> For my use cases it doesn't appear to be any better than o3/o3-pro. It doesn't have to be better, it has to be "as close to those as possible", while being c

> For my use cases it doesn't appear to be any better than o3/o3-pro. It doesn't have to be better, it has to be "as close to those as possible", while being cost efficient to run and serve at scale. > This probably means that we're in the "slow AI / stagnation" timeline I'd say we're more in the "ok, we got the capabilities in huge models, now we need to make them smaller, faster and scalable" timeline. If they capture ~80-90% of the capabilities of their strongest models while reducing costs a lot (they've gone from 40-60$/Mtok to 10$/Mtok) then they're starting to approach a break-even point and slowly make money off of serving tokens. There's also a move towards having specialised models (code w/ claude, long context w/ gemini, etc) and oAI seem to have gone in this direction as well. They've said for a long time that gpt5 will be a "systems" update and not necessarily a core model update. That is, they have a routing model that takes a query and routes it towards the best model for the task. Once devs figure out how to use this to their advantage, the "vibes" will improve.