Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral Large 3 finishes one point ahead overall, 61 to 60. 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.
Mistral Large 3's sharpest advantage is in coding, where it averages 41 against 35. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 52 to 60. Ministral 3 14B (Reasoning) does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Mistral Large 3 is also the more expensive model on tokens at $2.00 input / $6.00 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 Mistral Large 3 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 Mistral Large 3 if you want the stronger benchmark profile. Ministral 3 14B (Reasoning) only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Mistral Large 3
52.5
Ministral 3 14B (Reasoning)
58.5
Mistral Large 3
41
Ministral 3 14B (Reasoning)
35
Mistral Large 3
75.5
Ministral 3 14B (Reasoning)
71.5
Mistral Large 3
70.6
Ministral 3 14B (Reasoning)
69.2
Mistral Large 3
57.1
Ministral 3 14B (Reasoning)
52.1
Mistral Large 3
83
Ministral 3 14B (Reasoning)
81
Mistral Large 3
78.8
Ministral 3 14B (Reasoning)
77.8
Mistral Large 3
77.3
Ministral 3 14B (Reasoning)
75.2
Mistral Large 3 is ahead overall, 61 to 60. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 52 and 60.
Mistral Large 3 has the edge for knowledge tasks in this comparison, averaging 57.1 versus 52.1. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for coding in this comparison, averaging 41 versus 35. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for math in this comparison, averaging 77.3 versus 75.2. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for reasoning in this comparison, averaging 70.6 versus 69.2. Inside this category, SimpleQA 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 52.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for multimodal and grounded tasks in this comparison, averaging 75.5 versus 71.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for instruction following in this comparison, averaging 83 versus 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for multilingual tasks in this comparison, averaging 78.8 versus 77.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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