Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 84 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5 (Reasoning)'s sharpest advantage is in multilingual, where it averages 89 against 87. The single biggest benchmark swing on the page is MMLU, 96 to 82. GPT-4o mini does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GLM-5 (Reasoning) 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-5 (Reasoning) gives you the larger context window at 200K, compared with 128K for GPT-4o mini.
Pick GLM-5 (Reasoning) 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-5 (Reasoning)
80.3
GPT-4o mini
82
GLM-5 (Reasoning)
69.3
GPT-4o mini
87.2
GLM-5 (Reasoning)
89
GPT-4o mini
87
GLM-5 (Reasoning) is ahead overall, 84 to 43. The biggest single separator in this matchup is MMLU, where the scores are 96 and 82.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 80.3. 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 69.3. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 89 versus 87. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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