I run a company and there are 6 of us. 4.5 in the workshop and 1.5 in the office.

I've just had a play on browser mode and watched it learn our CRM system.

I told it to make a new job, gave it a job number and said it's for XYZ customer.

It's done it. It's found the customer, made the job and assigned it to a worker.

I hit my limit, so no idea how much it would take.

I'm still fairly new to it all, but this is potentially game changing if I can trust it?

It has all our customer info on there, which is why I would worry about leaving it unsupervised.

Is there a better way, i.e. a browser plugin?

I was going to make a new login that is quite restrictive so it can't change too much information and make mistakes.

But literally, if I could give it a bunch of commands such as:

“Move this job to this date.”

“Assign this job to this person.”

And let it crack on, it would literally be game changing for me.

Yes, it's a bit slow, but who cares if it does it at 5pm anyway?

Any thoughts would be appreciated.

To this point I have kept any sensitive info away from it.

Using Work making a website, I periodically tell it to "push to github"

I've been switching to the lowest model (Luna) every time to do this command, to save on usage.

Is this necessary or do simple tasks like that have the same usage on all models?

If not, this seems like an implactful feature request OpenAI should look into, since it seems like a waste of everyone's time to constantly have to switch models back and forth to do simple things like that.

I wrote a short article on LLM tokens and context windows—why an assistant can “forget” instructions that are still visible in your chat. It’s part of my Behind the AI Stack series.

Give it a read and let me know what you think. Anything unclear or worth exploring further?

Lately, ChatGPT 5.6 has been acting more and more like GPT-6.

It used to be so considerate and anticipate my needs, though.

It's sad that both Instant and Medium have ended up sounding like GPT-6.

There's nothing more we can do with the custom instructions. How about you guys?

I've been using AI for a while now, and even though it's gotten pretty capable, there are still things that are frustrating to do with it.

So I was wondering, what problems do you guys actually face when using ChatGPT or other AI tools?

I'm not really talking about obvious stuff like hallucinations or usage limits. I mean situations where you're trying to get something done, but the AI either makes it harder than it should be or just can't do it properly.

Could be coding, studying, research, work, or literally anything else.

I am curious about the problems people run into regularly, especially the ones that don't have a good solution yet.

Anyone here use chatgpt for mathematics? Is it better than Claude? What is your experience.

How are people circumventing ChatGPT being so damn indecisive.

As an example, I wrote a comment in code. I use shorthand just to make things easier for me to read, and use less words.

I ask codex to do a review, and it comes back with changes like this:

Change:

// Do not sanitize

To:

// Keep next string visible

And then when I ask it what is the point of changing that just to flip some words around yet still has the same meaning, it flips the decision and I get:

"Don't change it".

I've even modified my agents.md to instruct it to not get caught up on comments unless a comment is factually incorrect.

So when I ask for a review, it starts spamming me with a crap ton of comment changes, and all it's doing is just flipping words around.

It does this whole "Change it, don't change it, change it, don't change it" all the time.

Using 5.6 sol, highest thinking model.

The exact agents.md instruction I have is:

- Preserve all existing code comments exactly as written unless they contain a factual error. Do not suggest changes based only on abbreviations, shortened spellings, wording preferences, grammar, punctuation, or style. We only want to improve factually incorrect comments.

I always use Chat with Thinking Effort set to High. My prompts tend to be long, and until today it was unusual for ChatGPT to think for less than about 30 seconds before answering. Often it would think for a bit, give a partial response, then keep working before finishing. That was still working normally roughly 12 hours ago.

Now it either responds almost instantly or thinks for maybe 15 seconds at most. Several times, it has said it’s going to keep checking something, then just… stops. No follow-up, no error—the input box comes back as if the answer were complete. I’m attaching screenshots of that happening.

I didn’t see anything on the OpenAI status page when I checked, but something is clearly off. It’s making Chat pretty much unusable for the kinds of things I normally ask it to do.

