Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 42. 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. The single biggest benchmark swing on the page is MuSR, 82 to 43.
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 Scout. That is roughly Infinityx on output cost alone. Mercury 2 is the reasoning model in the pair, while Llama 4 Scout 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 Scout gives you the larger context window at 10M, compared with 128K for Mercury 2.
Pick Mercury 2 if you want the stronger benchmark profile. Llama 4 Scout only becomes the better choice if you want the cheaper token bill or you need the larger 10M context window.
Mercury 2
63.7
Llama 4 Scout
40.6
Mercury 2
41.1
Llama 4 Scout
12.9
Mercury 2
68.3
Llama 4 Scout
57.8
Mercury 2
80.1
Llama 4 Scout
55
Mercury 2
57.2
Llama 4 Scout
35.6
Mercury 2
84
Llama 4 Scout
68
Mercury 2
79.7
Llama 4 Scout
59.8
Mercury 2
80.9
Llama 4 Scout
51
Mercury 2 is ahead overall, 65 to 42. The biggest single separator in this matchup is MuSR, where the scores are 82 and 43.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 35.6. 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 12.9. 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. 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 55. 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 40.6. 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 57.8. 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.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.