I can't speak to other uses such as coding, but as a sounding board GPT-5 is better than GPT-4o, which was already pretty good. GPT-5's personality has definitely shifted to a more professional tone which I like.

I do understand why people are missing the more synchophant persoanlity of GPT-4o, but I'm not one of them.

I don’t feel like I got anything new, I feel like something got taken away.

Upon some digging, it seems that part of the slowdown is due to the gpt-5 models by default doing some reasoning (reasoning effort "medium"), even for the nano or mini model. Setting the reasoning effort to "minimal" improves the speed a lot.

However, to be able to set the reasoning effort you have to switch to the new Response API, which wasn't a lot of work, but more than just changing a URL.

Code seems to work on the first try more often for me too.

Perhaps my favorite change so far is the difference of verbosity. Some of the responses I am receiving when asking trivial questions are now are merely a handful of sentences instead of a dissertation. However, dissertation mode comes back when appropriate, which is also nice.

Edit: Slightly tangential, but I forgot to ask, do any of you all have access to the $200/month plan? If so, how does that model compare to GPT-5?

I’m reminded of that Louis CK joke about people being upset about the WiFi not working on their airplane.

ChatGPT 5 in web is a router to lots of different models [1], and the non-reasoning GPT-5 chat model ("gpt-5-chat-latest" in the API) is quite dumb - no significant difference from 4o/4.1. Even if you choose GPT-5 Thinking, there's a chance that your request will be routed to GPT-5 Mini, not to full GPT-5. The only real way to fix that in ChatGPT is to subscribe to Pro and use GPT-5 Pro, but of course that's very expensive. Otherwise people suggest saying "think hard" in the prompt, which might make the router choose the better model. Worse even, Sam Altman publicly said that in the first day of the GPT-5 release their router didn't work properly. [2]

I'd suggest trying GPT-5 in API or in apps like Cursor/Windsurf if you want to truly test it.

Oh, and from the GPT-5 guide [3] apparently OpenAI considers GPT-5 to be good even at "minimal" reasoning (it's still the full thinking model then, not the chat variant, and will respond much faster):

> The minimal setting performs especially well in coding and instruction following scenarios, adhering closely to given directions

[1] https://cdn.openai.com/pdf/8124a3ce-ab78-4f06-96eb-49ea29ffb... "Table 1: Model progressions"

[2] https://x.com/sama/status/1953893841381273969 "Yesterday, the autoswitcher broke and was out of commission for a chunk of the day, and the result was GPT-5 seemed way dumber"

[3] https://platform.openai.com/docs/guides/latest-model

I think the make everything bigger is plateauing and not yielding the infinite returns that were first suggested and demonstrated from gpt 2 - 4.

I think now it’s harder because they’ve got to focus on value per watt. Smaller good models mean less energy, less complexity to go wrong, but harder to achieve.

The unlock could be more techniques and focussed synthetic data from old models used to train new ones but apparently gpt-5 uses synthetic data and this is one of the reasons it isn’t necessarily good in real world tasks.

For me if we go the synthetic data route it’s important to shoot for quality - good synthetic data distils useful stuff, discards the noise so useful patterns are more solid in training, but imagine it’s hard to distinguish signal from noise to produce good synthetic data.