Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 45. 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 coding, where it averages 41.1 against 15.8. The single biggest benchmark swing on the page is SWE-bench Verified, 46 to 9.
Mercury 2 is the reasoning model in the pair, while Llama 3 70B 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 70B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
Llama 3 70B
41.2
Mercury 2
41.1
Llama 3 70B
15.8
Mercury 2
68.3
Llama 3 70B
52.3
Mercury 2
80.1
Llama 3 70B
59.6
Mercury 2
57.2
Llama 3 70B
42.2
Mercury 2
84
Llama 3 70B
77
Mercury 2
79.7
Llama 3 70B
67.5
Mercury 2
80.9
Llama 3 70B
63.3
Mercury 2 is ahead overall, 65 to 45. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 46 and 9.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 42.2. 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 15.8. 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 63.3. 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 59.6. 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 41.2. 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 52.3. 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 77. 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 67.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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