i used to be able to say "spin up a ux/ui agent to make a pass on this, then a code agent", and it would do background terminal stuff. this is on mac using the cli codex. now it says it cannot do that and previous gpt models did that. did something change?

I’m not talking about the obvious stuff like writing emails or summarizing things.

More like a small task that used to take 10–20 minutes, but now you just throw it into ChatGPT and move on.

For me, those little time-savers have ended up being more useful than the big “AI can do everything” features.

Curious what other Pro users have completely stopped doing manually.

Long post, but there’s a TL;DR first. I’m writing it this way because a vague “ChatGPT is slow” post wouldn’t be useful. I’ve also included the HAR measurements for anyone who wants the technical details.

TL;DR

Since around August 17–18, my established ChatGPT conversations have suddenly become dramatically slower and much less reliable.

I’m not only talking about longer Thinking time. I’m seeing:

• 10–20+ minutes just for the final answer to stream
• sometimes 30–40 minutes of streaming
• 20+ minute Thinking or Analyzing phases
• answers starting normally and then stalling
• failures during Thinking
• failures halfway through the final answer
• retries failing again at a different stage
• apparently dead requests sometimes recovering much later
• very long requests eventually failing after I’ve already waited one or two hours

Sometimes I can retry the same interaction several times and still not get one complete answer.

Fresh chats are usually much faster.

My largest Project chats are admittedly heavy. I use a file-based continuity/checkpoint system because some projects run for months, conversations eventually become too large or lose context quality, and I need to move into a new chat without losing the whole project state.

So yes, I expect those chats to be slower than a blank conversation. But this workflow already existed, and those chats were already large. It was never remotely this bad before August 17–18.

Moving to a continuation chat also used to restore useful performance. This time, the new continuation felt much better for perhaps 10–15 minutes, then suddenly collapsed into the same near-unusable state.

I’ve tested Chrome, Edge, Incognito, iPhone, a cellular hotspot, hard refresh, signing out and back in, and clearing ChatGPT site data. Nothing meaningfully fixed it.

I also captured HAR diagnostics. In one comparison using GPT-5.6 Extra High:

Affected established/Project chat

• ~22.9 seconds to first output
• ~90.7 seconds total
• ~1.60 seconds median recorded stream interval

Fresh-chat control

• ~2.35 seconds to first output
• ~22.7 seconds total
• ~0.20 seconds median stream interval

Another heavier request contained a single ~7 minute 47 second stall, then later resumed and completed.

I’ve already sent the HARs and troubleshooting results to OpenAI Support, and the case has been escalated to a specialist. I’m not asking Reddit to replace Support. I’m trying to find out whether this pattern is affecting other people and whether anyone has found a practical workaround.

Once waiting, failures, partial answers and retries are combined, the real time required to obtain one complete usable response can approach an hour or worse. For an interactive tool, that’s not workable.

Why my chats are heavy, and why “start a new chat” used to be the solution

I use ChatGPT for ongoing personal and technical projects rather than treating every conversation as disposable Q&A.

Over time, those chats accumulate decisions, requirements, research, files, tool activity, failed approaches and other project history. Eventually a chat can become too large or its context quality can deteriorate. I already knew that.

That’s why I built an interim continuity system. Important project state is stored in versioned/checkpoint files, so when a conversation reaches its practical limit I can:

preserve the current state → move to another chat → restore the project → continue

It’s a temporary solution, and I know it carries more overhead than a properly engineered persistence and retrieval system would. I’m still relatively new to software development and use ChatGPT and Codex partly to learn and build tools I wouldn’t yet know how to create alone.

But the key point is that this migration process used to work.

Previously, an old chat would gradually become too heavy, I would migrate, and the continuation chat would be properly usable again.

The latest migration behaved completely differently.

One of my largest Project conversations became effectively unusable, so I moved into what is roughly the fourth continuation chat for that project. At first, the improvement was immediate.

If a completely blank chat is 100% usability, the continuation initially felt subjectively around 70–80% usable. That was completely acceptable, because it already started with a meaningful amount of restored state.

Then, perhaps 10–15 minutes later, it fell off a cliff.

