Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mercury 2's sharpest advantage is in reasoning, where it averages 80.1 against 64.6. The single biggest benchmark swing on the page is SWE-bench Verified, 46 to 20. GPT-4o does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.25 input / $0.75 output per 1M tokens for Mercury 2. That is roughly 13.3x on output cost alone. Mercury 2 is the reasoning model in the pair, while GPT-4o 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 Mercury 2 if you want the stronger benchmark profile. GPT-4o only becomes the better choice if multimodal & grounded is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
GPT-4o
51.2
Mercury 2
41.1
GPT-4o
32.2
Mercury 2
68.3
GPT-4o
72.2
Mercury 2
80.1
GPT-4o
64.6
Mercury 2
57.2
GPT-4o
47.4
Mercury 2
84
GPT-4o
82
Mercury 2
79.7
GPT-4o
75.5
Mercury 2
80.9
GPT-4o
71.8
Mercury 2 is ahead overall, 65 to 56. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 46 and 20.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 47.4. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for coding in this comparison, averaging 41.1 versus 32.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for math in this comparison, averaging 80.9 versus 71.8. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for reasoning in this comparison, averaging 80.1 versus 64.6. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for agentic tasks in this comparison, averaging 63.7 versus 51.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-4o has the edge for multimodal and grounded tasks in this comparison, averaging 72.2 versus 68.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for instruction following in this comparison, averaging 84 versus 82. 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 75.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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