That is roughly Infinityx on output cost alone. Ministral 3 14B (Reasoning) is the reasoning model in the pair, while GPT-4o mini 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.
Pick Ministral 3 14B (Reasoning) if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Ministral 3 14B (Reasoning)
58.5
GPT-4o mini
50.9
Ministral 3 14B (Reasoning)
35
GPT-4o mini
65
Ministral 3 14B (Reasoning)
71.5
GPT-4o mini
60.2
Ministral 3 14B (Reasoning)
69.2
GPT-4o mini
49.4
Ministral 3 14B (Reasoning)
52.1
GPT-4o mini
62
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Ministral 3 14B (Reasoning)
77.8
GPT-4o mini
74.7
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Ministral 3 14B (Reasoning) is ahead overall, 60 to 52. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 36 and 65.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 62 versus 52.1. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for coding in this comparison, averaging 65 versus 35. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for reasoning in this comparison, averaging 69.2 versus 49.4. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for agentic tasks in this comparison, averaging 58.5 versus 50.9. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 71.5 versus 60.2. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for multilingual tasks in this comparison, averaging 77.8 versus 74.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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