Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 61. 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 55.3. The single biggest benchmark swing on the page is MuSR, 82 to 62. Nemotron 3 Super 120B A12B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Mercury 2 is also the more expensive model on tokens at $0.25 input / $0.75 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Nemotron 3 Super 120B A12B. That is roughly Infinityx on output cost alone. Mercury 2 is the reasoning model in the pair, while Nemotron 3 Super 120B A12B 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. Nemotron 3 Super 120B A12B gives you the larger context window at 256K, compared with 128K for Mercury 2.
Pick Mercury 2 if you want the stronger benchmark profile. Nemotron 3 Super 120B A12B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Mercury 2
63.7
Nemotron 3 Super 120B A12B
55.3
Mercury 2
41.1
Nemotron 3 Super 120B A12B
44.2
Mercury 2
68.3
Nemotron 3 Super 120B A12B
60.4
Mercury 2
80.1
Nemotron 3 Super 120B A12B
71.8
Mercury 2
57.2
Nemotron 3 Super 120B A12B
55.8
Mercury 2
84
Nemotron 3 Super 120B A12B
86
Mercury 2
79.7
Nemotron 3 Super 120B A12B
81.5
Mercury 2
80.9
Nemotron 3 Super 120B A12B
74.6
Mercury 2 is ahead overall, 65 to 61. The biggest single separator in this matchup is MuSR, where the scores are 82 and 62.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 55.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Nemotron 3 Super 120B A12B has the edge for coding in this comparison, averaging 44.2 versus 41.1. 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 74.6. 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 71.8. 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 55.3. Inside this category, OSWorld-Verified 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 60.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Nemotron 3 Super 120B A12B has the edge for instruction following in this comparison, averaging 86 versus 84. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Nemotron 3 Super 120B A12B has the edge for multilingual tasks in this comparison, averaging 81.5 versus 79.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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