Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 40. 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 47.1. The single biggest benchmark swing on the page is MuSR, 82 to 44.
Mercury 2 is the reasoning model in the pair, while Llama 4 Behemoth 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. Mercury 2 gives you the larger context window at 128K, compared with 32K for Llama 4 Behemoth.
Pick Mercury 2 if you want the stronger benchmark profile. Llama 4 Behemoth 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 4 Behemoth
34.6
Mercury 2
41.1
Llama 4 Behemoth
14.1
Mercury 2
68.3
Llama 4 Behemoth
55.1
Mercury 2
80.1
Llama 4 Behemoth
47.1
Mercury 2
57.2
Llama 4 Behemoth
36.7
Mercury 2
84
Llama 4 Behemoth
68
Mercury 2
79.7
Llama 4 Behemoth
62.8
Mercury 2
80.9
Llama 4 Behemoth
52.9
Mercury 2 is ahead overall, 65 to 40. The biggest single separator in this matchup is MuSR, where the scores are 82 and 44.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 36.7. 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 14.1. 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 52.9. 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 47.1. 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 34.6. 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 55.1. Inside this category, OfficeQA 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 68. 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 62.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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