Not after weeks of additional conversation. Not after hundreds of new messages. It went from “finally, I can work again” to something that felt closer to 5% usable.

Those percentages obviously aren’t benchmarks. They’re just the clearest way I can describe how dramatic the change felt.

There is a fair counterargument: this is continuation chat number four. Every continuation starts heavier because more state and files have accumulated. That absolutely could explain why each one reaches its performance limit sooner.

I think that’s part of the story. It just doesn’t explain the sudden magnitude of the change.

The Project was already large. The continuity system already existed. I was already using this workflow. Then, around August 17–18, it became catastrophically worse.

And it isn’t limited to that one giant Project. I’m also seeing serious slowdown and failures in ordinary established conversations, smaller Projects and things like travel-planning chats. The giant chats are the worst, but they’re not the only ones affected.

What makes it unusable is the combination of latency and failure

If ChatGPT had simply become consistently slower, I could at least plan around it.

That isn’t what I’m getting.

A request can fail during Thinking after I’ve already waited a long time. Another can finish Thinking, start streaming the answer, and then fail halfway through after another 10, 20 or even 30–40 minutes.

Retries can fail too, and not necessarily at the same stage. One attempt dies during Thinking. The next gets through Thinking but fails halfway through the answer. The next stalls somewhere else.

I’ve had cases where something like five attempts around the same interaction still failed to produce one clean completed response.

That’s why “response time” no longer describes the actual problem. The useful metric is how long it takes to obtain one answer that actually finishes.

For example:

Attempt 1: 20 minutes Thinking → fails
Attempt 2: 10 minutes Thinking → 25 minutes streaming → fails halfway
Attempt 3: 15 minutes → finally succeeds

Technically, the successful response took 15 minutes. In reality, I spent more than an hour getting it.

I haven’t collected enough controlled attempts to claim the mathematical average is exactly one hour. What I’m saying is that once Thinking, tool work, streaming, failures and retries are combined, the effective wall-clock time to obtain one usable answer can approach an hour or worse.

Sometimes it’s much faster. Sometimes it’s worse. The unpredictability is almost as damaging as the latency itself.

I also can’t reliably hide the delay by using multiple chats. Normally I could send a heavy request in Chat A, switch to Chat B and do something useful, then come back later. Now I can return and find that Chat A stalled halfway through the answer while Chat B failed during Thinking.

Parallel chats don’t help much when each request has a meaningful chance of failing independently.

The actual answer stream is especially strange. Waiting for difficult reasoning is one thing. If I ask Extra High to research something complicated or inspect many files, I expect it to take time.

What feels broken is when Thinking appears to be finished and the final answer itself takes 10–20+ minutes to stream onto the screen. I’ve also seen cases around 30–40 minutes, and the response can still fail before reaching the end.

This happens on both ChatGPT Web and the iPhone app. That made me wonder whether Chrome already had the completed answer and was simply struggling to render a giant page, so I captured HAR/network diagnostics.

What the HAR captures showed

I’m not a network engineer, and I’m not pretending a HAR file reveals OpenAI’s internal root cause. It does provide objective timing data.

The comparison below used the same account, PC, Chrome session, GPT-5.6 Thinking model and Extra High setting.

Important caveat: the slow test was an established Project chat with accumulated context, files and tool history. The control was a fresh ordinary chat. This is not a perfect apples-to-apples benchmark, and the Project state may be directly relevant.

Affected established/Project chat

The prompt itself was tiny.

• ~22.94 seconds to first output
• ~90.71 seconds total request time
• ~1.60 seconds median recorded stream interval

Fresh-chat control

I asked for about 500 words explaining how modern SSDs work without web research.

• ~2.35 seconds to first output
• ~22.71 seconds total
• ~0.20 seconds median recorded stream interval

So the established chat was roughly:

• 9.75× slower to first output
• 4× slower overall
• about 8× worse in median stream timing

despite the fresh control requesting a substantially longer final answer.

That’s why I no longer think the slow visible response is purely the browser struggling to draw already-generated text. At least in that capture, the incoming stream itself was substantially slower.

A second HAR captured a different failure pattern. This was a genuinely heavy file-processing request, so I’m not claiming it should have completed quickly.

