r/ChatGPTPro

Anyone notice that as of today outputs from 5.4 Pro are much, much faster? like on the order of thinking times, if not shorter? Last time this happened months ago it seemed to indicate dininished performance, for several days, until it went back to thinking very hard about everything, even relatively simple follow up prompts. Now, on pro extended thinking, the same prompt that last week took 30 minutes takes 3 minutes today. Any thoughts?

I moved most of my workflow to Claude over the past few months and it’s been handling the bulk of my work really well. Writing, coding, prototyping and data analysis. Opus 4.7 has been the daily driver.

But I kept my ChatGPT Pro sub and I’m not planning to cancel. A few reasons:

- Cross-checking: when Claude gives me an answer that feels slightly off or when I’m about to act on something high-stakes, I’ll run it past ChatGPT to stress-test.

- Image generation: GPT’s image tools are just better for what I need.

- Backup when Claude is acting weird or busy with other tasks; Occasionally Claude has an off day (vague, over-hedged, or just not getting my intent). Having ChatGPT there as a fallback

Feels expensive to pay for two but I think the productivity delta is real. Curious how others are thinking about this: Are you running both? How do you split? Or have you fully committed to one and found the other unnecessary?

I am posting this because i wish someone else had posted something similar for me to find when i researched Plus vs Pro...

I am not a programmer, i am in a project where i need to write a very precise and well defined business text, and im not native English so ChatGPT is a good help to have.

But I just discovered that the chatGPT has, surprisingly, same small chat window context size as the Plus. I do text editing in the canvas and discuss different strategies with chatGPT who has a good overview of the whole text (10K words). It works well for about 3-4 hours and then it start to behave weird. Above 15K word or 20K tokens (ChatGPT Token Counter addon for Chrome), the chat decays rapidly, for it to be no useful anymore. Sure i can just start all over again, but it is time consuming and not optimal.

One good ting with Pro subscription, is that they allow for more text in the canvas, that is a big plus. But Pro thinking does not support canvas, but pro thinking is of no use for me as it takes forever to get a reply anyway. I use it for deep reviews of my text sometime though.

5.4 Thinking has a "Heavy" level above "Extended", which seems to be as fast as "Extended" so that is the one i'm using for the editing and everything else.

20 min reasoning time reduced to 3-4 min (GPT 5.4 pro extended thinking)

I have Pro since a couple of months and I have canceled many of the subscriptions to SaaS and I am to cancel everything within 3 months. I replaced CRM, Note Management, Mail Contacts and Calendar app. I just told to Chatgpt what I wanted and to include the best features form the best apps around and I got a fantastic result. It then prepared a series of documents for Codex and Codex delivered. Still a significant debugging time sometime because I did not express myself well enough on my needs but improving. I even provided screenshots from other apps of what I wanted and it reproduced them at perfection. I then extended the apps to my coworkers that have given back feedback and suggestions on new features, on things not working or to change. The real exciting thing is that every tool we used in the past had always something missing, after all we do things differently than others, but in this case we can customise the final apps to do exactly what we need. It even helped me find a VPS and upload everything there. The monthly costs are a fraction of what we paid before. I do not know how to code but I know mostly what I want from my apps.

Feels like everyone is focused on getting better models , But after working on real projects, I’m starting to think the bigger problem isn’t the model  it’s context  ,most setups today rely on 

* markdown files

* long prompts

* manually feeding information

And it breaks pretty quickly:

* context gets outdated

* too much irrelevant info

* hard to manage across features

Even strong models struggle when the context itself is messy, what seems more important now is dynamic context retrieval ,separating specs from general docs and only loading what’s relevant to the task

I’ve tried using structured workflows and tools like traycer to improve this, and they help especially when you can actually see how changes and context flow across a project  but it still feels like we’re layering solutions on top of a core problem.

Curious if others feel the same, or if model improvements are still the bigger bottleneck.

I got booted from ChatGPT on all my devices, and now I'm just getting hit with error messages whenever I try to log back into my account.

I’m pretty adept at GPT’s and LLM’s. I was an early adopter and have really pushed their use professionally and personally. I have started building my own custom GPT’s for work to automate things and I’m having some success. Apparently, I’m a top 5 user at my firm, which is a very large institution.

With that said, I’m not a coder. I work in risk management, most of what I am doing is task/process automation. I do believe this is the future. I think we could cut my team by 50%.

Any ideas on what kind of roles I could pursue or look for? Are there any firms that specialize it LLM implementation? I see a lot of prompt engineering roles but they require coding or AI research.

Couple months ago I had 0 problems sending GPT large data sets to analyze, no problems at all.

Now it crashes over and over and admits to me that it can’t do it anymore. Crazy.

I recently upgraded from Plus to Pro and am very impressed by the quality of the responses from the Pro model - I mostly use it to help with writing projects and my day job.

One thing I’m missing is that when it’s ‘reasoning’, it doesn’t show any ‘steps’ or live text as it’s doing so.. (the stream of consciousness that the Thinking model displays) and it doesn’t seem to show any ‘details’ in the Detail space.

