Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GLM-4.7-Flash is clearly ahead on the aggregate, 64 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7-Flash's sharpest advantage is in mathematics, where it averages 67.5 against 26.4. The single biggest benchmark swing on the page is AIME 2024, 68 to 26.4. GPT-4.1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GLM-4.7-Flash is the reasoning model in the pair, while GPT-4.1 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 gives you the larger context window at 1M, compared with 200K for GLM-4.7-Flash.
Pick GLM-4.7-Flash if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if knowledge is the priority or you need the larger 1M context window.
GLM-4.7-Flash
57.3
GPT-4.1
78.3
GLM-4.7-Flash
47.7
GPT-4.1
54.6
GLM-4.7-Flash
67.5
GPT-4.1
26.4
GLM-4.7-Flash
84
GPT-4.1
87.4
GLM-4.7-Flash is ahead overall, 64 to 43. The biggest single separator in this matchup is AIME 2024, where the scores are 68 and 26.4.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 78.3 versus 57.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 54.6 versus 47.7. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for math in this comparison, averaging 67.5 versus 26.4. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 84. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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