Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.2 Pro is clearly ahead on the aggregate, 90 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2 Pro's sharpest advantage is in coding, where it averages 84.8 against 41.1. The single biggest benchmark swing on the page is LiveCodeBench, 81 to 35.
GPT-5.2 Pro 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. GPT-5.2 Pro gives you the larger context window at 400K, compared with 200K for GLM-5.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. GLM-5 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.2 Pro
85.9
GLM-5
62.3
GPT-5.2 Pro
84.8
GLM-5
41.1
GPT-5.2 Pro
96
GLM-5
69.2
GPT-5.2 Pro
95.2
GLM-5
79.4
GPT-5.2 Pro
81.5
GLM-5
62.1
GPT-5.2 Pro
95
GLM-5
85
GPT-5.2 Pro
93.4
GLM-5
82.1
GPT-5.2 Pro
98.2
GLM-5
84.8
GPT-5.2 Pro is ahead overall, 90 to 65. The biggest single separator in this matchup is LiveCodeBench, where the scores are 81 and 35.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 62.1. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for coding in this comparison, averaging 84.8 versus 41.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for math in this comparison, averaging 98.2 versus 84.8. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for reasoning in this comparison, averaging 95.2 versus 79.4. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for agentic tasks in this comparison, averaging 85.9 versus 62.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 96 versus 69.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for instruction following in this comparison, averaging 95 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for multilingual tasks in this comparison, averaging 93.4 versus 82.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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