Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 59. 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 68.3. The single biggest benchmark swing on the page is MuSR, 82 to 66. Llama 3.1 405B does hit back in instruction following, 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 Llama 3.1 405B 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. Llama 3.1 405B 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
Llama 3.1 405B
53.9
Mercury 2
41.1
Llama 3.1 405B
40.6
Mercury 2
68.3
Llama 3.1 405B
62.3
Mercury 2
80.1
Llama 3.1 405B
68.3
Mercury 2
57.2
Llama 3.1 405B
53.2
Mercury 2
84
Llama 3.1 405B
86
Mercury 2
79.7
Llama 3.1 405B
80.1
Mercury 2
80.9
Llama 3.1 405B
74.9
Mercury 2 is ahead overall, 65 to 59. The biggest single separator in this matchup is MuSR, where the scores are 82 and 66.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 53.2. 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 40.6. 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 74.9. 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 68.3. 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 53.9. 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 62.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Llama 3.1 405B 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.
Llama 3.1 405B has the edge for multilingual tasks in this comparison, averaging 80.1 versus 79.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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