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 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-Flash gives you the larger context window at 200K, compared with 128K for GPT-4o mini.
Pick GLM-4.7-Flash 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-Flash
57.3
GPT-4o mini
82
GLM-4.7-Flash
47.7
GPT-4o mini
87.2
GLM-4.7-Flash
85
GPT-4o mini
87
GLM-4.7-Flash is ahead overall, 64 to 43. The biggest single separator in this matchup is HumanEval, where the scores are 58 and 87.2.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 57.3. 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 47.7. 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 85. Inside this category, MGSM 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.