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.
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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.
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.