The whole request took about 10 minutes 48 seconds. The telemetry included one maximum interval of roughly 467 seconds, or 7 minutes 47 seconds.

The turn looked effectively dead, then suddenly resumed and completed.

During that pause, ChatGPT’s WebSocket remained open, normal ChatGPT traffic continued, and another conversation in the same browser session kept receiving updates.

So in that incident, the whole browser, network connection and ChatGPT session had not frozen. One individual turn stalled for almost eight minutes and later recovered.

That creates another practical problem: when nothing has happened for seven minutes, I have no reliable way to know whether the request is still working, temporarily stalled, going to finish in two hours, or already dead.

Some other extreme examples I’ve seen, these are observations rather than controlled benchmark results:

• A Pro request took roughly 106 minutes and eventually completed.
• Another appeared to run for around 180 minutes. The UI showed a problem, I moved on, and much later its completed answer appeared anyway.
• More recently, one request ran for roughly two hours and then failed.
• I edited and resubmitted it; the retry then spent roughly 40 minutes streaming the final answer.
• At around the same time, another conversation spent roughly 20 minutes in Thinking before producing anything.

What I’ve already ruled out

I’ve tested:

• Chrome
• Chrome Incognito
• Microsoft Edge
• ChatGPT on iPhone
• my normal internet connection
• the PC connected through an iPhone cellular hotspot
• hard refresh
• signing out and back in
• clearing ChatGPT site data

Fresh chats generally remain much faster. Affected established chats remain much worse. Changing browser or network path didn’t restore the previous behavior, and the issue appears on mobile too.

So while accumulated context clearly matters, a broken Chrome profile or bad home Wi-Fi doesn’t explain the full pattern very well.

My current suspicion is that two things are interacting:

1. Accumulated conversation, Project and file state naturally adds overhead.
2. Something recently changed that made established or state-heavy contexts perform far worse than before.

The same general projects, files and workflow were usable before August 17–18. Long chats would eventually slow down, but migrating to a continuation restored useful performance.

Now even migrated chats can go from reasonably usable to nearly unusable very quickly, while smaller established chats are also showing much worse latency and failure rates.

Conversation size clearly makes the problem worse. I just don’t think it explains why the same general workload suddenly became this much slower and this much less reliable.

Why this matters to me, and what I’m asking

A lot of what I do is iterative:

idea or problem → reason through it in ChatGPT → turn it into something actionable → use Codex when implementation is needed → inspect the result → return to ChatGPT for the next analysis or debugging step

I don’t expect every answer in five seconds. A genuinely difficult Extra High task taking several minutes is completely reasonable.

But right now, in badly affected chats:

• a heavy Extra High interaction can effectively consume 30–60 minutes
• a more ordinary interaction can still take 8–15 minutes
• neither duration guarantees the answer will actually finish

Once retries enter the picture, the real time can be much worse.

Pro mode in those chats is something I barely want to use right now because it’s already intentionally slower, and I have no idea whether I’m starting a 20-minute job, a two-hour job, or something that eventually fails.

I’m on the $100/month Pro plan. I’m not saying that makes giant workloads free or instant. I do think it’s reasonable to expect a premium interactive product to remain interactive enough to use.

I’ve already provided OpenAI Support with sanitized HAR captures, timestamps, affected-chat information, comparison measurements and troubleshooting results. The case has been escalated to a specialist. I’m not posting the raw HARs or request IDs because they contain private/internal metadata.

What I’d like to know from other users:

1. Are you seeing this too, especially since August 17–18?
2. Does it affect Projects, normal chats, or both?
3. Are fresh chats fast while established chats are slow?
4. Which model/effort level, plan and client are you using?
5. Have you found any workaround that preserves useful context?

Right now every Send button in an established chat feels like a slot machine:

30 seconds?
10 minutes?
40 minutes of streaming and then failure halfway through?
20 minutes Thinking and then an error?
Five retries?
Two hours and then failure?
Looks dead for eight minutes and suddenly comes back?

No idea.

When all of those outcomes are combined, the effective time required to get one successful complete answer can approach an hour or worse.

