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 62. 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 45.9. The single biggest benchmark swing on the page is SWE-bench Verified, 83 to 40.
GPT-5.2 Pro gives you the larger context window at 400K, compared with 200K for GLM-4.7-Flash.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. GLM-4.7-Flash only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GPT-5.2 Pro
85.9
GLM-4.7-Flash
61.3
GPT-5.2 Pro
84.8
GLM-4.7-Flash
45.9
GPT-5.2 Pro
96
GLM-4.7-Flash
62.5
GPT-5.2 Pro
95.2
GLM-4.7-Flash
69.7
GPT-5.2 Pro
81.5
GLM-4.7-Flash
54.1
GPT-5.2 Pro
95
GLM-4.7-Flash
84
GPT-5.2 Pro
93.4
GLM-4.7-Flash
81.8
GPT-5.2 Pro
98.2
GLM-4.7-Flash
74
GPT-5.2 Pro is ahead overall, 90 to 62. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 83 and 40.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 54.1. Inside this category, GPQA 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 45.9. Inside this category, SWE-bench Verified 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 74. Inside this category, HMMT Feb 2023 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 69.7. Inside this category, SimpleQA 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 61.3. Inside this category, BrowseComp 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 62.5. 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 84. 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 81.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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