Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 and Mercury 2 finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.25 input / $0.75 output per 1M tokens for Mercury 2. That is roughly 10.7x on output cost alone. Mercury 2 is the reasoning model in the pair, while GPT-4.1 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. GPT-4.1 gives you the larger context window at 1M, compared with 128K for Mercury 2.
Treat this as a split decision. GPT-4.1 makes more sense if coding is the priority or you need the larger 1M context window; Mercury 2 is the better fit if mathematics is the priority or you want the cheaper token bill.
GPT-4.1
64.7
Mercury 2
63.7
GPT-4.1
51.7
Mercury 2
41.1
GPT-4.1
73.6
Mercury 2
68.3
GPT-4.1
80.9
Mercury 2
80.1
GPT-4.1
63.3
Mercury 2
57.2
GPT-4.1
87.4
Mercury 2
84
GPT-4.1
69
Mercury 2
79.7
GPT-4.1
26.4
Mercury 2
80.9
GPT-4.1 and Mercury 2 are tied on overall score, so the right pick depends on which category matters most for your use case.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 63.3 versus 57.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 51.7 versus 41.1. 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 26.4. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for reasoning in this comparison, averaging 80.9 versus 80.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for agentic tasks in this comparison, averaging 64.7 versus 63.7. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for multimodal and grounded tasks in this comparison, averaging 73.6 versus 68.3. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 84. 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 69. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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