Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 84 to 23. 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 mathematics, where it averages 96 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 99 to 9.8.
GLM-5 (Reasoning) is the reasoning model in the pair, while GPT-4.1 nano 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-4.1 nano gives you the larger context window at 1M, compared with 200K for GLM-5 (Reasoning).
Pick GLM-5 (Reasoning) if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5 (Reasoning)
80.3
GPT-4.1 nano
65.2
GLM-5 (Reasoning)
96
GPT-4.1 nano
9.8
GLM-5 (Reasoning)
92
GPT-4.1 nano
83.2
GLM-5 (Reasoning) is ahead overall, 84 to 23. The biggest single separator in this matchup is AIME 2024, where the scores are 99 and 9.8.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 80.3 versus 65.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for math in this comparison, averaging 96 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for instruction following in this comparison, averaging 92 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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