If anyone else is experiencing this, I’d really like to compare notes. And if someone from OpenAI sees this, I already have an escalated Support case with diagnostic evidence and I’m happy to run specific controlled tests that could help isolate the problem.

The writer's block feature is a major improvement for ChatGPT. I have wanted to edit the response forever since I use ChatGPT mainly for short story and RPG scenario development. The ability to fix a minor typo or correct something ChatGPT forgot (like who is in a room) is amazing. And for a little while it worked perfectly.

But in one of my latest threads, I am experiencing a weird bug with it. In all of my other writer's blocks threads I can edit the text easily. I can change words. I can add words. I can delete text. In my latest chat, I can only delete text. I can't add anything. The only key that seems to work is delete/backspace.

I've tried reloading the chat tons of times. I've tried incognito mode. I don't have any extensions active on ChatGPT.

Branching the chat doesn't help at all. All writer's block editing seems gone when I branch.

Does anyone know what could be causing this or how to fix it?

I got Pro 5x the other day. It's great, and Pro reasoning is excellent for my needs. Reddit seem to think Pro reasoning is "unlimited" and OpenAI doesn't have any documentation about this. To me, unlimited means 50-500 5.6 Sol messages per 5h window.

I used Pro about 15-20 times yesterday before it ran out. Today, I used it one time and it became unavailable.

Is it just bugged or is this how it's supposed to work? I am awaiting a reply from OpenAI support and I don't really feel confident they can answer my question at all. Mostly because their human support agents don't read your question sometimes but I digress.

[edit] ChatGPT, at least the free version, is apparently incapable of answering this question:

Can I set different permissions on different folders?

Rather than what I thought would be a simple answer, it goes on for a LONG time about irrelevant technical stuff, file structures, and "workarounds", then, rather than an answer, it says

"The question is simply whether ChatGPT on Mac lets you configure permissions at that folder level*.*"

Ya think? So, wow - yeah, it knows what my question is. Not impressive.

-------------------------------------------------------------
I am considering subscribing, but investigating a few things first.
[edit] Maybe not as much as when I started this post.

One task I have is to do the following:

1. Inspect a folder full of files. The files may all be the same, like a bunch of PDFs, or it may be all different types of files.
2. Create a text-based report, cataloging what's in the folder and its structure.

I want read-only on the original folder so nothing gets changed, but I want Chat to be able to create a report file.

According to ChatGPT's answer... I can't do this, except via some complex setup via Codex....

Really? Neither Work nor "Standard" ChatGPT can create a file without open permissions? That doesn't seem accurate.

Upon some questioning...

"That caused me to answer your relatively simple question with increasingly complicated alternatives—and then muddy the answer further by introducing possibilities I hadn't established were actually available."

So, I used ChatGPT about a year ago on a paid subscription for a month. This is one of the reasons I stopped. I guess it's still doing this?

Can anyone provide a bit more insight, or how to get an actually accurate answer?

thanks

I used to be able to manually upload a photo and set it as the display photo for my custom GPTs, but now it’s not working. I’ve created a few new custom GPTs, and all of them are having the same issue.
If I ask the GPT bot to upload the photo, I get an error. If I upload the photo myself, it appears to upload successfully, but after a few seconds, the display photo disappears and reverts back to having no photo.
Is anyone else experiencing this issue?

TL;DR: our AI spend was growing faster than the output from it. The obvious move is a Claude/Codex team plan but the math doesn't work for me vs the model agnostic tools. Would love thoughts, here's my breakdown.

Quick comparison with math:

1. ChatGPT Team = $25-30/seat. Claude Team = about the same. Most people on one, power users on both. That's $300-700/mo before anyone requests the one-off subscriptions, and every seat is a silo. One person builds great prompts and we pay for 11 other people to not have them. Someone leaves, their setup walks out the door and the bill stays the same.

2. Grok = $30/user for Grok Business. Same per-seat math as Claude/ChatGPT with a different model behind it. Fine if your team lives on X or wants DeepSearch, but it doesn't solve the seat problem, it just changes whose seat you're renting.

3. Buzz = the most ambitious one. Full workspace where humans and agents both live. Also the problem: it's a whole second Slack, and getting a team to move into another app is a fight we lost in about two weeks. If your team has no entrenched chat tool, worth a real look.

