Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 47. 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 17.2. The single biggest benchmark swing on the page is SWE-bench Verified, 46 to 5.
Mercury 2 is the reasoning model in the pair, while GPT-4 Turbo 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. GPT-4 Turbo only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
GPT-4 Turbo
44.7
Mercury 2
41.1
GPT-4 Turbo
17.2
Mercury 2
68.3
GPT-4 Turbo
55.3
Mercury 2
80.1
GPT-4 Turbo
61
Mercury 2
57.2
GPT-4 Turbo
41.1
Mercury 2
84
GPT-4 Turbo
80
Mercury 2
79.7
GPT-4 Turbo
68.5
Mercury 2
80.9
GPT-4 Turbo
64.4
Mercury 2 is ahead overall, 65 to 47. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 46 and 5.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 41.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for coding in this comparison, averaging 41.1 versus 17.2. 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 64.4. 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 61. 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 44.7. 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 55.3. 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 80. 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 68.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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