We do care about cost, of course. If money didn't matter, everyone would get infinite rate limits, 10M context windows, and free subscriptions. So if we make new models more efficient without nerfing them, that's great. And that's generally what's happened over the past few years. If you look at GPT-4 (from 2023), it was far less efficient than today's models, which meant it had slower latency, lower rate limits, and tiny context windows (I think it might have been like 4K originally, which sounds insanely low now). Today, GPT-5 Thinking is way more efficient than GPT-4 was, but it's also way more useful and way more reliable. So we're big fans of efficiency as long as it doesn't nerf the utility of the models. The more efficient the models are, the more we can crank up speeds and rate limits and context windows.
That said, there are definitely cases where we intentionally trade off intelligence for greater efficiency. For example, we never made GPT-4.5 the default model in ChatGPT, even though it was an awesome model at writing and other tasks, because it was quite costly to serve and the juice wasn't worth the squeeze for the average person (no one wants to get rate limited after 10 messages). A second example: in our API, we intentionally serve dumber mini and nano models for developers who prioritize speed and cost. A third example: we recently reduced the default thinking times in ChatGPT to speed up the times that people were having to wait for answers, which in a sense is a bit of a nerf, though this decision was purely about listening to feedback to make ChatGPT better and had nothing to do with cost (and for the people who want longer thinking times, they can still manually select Extended/Heavy).
I'm not going to comment on the specific techniques used to make GPT-5 so much more efficient than GPT-4, but I will say that we don't do any gimmicks like nerfing by time of day or nerfing after launch. And when we do make newer models more efficient than older models, it mostly gets returned to people in the form of better speeds, rate limits, context windows, and new features.
It was available in the API from Feb 2025 to July 2025, I believe. There's probably another world where we could have kept it around longer, but there's a surprising amount of fixed cost in maintaining / optimizing / serving models, so we made the call to focus our resources on accelerating the next gen instead. A bit of a bummer, as it had some unique qualities.
That said, there are definitely cases where we intentionally trade off intelligence for greater efficiency. For example, we never made GPT-4.5 the default model in ChatGPT, even though it was an awesome model at writing and other tasks, because it was quite costly to serve and the juice wasn't worth the squeeze for the average person (no one wants to get rate limited after 10 messages). A second example: in our API, we intentionally serve dumber mini and nano models for developers who prioritize speed and cost. A third example: we recently reduced the default thinking times in ChatGPT to speed up the times that people were having to wait for answers, which in a sense is a bit of a nerf, though this decision was purely about listening to feedback to make ChatGPT better and had nothing to do with cost (and for the people who want longer thinking times, they can still manually select Extended/Heavy).
I'm not going to comment on the specific techniques used to make GPT-5 so much more efficient than GPT-4, but I will say that we don't do any gimmicks like nerfing by time of day or nerfing after launch. And when we do make newer models more efficient than older models, it mostly gets returned to people in the form of better speeds, rate limits, context windows, and new features.