Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2.5 and Ministral 3 14B (Reasoning) finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
Kimi K2.5 is also the more expensive model on tokens at $0.50 input / $2.80 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 Kimi K2.5 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.
Treat this as a split decision. Kimi K2.5 makes more sense if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Ministral 3 14B (Reasoning) is the better fit if multimodal & grounded is the priority or you want the cheaper token bill.
Kimi K2.5
52.3
Ministral 3 14B (Reasoning)
58.5
Kimi K2.5
38.9
Ministral 3 14B (Reasoning)
35
Kimi K2.5
64.6
Ministral 3 14B (Reasoning)
71.5
Kimi K2.5
71.7
Ministral 3 14B (Reasoning)
69.2
Kimi K2.5
57.2
Ministral 3 14B (Reasoning)
52.1
Kimi K2.5
85
Ministral 3 14B (Reasoning)
81
Kimi K2.5
79.8
Ministral 3 14B (Reasoning)
77.8
Kimi K2.5
78.7
Ministral 3 14B (Reasoning)
75.2
Kimi K2.5 and Ministral 3 14B (Reasoning) are tied on overall score, so the right pick depends on which category matters most for your use case.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 52.1. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for coding in this comparison, averaging 38.9 versus 35. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for math in this comparison, averaging 78.7 versus 75.2. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 71.7 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 52.3. Inside this category, Terminal-Bench 2.0 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 64.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 85 versus 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for multilingual tasks in this comparison, averaging 79.8 versus 77.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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