Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude Haiku 4.5 has the cleaner overall profile here, landing at 62 versus 60. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Claude Haiku 4.5's sharpest advantage is in multimodal & grounded, where it averages 78.4 against 71.5. The single biggest benchmark swing on the page is MMMU-Pro, 82 to 71. Ministral 3 14B (Reasoning) does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Claude Haiku 4.5 is also the more expensive model on tokens at $0.80 input / $4.00 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 Claude Haiku 4.5 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. Claude Haiku 4.5 gives you the larger context window at 200K, compared with 128K for Ministral 3 14B (Reasoning).
Pick Claude Haiku 4.5 if you want the stronger benchmark profile. Ministral 3 14B (Reasoning) only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Claude Haiku 4.5
56.7
Ministral 3 14B (Reasoning)
58.5
Claude Haiku 4.5
41.7
Ministral 3 14B (Reasoning)
35
Claude Haiku 4.5
78.4
Ministral 3 14B (Reasoning)
71.5
Claude Haiku 4.5
68.9
Ministral 3 14B (Reasoning)
69.2
Claude Haiku 4.5
53.6
Ministral 3 14B (Reasoning)
52.1
Claude Haiku 4.5
86
Ministral 3 14B (Reasoning)
81
Claude Haiku 4.5
80.1
Ministral 3 14B (Reasoning)
77.8
Claude Haiku 4.5
73.3
Ministral 3 14B (Reasoning)
75.2
Claude Haiku 4.5 is ahead overall, 62 to 60. The biggest single separator in this matchup is MMMU-Pro, where the scores are 82 and 71.
Claude Haiku 4.5 has the edge for knowledge tasks in this comparison, averaging 53.6 versus 52.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for coding in this comparison, averaging 41.7 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 math in this comparison, averaging 75.2 versus 73.3. Inside this category, AIME 2023 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 68.9. Inside this category, MuSR 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 56.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for multimodal and grounded tasks in this comparison, averaging 78.4 versus 71.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for instruction following in this comparison, averaging 86 versus 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for multilingual tasks in this comparison, averaging 80.1 versus 77.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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