Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 and Seed 1.6 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.
Seed 1.6's sharpest advantage is in agentic, where it averages 62.3 against 62.3. The single biggest benchmark swing on the page is HumanEval, 80 to 64. GLM-5 does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Seed 1.6 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. Seed 1.6 gives you the larger context window at 256K, compared with 200K for GLM-5.
Treat this as a split decision. GLM-5 makes more sense if mathematics is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Seed 1.6 is the better fit if multimodal & grounded is the priority or you need the larger 256K context window.
GLM-5
62.3
Seed 1.6
62.3
GLM-5
41.1
Seed 1.6
42.4
GLM-5
69.2
Seed 1.6
79.6
GLM-5
79.4
Seed 1.6
74.5
GLM-5
62.1
Seed 1.6
56.4
GLM-5
85
Seed 1.6
87
GLM-5
82.1
Seed 1.6
83.4
GLM-5
84.8
Seed 1.6
75.9
GLM-5 and Seed 1.6 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 56.4. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for coding in this comparison, averaging 42.4 versus 41.1. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-5 has the edge for math in this comparison, averaging 84.8 versus 75.9. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-5 has the edge for reasoning in this comparison, averaging 79.4 versus 74.5. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GLM-5 and Seed 1.6 are effectively tied for agentic tasks here, both landing at 62.3 on average.
Seed 1.6 has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 69.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for instruction following in this comparison, averaging 87 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for multilingual tasks in this comparison, averaging 83.4 versus 82.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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