Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Ministral 3 14B is clearly ahead on the aggregate, 55 to 47. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Ministral 3 14B's sharpest advantage is in coding, where it averages 33 against 17.2. The single biggest benchmark swing on the page is SWE-bench Verified, 37 to 5.
Pick Ministral 3 14B if you want the stronger benchmark profile. GPT-4 Turbo only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Ministral 3 14B
48.4
GPT-4 Turbo
44.7
Ministral 3 14B
33
GPT-4 Turbo
17.2
Ministral 3 14B
70.5
GPT-4 Turbo
55.3
Ministral 3 14B
63.6
GPT-4 Turbo
61
Ministral 3 14B
50.1
GPT-4 Turbo
41.1
Ministral 3 14B
80
GPT-4 Turbo
80
Ministral 3 14B
76.8
GPT-4 Turbo
68.5
Ministral 3 14B
69.7
GPT-4 Turbo
64.4
Ministral 3 14B is ahead overall, 55 to 47. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 37 and 5.
Ministral 3 14B has the edge for knowledge tasks in this comparison, averaging 50.1 versus 41.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for coding in this comparison, averaging 33 versus 17.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for math in this comparison, averaging 69.7 versus 64.4. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for reasoning in this comparison, averaging 63.6 versus 61. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for agentic tasks in this comparison, averaging 48.4 versus 44.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for multimodal and grounded tasks in this comparison, averaging 70.5 versus 55.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Ministral 3 14B and GPT-4 Turbo are effectively tied for instruction following here, both landing at 80 on average.
Ministral 3 14B has the edge for multilingual tasks in this comparison, averaging 76.8 versus 68.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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