The article is solid. But there is a nuance what he skipped — quality vs price. Sure, GPT-5.6 Luna for pennies can do the same thing what Claude 4.5 Sonnet did for a dollar a year ago. Except Sonnet back then actually carried the codebase, while Luna... eh, not so much. And another thing, speed. You can make it cheaper as much as you want, but if a model thinks for half a minute you save cents but lose time.
The other day I saw a benchmark of coding models that fit in 8GB VRAM - for reference some version of Mistral was added, normally requiring 32GB, but moving along at 4-5tok/sec when partially offloaded to CPU.
Surprisingly, some of the small models would not only give worse results, but also took longer than Mistral, because they were thinking so much.
That is an important detail which I was previously overlooking.