Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1 has the cleaner overall profile here, landing at 68 versus 65. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
o1's sharpest advantage is in knowledge, where it averages 69.6 against 57.2. The single biggest benchmark swing on the page is MMLU, 91.8 to 78. Mercury 2 does hit back in mathematics, 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 $0.25 input / $0.75 output per 1M tokens for Mercury 2. That is roughly 80.0x on output cost alone. o1 gives you the larger context window at 200K, compared with 128K for Mercury 2.
Pick o1 if you want the stronger benchmark profile. Mercury 2 only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
o1
65.4
Mercury 2
63.7
o1
48.4
Mercury 2
41.1
o1
70.7
Mercury 2
68.3
o1
78.1
Mercury 2
80.1
o1
69.6
Mercury 2
57.2
o1
92.2
Mercury 2
84
o1
77
Mercury 2
79.7
o1
74.3
Mercury 2
80.9
o1 is ahead overall, 68 to 65. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 78.
o1 has the edge for knowledge tasks in this comparison, averaging 69.6 versus 57.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o1 has the edge for coding in this comparison, averaging 48.4 versus 41.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for math in this comparison, averaging 80.9 versus 74.3. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for reasoning in this comparison, averaging 80.1 versus 78.1. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
o1 has the edge for agentic tasks in this comparison, averaging 65.4 versus 63.7. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
o1 has the edge for multimodal and grounded tasks in this comparison, averaging 70.7 versus 68.3. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 84. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for multilingual tasks in this comparison, averaging 79.7 versus 77. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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