Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 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.
Mercury 2's sharpest advantage is in coding, where it averages 41.1 against 41.1. The single biggest benchmark swing on the page is MMLU, 88 to 78. GLM-5 does hit back in knowledge, 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 GLM-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. GLM-5 gives you the larger context window at 200K, compared with 128K for Mercury 2.
Treat this as a split decision. GLM-5 makes more sense if knowledge is the priority or you need the larger 200K context window; Mercury 2 is the better fit if agentic is the priority or you want the stronger reasoning-first profile.
GLM-5
62.3
Mercury 2
63.7
GLM-5
41.1
Mercury 2
41.1
GLM-5
69.2
Mercury 2
68.3
GLM-5
79.4
Mercury 2
80.1
GLM-5
62.1
Mercury 2
57.2
GLM-5
85
Mercury 2
84
GLM-5
82.1
Mercury 2
79.7
GLM-5
84.8
Mercury 2
80.9
GLM-5 and Mercury 2 are tied on overall score, so the right pick depends on which category matters most for your use case.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 62.1 versus 57.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-5 and Mercury 2 are effectively tied for coding here, both landing at 41.1 on average.
GLM-5 has the edge for math in this comparison, averaging 84.8 versus 80.9. 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 79.4. Inside this category, BBH 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 62.3. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
GLM-5 has the edge for multimodal and grounded tasks in this comparison, averaging 69.2 versus 68.3. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GLM-5 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.
GLM-5 has the edge for multilingual tasks in this comparison, averaging 82.1 versus 79.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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