I’ve asked it various things which it’s definitely had to go off and look things up for, but no stream of thought appears.. is this normal for everyone else(?)

Thanks v much all!

Title. The two possibilities is that they either allocated more resources and it has higher tokens/sec, or they nerfed it. I would be very disappointed if they did the latter because the whole point of the model is thinking deeply and trading speed for depth and thoroughness.

If they keep it nerfed and I notice a drop in quality, I will probably go back to Plus, since I don't use Codex. I just subbed to Pro for the model.

How can I smartly use ChatGPT as a founder for

Myself and the team? I’m a founder and pretty tech savvy but not finding the time to automate workflows. Inspire me without cheesy you tube videos that talk more than they show

Did anyone else notice that 5.4 Pro is taking a lot less time to think today?

I've been using ChatGPT (and Claude, and a few other tools) pretty much every workday for about a year and a half now. Mostly for knowledge work, research, drafting, analysis, strategy docs.

Somewhere around the 12-month mark I started noticing that my relationship with the tools had shifted in ways I didn't consciously choose. Not in a dramatic way. More like I'd absorbed a set of assumptions about how AI fits into work, and when I actually examined them, a few of them were... wrong? Or at least way more complicated than I'd assumed.

I want to share the five because I'm genuinely curious whether other people have hit the same things or if this is just me.

1. "AI saves me time."

This was the big one. I realized AI wasn't actually saving me time, it was shifting where my time went. Before AI, writing a strategy memo was maybe 70% writing/thinking, 20% research, 10% formatting. The writing was where I figured out what I actually believed.

After AI, the research and drafting happen almost instantly. So in theory I have all this freed-up time. In practice? For months I just did more stuff, faster. More memos. More emails. Higher volume. The thinking time didn't get reinvested into deeper thinking, it just evaporated.

I looked back at work I did a year ago and it was genuinely sharper than what I was producing with AI. That was a weird realization.

2. "More AI = more productive."

I think the actual relationship is more like an inverted U. At low-to- medium usage, AI gives you real leverage. You use it for specific things where it clearly helps. But past a certain point - and I think I crossed it, you start outsourcing cognitive work that was actually keeping you sharp. Writing a first draft from scratch forces you to organize your thinking. Reading a full doc forces you to notice things a summary misses. When you hand those tasks to AI, you lose the cognitive byproducts, and those byproducts were often more valuable than the task itself.

3. "AI does what I tell it."

This is the one that messed with me the most. Technically true, but it misses something important: when AI generates a draft, it makes hundreds of small framing decisions, which points to emphasize, which structure, which examples. Then I edit within that frame. I'm not really directing. I'm reacting within boundaries the AI set.

I tested this by occasionally writing important pieces with no AI draft at all - just a blank page. They went in noticeably different directions. Not always better. But different in ways the AI version never would have gone. Those differences are mine and I think they matter, but I was losing them without noticing.

4. "I can tell when the output is wrong."

I can catch the obvious errors, outdated facts, wrong context, things that clash with stuff I know well. Those are easy.

What I can't reliably catch are the subtle errors: slightly skewed framing that leads to a different conclusion than the evidence supports, a comparison that omits the most relevant option because the model didn't know about it, an argument that sounds airtight but rests on an assumption that doesn't hold in my specific case.

These errors are invisible precisely because they live in the gap between what I know and what I think I know. The AI presents them confidently, they pattern-match to things that seem right, and because I'm reading as an editor (does this sound right?) rather than a researcher (is this actually right?), they sail through.

My most expensive AI mistakes were never the obviously broken outputs. They were the 95% correct ones where the other 5% was wrong in a way I wasn't equipped to notice.

5. "AI makes juniors as effective as seniors."

I hear this one a lot from managers and I think it's wrong in an important way. AI closes the output gap, a junior with AI can produce a memo that looks almost identical to a senior's work. But it doesn't close the judgment gap. The senior reads the AI draft and notices what's missing because they've lived through the situations the draft references. The junior reads it and sees no flaws.

The part that worries me: juniors become seniors by doing the work badly first, learning from the friction, and slowly building judgment. If AI smooths away that friction, the learning never happens. You get people who can produce polished work on any topic and have deep understanding of none.

I want to be clear, I haven't stopped using AI. I use it every day and I think it's genuinely powerful. But I've adjusted how I use it based on realizing these beliefs were steering me wrong.

The big shift: I've started treating AI less like a production tool and more like a sparring partner. I use it to challenge my thinking more than to produce my output. And I deliberately do some work without it - not because I'm anti-AI, but because I noticed what I was losing when everything went through the model first.

Could be totally wrong about some of these. Has anyone else hit similar realizations after extended daily use? Or gone the other direction, found that heavier use actually made you better, not worse? Genuinely curious.

Quero um prompt pra me transformar no Miguel ohara do Aranha multiveis mas não tô conseguindo

Is the difference as big as people say? What topics yield the biggest differnece? What topics yield the smallest? Would love to know as i want to get pro.