Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 has the cleaner overall profile here, landing at 65 versus 62. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Mercury 2's sharpest advantage is in reasoning, where it averages 80.1 against 73.6. The single biggest benchmark swing on the page is MuSR, 82 to 75. DeepSeek LLM 2.0 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Mercury 2 is the reasoning model in the pair, while DeepSeek LLM 2.0 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. DeepSeek LLM 2.0 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
DeepSeek LLM 2.0
57.9
Mercury 2
41.1
DeepSeek LLM 2.0
42.9
Mercury 2
68.3
DeepSeek LLM 2.0
64.5
Mercury 2
80.1
DeepSeek LLM 2.0
73.6
Mercury 2
57.2
DeepSeek LLM 2.0
57.5
Mercury 2
84
DeepSeek LLM 2.0
85
Mercury 2
79.7
DeepSeek LLM 2.0
78.8
Mercury 2
80.9
DeepSeek LLM 2.0
80.8
Mercury 2 is ahead overall, 65 to 62. The biggest single separator in this matchup is MuSR, where the scores are 82 and 75.
DeepSeek LLM 2.0 has the edge for knowledge tasks in this comparison, averaging 57.5 versus 57.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for coding in this comparison, averaging 42.9 versus 41.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for math in this comparison, averaging 80.9 versus 80.8. 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 73.6. 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 57.9. 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 64.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for instruction following in this comparison, averaging 85 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 78.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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