Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Claude Haiku 4.5 is clearly ahead on the aggregate, 64 to 51. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Haiku 4.5's sharpest advantage is in coding, where it averages 48 against 41. The single biggest benchmark swing on the page is MMLU, 68 to 91.8. o1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.80 input / $4.00 output per 1M tokens for Claude Haiku 4.5. That is roughly 15.0x on output cost alone. o1 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.
Pick Claude Haiku 4.5 if you want the stronger benchmark profile. o1 only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Claude Haiku 4.5
57.8
o1
83.8
Claude Haiku 4.5
48
o1
41
Claude Haiku 4.5
68.8
o1
74.3
Claude Haiku 4.5
86
o1
92.2
Claude Haiku 4.5 is ahead overall, 64 to 51. The biggest single separator in this matchup is MMLU, where the scores are 68 and 91.8.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 57.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for coding in this comparison, averaging 48 versus 41. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o1 has the edge for math in this comparison, averaging 74.3 versus 68.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 86. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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