Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 finishes one point ahead overall, 65 to 64. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Mercury 2's sharpest advantage is in agentic, where it averages 63.7 against 57.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63 to 56. Qwen2.5-72B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Mercury 2 is the reasoning model in the pair, while Qwen2.5-72B 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. Qwen2.5-72B only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
Qwen2.5-72B
57.7
Mercury 2
41.1
Qwen2.5-72B
43.8
Mercury 2
68.3
Qwen2.5-72B
66.7
Mercury 2
80.1
Qwen2.5-72B
75.8
Mercury 2
57.2
Qwen2.5-72B
59.8
Mercury 2
84
Qwen2.5-72B
85
Mercury 2
79.7
Qwen2.5-72B
80.8
Mercury 2
80.9
Qwen2.5-72B
83.5
Mercury 2 is ahead overall, 65 to 64. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63 and 56.
Qwen2.5-72B has the edge for knowledge tasks in this comparison, averaging 59.8 versus 57.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Qwen2.5-72B has the edge for coding in this comparison, averaging 43.8 versus 41.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Qwen2.5-72B has the edge for math in this comparison, averaging 83.5 versus 80.9. 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 75.8. Inside this category, BBH 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 57.7. 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 66.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen2.5-72B has the edge for instruction following in this comparison, averaging 85 versus 84. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen2.5-72B has the edge for multilingual tasks in this comparison, averaging 80.8 versus 79.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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