Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o1 is clearly ahead on the aggregate, 51 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1's sharpest advantage is in knowledge, where it averages 83.8 against 32.2. The single biggest benchmark swing on the page is MMLU, 91.8 to 37.
o1 is the reasoning model in the pair, while GLM-4.5 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. o1 gives you the larger context window at 200K, compared with 128K for GLM-4.5.
Pick o1 if you want the stronger benchmark profile. GLM-4.5 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
o1
83.8
GLM-4.5
32.2
o1
41
GLM-4.5
20
o1
74.3
GLM-4.5
38.6
o1
92.2
GLM-4.5
68
o1 is ahead overall, 51 to 38. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 37.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 32.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o1 has the edge for coding in this comparison, averaging 41 versus 20. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o1 has the edge for math in this comparison, averaging 74.3 versus 38.6. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.