Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 36. 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 reasoning, where it averages 80.1 against 43.8. The single biggest benchmark swing on the page is MuSR, 82 to 33.
Mercury 2 is the reasoning model in the pair, while GLM-4.5 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. GLM-4.5 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
GLM-4.5
31.3
Mercury 2
41.1
GLM-4.5
14.4
Mercury 2
68.3
GLM-4.5
41
Mercury 2
80.1
GLM-4.5
43.8
Mercury 2
57.2
GLM-4.5
32
Mercury 2
84
GLM-4.5
68
Mercury 2
79.7
GLM-4.5
58.1
Mercury 2
80.9
GLM-4.5
45.5
Mercury 2 is ahead overall, 65 to 36. The biggest single separator in this matchup is MuSR, where the scores are 82 and 33.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 32. 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 14.4. 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 45.5. 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 43.8. 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 31.3. 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 41. 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 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 58.1. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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