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 mathematics, where it averages 80.9 against 51.3. The single biggest benchmark swing on the page is MuSR, 82 to 42.
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 Llama 4 Maverick. That is roughly Infinityx on output cost alone. Mercury 2 is the reasoning model in the pair, while Llama 4 Maverick 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. Llama 4 Maverick gives you the larger context window at 1M, compared with 128K for Mercury 2.
Pick Mercury 2 if you want the stronger benchmark profile. Llama 4 Maverick only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Mercury 2
63.7
Llama 4 Maverick
40.9
Mercury 2
41.1
Llama 4 Maverick
15.7
Mercury 2
68.3
Llama 4 Maverick
56.8
Mercury 2
80.1
Llama 4 Maverick
54
Mercury 2
57.2
Llama 4 Maverick
36.5
Mercury 2
84
Llama 4 Maverick
68
Mercury 2
79.7
Llama 4 Maverick
59.8
Mercury 2
80.9
Llama 4 Maverick
51.3
Mercury 2 is ahead overall, 65 to 43. The biggest single separator in this matchup is MuSR, where the scores are 82 and 42.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 36.5. 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 15.7. 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 51.3. 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 54. 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 40.9. 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 56.8. 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 59.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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