Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 58. 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 agentic, where it averages 63.7 against 51.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63 to 48. Aion-2.0 does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
Aion-2.0 is also the more expensive model on tokens at $0.80 input / $1.60 output per 1M tokens, versus $0.25 input / $0.75 output per 1M tokens for Mercury 2. That is roughly 2.1x on output cost alone. Mercury 2 is the reasoning model in the pair, while Aion-2.0 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. Aion-2.0 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
Aion-2.0
51.7
Mercury 2
41.1
Aion-2.0
33.2
Mercury 2
68.3
Aion-2.0
66
Mercury 2
80.1
Aion-2.0
70.3
Mercury 2
57.2
Aion-2.0
54
Mercury 2
84
Aion-2.0
93
Mercury 2
79.7
Aion-2.0
78.1
Mercury 2
80.9
Aion-2.0
72.1
Mercury 2 is ahead overall, 65 to 58. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63 and 48.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 54. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for coding in this comparison, averaging 41.1 versus 33.2. 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 72.1. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for reasoning in this comparison, averaging 80.1 versus 70.3. Inside this category, LongBench v2 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 51.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for multimodal and grounded tasks in this comparison, averaging 68.3 versus 66. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Aion-2.0 has the edge for instruction following in this comparison, averaging 93 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 78.1. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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