Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-5.4 Pro is clearly ahead on the aggregate, 94 to 74. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 Pro's sharpest advantage is in coding, where it averages 89 against 53.7. The single biggest benchmark swing on the page is LiveCodeBench, 86 to 35.
GPT-5.4 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-5.4 Pro gives you the larger context window at 1.05M, compared with 200K for GLM-5.
Pick GPT-5.4 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-5.4 Pro
88.8
GLM-5
71.2
GPT-5.4 Pro
89
GLM-5
53.7
GPT-5.4 Pro
98
GLM-5
86.4
GPT-5.4 Pro
96.7
GLM-5
83
GPT-5.4 Pro
97
GLM-5
85
GPT-5.4 Pro
97
GLM-5
84
GPT-5.4 Pro is ahead overall, 94 to 74. The biggest single separator in this matchup is LiveCodeBench, where the scores are 86 and 35.
GPT-5.4 Pro has the edge for knowledge tasks in this comparison, averaging 88.8 versus 71.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for coding in this comparison, averaging 89 versus 53.7. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for math in this comparison, averaging 98 versus 86.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for reasoning in this comparison, averaging 96.7 versus 83. Inside this category, BBH is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for instruction following in this comparison, averaging 97 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for multilingual tasks in this comparison, averaging 97 versus 84. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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