Ivywen@Ivywen_W
A good product stabilizes. A failed replacement keeps needing another replacement.
Just look at the availability lifespan of ChatGPT models:
· GPT-4: 778 days
· GPT-4o: 641 days (about 690 days if Business access is included)
But after the GPT-5 series began:
· GPT-5 Instant / Thinking: 190 days
· GPT-5.1 Instant / Thinking: 119 days
· GPT-5.2 Instant: 82 days
· GPT-5.4 Thinking / Pro: 49 days
Since GPT-5, OpenAI’s model iteration has become faster and faster. A successor model barely has time to be tested by users before it is replaced, moved into legacy access, or retired.
This does not necessarily prove rapid technical progress.
It may suggest the opposite: that none of the successor models has been good enough to truly stabilize.
GPT-4o and GPT-4 series were validated by users and the market through long-term use. People built workflows and trust around them. They were mature products tested in real use.
But many post-4o models did not even have time to stabilize before the next version covered them.
Rapid iteration creates a kind of product accountability that can never be settled.
Every replacement escapes full judgment because another replacement quickly takes its place.
But users keep paying the cost.
They are forced to adapt again, recalibrate trust again, absorb the damage caused by immature models again.
A truly mature product should allow users to form stable expectations.
If users are always migrating, adapting, and rebuilding, that is not maturity. That is shifting the cost of testing onto users.
Just looking at this table, it is obvious which model was the best one, isn’t it?
#keep4o #OpenSource4o #ChatGPT
@OpenAI @sama
