Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 43. 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 50.9. The single biggest benchmark swing on the page is MuSR, 82 to 40.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.25 input / $0.75 output per 1M tokens for Mercury 2. That is roughly 2.9x on output cost alone.
Pick Mercury 2 if you want the stronger benchmark profile. DeepSeek-R1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Mercury 2
63.7
DeepSeek-R1
44.5
Mercury 2
41.1
DeepSeek-R1
21.5
Mercury 2
68.3
DeepSeek-R1
47.5
Mercury 2
80.1
DeepSeek-R1
50.9
Mercury 2
57.2
DeepSeek-R1
37.9
Mercury 2
84
DeepSeek-R1
69
Mercury 2
79.7
DeepSeek-R1
60.4
Mercury 2
80.9
DeepSeek-R1
52.5
Mercury 2 is ahead overall, 65 to 43. The biggest single separator in this matchup is MuSR, where the scores are 82 and 40.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 37.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for coding in this comparison, averaging 41.1 versus 21.5. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for math in this comparison, averaging 80.9 versus 52.5. 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 50.9. 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 44.5. 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 47.5. 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 69. 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 60.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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