Minimax has been great for super high speed web/js/ts related work. It compares in my experience to Claude Sonnet, and at times gets stuff similar to Opus.
Design wise it produces some of the most beautiful AI generated page I've seen.
GLM-4.7 like a mix of Sonnet 4.5 and GPT-5 (the first version not the later ones). It has deep deep knowledge, but it's often just not as good in execution.
They're very cheap to try out, so you should see how your mileage varies.
Ofcourse for the hardest possible tasks that GPT 5.2 only approaches, they're not up to scratch. And for the hard-ish tasks in C++ for example that Opus 4.5 tackles Minimax feels closer, but just doesn't "grok" the problem space good enough.
To prove you right, you can read up on the incredible giga-brained countrywide experiments by Kardelj in Socialist Yugoslavia [0]. The result being a country where no-one wanted to work, and everyone had a great standard of living (while the IMF didn't call in its loans). And then the entire country collapsed all at once under the accumulated mismanagement.
I personally was affected by this fire, although I've always kept 3 month backups of production data, encrypted, on-site, just in case of emergencies like this. Haven't touched their services for anything production related ever since
It's missing a lot of crucial details. Nothing on the dataset used, nothing on the data mix, nothing on their data cleaning procedures, nothing on the tokens trained.
Not all of them per se, take a look at something like Mistral. It's a 7B model displaying incredible performance. IMO, we still haven't even scratched the surface of what is possible with small LLMs. Especially not with pre-filtered/classified pre-training data. (Interesting LLMs based on their data approach and relatively small size: Qwen, InternLM, Mistral, Phi)