Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7 is clearly ahead on the aggregate, 67 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in agentic, where it averages 66.1 against 55.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 67 to 52. Seed-2.0-Lite does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
GLM-4.7 is the reasoning model in the pair, while Seed-2.0-Lite 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-2.0-Lite gives you the larger context window at 256K, compared with 200K for GLM-4.7.
Pick GLM-4.7 if you want the stronger benchmark profile. Seed-2.0-Lite only becomes the better choice if multimodal & grounded is the priority or you need the larger 256K context window.
GLM-4.7
66.1
Seed-2.0-Lite
55.1
GLM-4.7
46.6
Seed-2.0-Lite
41.4
GLM-4.7
70.5
Seed-2.0-Lite
79.6
GLM-4.7
80.2
Seed-2.0-Lite
73
GLM-4.7
61.8
Seed-2.0-Lite
53.9
GLM-4.7
85
Seed-2.0-Lite
89
GLM-4.7
79.1
Seed-2.0-Lite
82.5
GLM-4.7
85
Seed-2.0-Lite
75
GLM-4.7 is ahead overall, 67 to 63. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 67 and 52.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 61.8 versus 53.9. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 46.6 versus 41.4. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for math in this comparison, averaging 85 versus 75. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for reasoning in this comparison, averaging 80.2 versus 73. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for agentic tasks in this comparison, averaging 66.1 versus 55.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Seed-2.0-Lite has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 70.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Seed-2.0-Lite has the edge for instruction following in this comparison, averaging 89 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Seed-2.0-Lite has the edge for multilingual tasks in this comparison, averaging 82.5 versus 79.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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