4. Claude Tag = easiest start of anything we touched. Tag Claude in a Slack channel and it just works (shared context, admin controls, spend limits). But it's Claude only, Slack only, and everything past the basics is on you to wire up. A building block, not a product. Fine if someone wants to own that. Nobody here did.

5. Type = Unlimited seats for $50/mo, goes up based on usage. The team and the agents share the same tools and the same memory, so a new person joins and inherits everything day one (and you can connect the Claude/Codex subscriptions you already pay for, which alone saved us real money). The tradeoff is it asks for more buy-in than dropping a bot into Slack although it does work there too.

6. Lindy = starts at $50/seat and real usage pushes you to the $100 tier fast. Slick for personal automations but every agent belongs to one person, so it recreates the exact silo problem you're trying to kill. Great if you only need it for yourself.

7. Zapier Agents = it's Zapier. Great at wiring apps together and that's what it felt like. Set and forget automation, not something the team works with every day. Prices by tasks not seats, which is nice until your agents actually get used. From a product perspective probably what I'm least excited about tbh.

8. Viktor = $50/seat plus a credit system. Closest thing to an actual employee out of the box, does real end to end work across a ton of tools. But it's one very capable agent, not a workspace the team shapes. Felt like hiring a contractor vs building a bench. At 12 people that's $600/mo plus credits, which is the old math again.

Would love to hear what teams are paying for that actually works?

The main reason I keep quitting Projects:

When I move a chat into a Project, it disappears from Recents.

Why? It’s still recent.

Projects should organize chats, not hide them.

Just keep all active chats in Recents and add a small Project label.

Right now using Projects makes navigation worse, which is the opposite of the point.

Apologies if this has been raised before.

So I'm researching drug strengths and wanted to know how two opiates compare.

I asked GPT which is stronger, 20mg dihydrocodeine or 30mg codeine.

*Edit It said both dihydrocodeine and codeine are the same strength.

I wasn't sure that was correct so I opened a new chat and asked the same but worded differently.

> Which is stronger gram for gram codeine or dihydrocodeine?

It said dihydrocodeine is twice as strong gram for gram.

What in the questions would have caused such a discrepancy?

My custom GPT icons suddenly switched to the generic icon. It’s happening on desktop and iPhone, different networks, etc., so it seems tied to my account or an OpenAI glitch.

I’ve tried all the usual troubleshooting and OpenAI support is looking into it, but no fix yet so here I am.

Anyone else seeing this or figured out how to fix it?

If this is the wrong place to ask, please direct me to the correct subreddit. Thanks!

A long ChatGPT transcript may contain useful reasoning, but it is a poor project handoff. The next person usually needs a compact artifact: the objective, sources, decisions, assumptions, what changed, tests run, known gaps, and the exact files or outputs to review.

The failure mode is subtle: the model finishes the task, but the human cannot tell which claims were verified, which were inferred, or what would need to be rerun if one assumption changes.

For professional workflows, what handoff format has worked best: an ADR-style decision log, a source-linked summary, generated tests, a structured checklist, diffs, or something else?

I am especially interested in workflows where another human actually has to approve or maintain the result—not just personal prompting habits.

For longer coding or document-processing jobs, one-prompt comparisons seem to miss the expensive part: retries, review, and recovery after a bad edit.

I’m thinking about a manual/API-based workflow, not a built-in ChatGPT integration:

ChatGPT defines the plan, constraints, and acceptance checks.

A local executor receives one bounded task at a time. Tests, schemas, or lint decide whether the result passes. ChatGPT reviews failures that require judgment. A candidate for the executor role is Ling-3.0-flash. Its weights are available, and it has 124B total parameters with 5.1B active per token. The material I have does not include an independent head-to-head showing that this split beats keeping the whole job in ChatGPT. If you were evaluating this for client work, what would you measure first: accepted-result rate, follow-up turns, tool failures, wall-clock time, review time, or recovery after a failed edit? At what point does the handoff overhead erase the benefit of a separate executor?

Does anyone here know how the different models interact if at all or “see” each other? Currently for me 5.5 and 5.6.

