Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GLM-4.7 is clearly ahead on the aggregate, 74 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7 is the reasoning model in the pair, while GPT-4o mini 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 GPT-4o mini.
Pick GLM-4.7 if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7
70.3
GPT-4o mini
82
GLM-4.7
54.7
GPT-4o mini
87.2
GLM-4.7
81
GPT-4o mini
87
GLM-4.7 is ahead overall, 74 to 43. The biggest single separator in this matchup is HumanEval, where the scores are 78 and 87.2.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 70.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for coding in this comparison, averaging 87.2 versus 54.7. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for multilingual tasks in this comparison, averaging 87 versus 81. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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