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 58. 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 51.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 67 to 48. Aion-2.0 does hit back in instruction following, 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 Aion-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. GLM-4.7 gives you the larger context window at 200K, compared with 128K for Aion-2.0.
Pick GLM-4.7 if you want the stronger benchmark profile. Aion-2.0 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7
66.1
Aion-2.0
51.7
GLM-4.7
46.6
Aion-2.0
33.2
GLM-4.7
70.5
Aion-2.0
66
GLM-4.7
80.2
Aion-2.0
70.3
GLM-4.7
61.8
Aion-2.0
54
GLM-4.7
85
Aion-2.0
93
GLM-4.7
79.1
Aion-2.0
78.1
GLM-4.7
85
Aion-2.0
72.1
GLM-4.7 is ahead overall, 67 to 58. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 67 and 48.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 61.8 versus 54. Inside this category, HLE 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 33.2. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for math in this comparison, averaging 85 versus 72.1. Inside this category, MATH-500 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 70.3. Inside this category, LongBench v2 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 51.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for multimodal and grounded tasks in this comparison, averaging 70.5 versus 66. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Aion-2.0 has the edge for instruction following in this comparison, averaging 93 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for multilingual tasks in this comparison, averaging 79.1 versus 78.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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