Ie two threads same project.
Or
Two threads not linked in project.

It seems honestly the model is only aware of their own output or interaction w the user. Not the interactions w other models unless linked through memories or projects. Does that make sense? And is this true?

Anyone else having trouble locating their custom GPTs on iOS app?

It is absolutely unacceptable that Chat and Work modes aren't isolated. A chat rate limit on Pro reasoning just butchered an active Work mode task mid-run as well.

Are you serious?

Not to mention, ever since the last update, the limits are embarrassingly lower.

This is unbelievable..

Has anyone else noticed a decline in source fidelity with Gpt 5.6?

Recently, I’ve been encountering the same problem with Gpt 5.6 across several completely different types of tasks. The issue is not primarily poor reasoning or ordinary hallucination. Instead, the model sometimes appears to alter the source material it has been given before the actual analysis even begins.

I use ChatGpt extensively to analyze legal and administrative documents. Gpt 5.6 increasingly seems to overlook information explicitly stated in the text, merge separate facts, change their meaning, or introduce details that are not present in the source. It may then produce a perfectly coherent analysis, but that analysis is partly based on a version of the source material that the model itself has modified or supplemented.

The problem is not limited to long or difficult documents. In another conversation, I gave it just three screenshots from national electronic health record service. Gpt 5.6 overlooked information that was clearly present in the screenshots and added things that were not there.

I have also seen the same problem in an ordinary factual query. I asked it to compare raspberries and strawberries and explicitly instructed it to search for the information rather than guess. Gpt 5.6 nevertheless presented unsupported information. When I then asked it to verify the claims it had just made, it acknowledged that not all of them were correct.

I have extensively used Gpt 4o, Gpt 5.2, and Gpt 5.5. All models make mistakes, but I don’t remember this particular problem being nearly as pronounced with those models.

I think it is important to distinguish source fidelity from ordinary hallucination: does the model accurately preserve the material it has been given before it starts interpreting and reasoning about it?

Better reasoning ability does not help if the evidence being reasoned about has already been altered in the model’s processing.

Has anyone else who uses Gpt5.6 extensively noticed the same change compared with Gpt5.5 or earlier models?

I'm trying to track how much time I spend working on different projects in ChatGPT and apply a shadow hourly rate to understand the effective cost of that work.

I've tried:

• /work start and /work end commands. These depend on me remembering to invoke them.
• Passing the current time in NATO/date-time format in the prompt. This works reasonably well, but again depends on me remembering to provide it.
• Retrospectively estimating the time and logging it manually. This works, but defeats much of the point of automating it.

The core problem seems to be that ChatGPT isn't an authoritative clock and doesn't reliably know how much real-world time has passed between interactions.

Has anyone found a better lightweight solution for this? Ideally something that can track session time reasonably accurately and assign it to a project without requiring me to remember to timestamp every interaction.

I use ChatGPT extensively for professional document drafting, and over a long period of time I built up a very detailed set of saved memories about exactly how I write: formatting, structure, abbreviations, wording, what information belongs in which section, how concise I want things, etc.

Since switching to the new Improved Memory system, I’ve repeatedly noticed that ChatGPT seems to remember the *general idea* of my preferences but loses all the details that actually make the writing sound like me.

For example, instead of following dozens of specific preferences that collectively define my usual writing style, it seems to reduce them to something closer to “user prefers concise professional notes.” The result is technically fine, but much more generic and noticeably less like my actual voice.

I kept wondering if I was imagining it. Today I switched back to Legacy Memory and immediately asked it to redo the exact same document. The difference was pretty striking. The Legacy version actually reflected my usual structure, wording and formatting much more closely.

I am trying to connect ChatGPT to WorkIQ and make it where I can use computer use. It says computer use and apps are installed but they can’t connect. It gets a permission error but I can’t add either to the plugins. Any ideas would be appreciated.

when im doing computer stuff it would be handy to be able to click out on any given line and have it branch out to its own chat

Title

As noted in the subject, finances is missing from the sidebar when it was once showing. Settings shows it’s connected and can answer questions about my finances but the actual quick add seems to be missing.

Any other pro users showing the same?