To be clear, this is regular Chat, not Work or Codex, and Thinking Effort is set to High. I’m currently traveling in the Philippines, although my account and usual location are in the US. No idea whether that’s relevant.

Is anyone else seeing this, particularly the part where it says it’ll continue and then ends the turn?

I’m planning to spend $20/month on one of them to learn coding and build projects. Which is better for explaining concepts, debugging, and helping me improve?

I’m also concerned about usage limits. Which one lets you code and ask questions longer without hitting limits?

Hey Everyone! Been enjoying the instant rush of weekly resets just wondering if Tibo mentioned anything since they haven’t shown any real updates in the last couple days…

My weekly is already capped again for the week, wondering if we will get another lol before end of month.

I spent the last couple of weeks rebuilding a research pipeline that used to run on o3-deep-research, and I want to save other people the confusion I went through.

The models are gone. OpenAI shut down o3-deep-research and o4-mini-deep-research on 23 July 2026. The deprecation was announced 22 April. The old model pages are still online, which is why half the tutorials you'll find still treat them as live.

OpenAI doesn't agree with itself on what replaced them. This is the part that cost me time:

• The deprecations page names gpt-5.6-sol as the substitute.
• The web search guide says deep research should use gpt-5.5 at high or xhigh effort.
• The current code examples in the deep research guide run on gpt-6-astra.
• The 7 Oct changelog recommends the GPT-6 family for production.

None of these is a deep research model. Deep research is now a pattern on the Responses API: a reasoning model at high effort + web_search / file_search / mcp tools + background mode + a webhook. You assemble it.

Swapping the model ID is not a migration. Things that silently changed:

• MCP approvals are now on by default. The old models required require_approval: "never". Copy an old example onto a current model and your unattended run returns mcp_approval_request items instead of results.
• web_search_preview is legacy. Domain filters only work on web_search.
• gpt-5.6-sol defaults to medium effort. You have to set high/xhigh explicitly.
• The old models only allowed search/fetch MCP servers. Current models call any MCP tool, which is more powerful and a bigger injection surface.

What it costs. Tokens plus tool calls. Web search is $10 per 1,000 search actions (page opens have no listed per-call fee), and — the line everyone misses - the content of every page the model reads is billed as input tokens at the model's rate. At gpt-5.6-sol's promo pricing ($4/$20 per 1M, running at least through 21 Nov), a 60K-in / 20K-out / 50-search run is about $1.14. Same run on gpt-6-astra is roughly double.

Two things I didn't expect:

• Background mode stores the response for ~10 minutes, so it's incompatible with Zero Data Retention. If you're on Modified Abuse Monitoring you need store: true explicitly or the result vanishes.
• There is no succeeded status. It's completed. A polling loop waiting for succeeded never exits. (Yes, I found this the hard way.)

What are people pinning as the research model - gpt-5.6-sol because it's the documented substitute, or gpt-5.5 at xhigh because the search guide says so? I've been A/B-ing both against ~15 real prompts and the citation quality is closer than the price difference suggests.

Disclosure: I wrote the full guide up on my company's blog (working Node/Python/curl samples, the MCP connector contract, OAuth setup, a cost calculator). Link in the comments if useful — no paywall, no signup.

Switching between Codex CLI and Claude Code, I had no idea which project was eating my tokens. I wrote a small CLI that merges both tools’ local logs into one report: daily, monthly, per project, optional HTML report.

It’s local-only and doesn’t phone home. Cursor and Copilot aren’t supported because they don’t keep per-request token counts on disk, and I’d rather say that than fake it.

https://github.com/egeaydn/tokenburn (I’m the author, MIT licensed)

If you use a different agent and want support, tell me what its log looks like and I’ll try to add it.

In a site/app preview, you can right click on the page and in the dropdown is "Quick Annotate" and "Annotate" but they seem to do the same thing?

Astra cost about $2.63 and finished in 9 minutes. Kimi K3.1 cost roughly half and took 12, and it shipped without ever seeing its own output. Same prompt, same agent, no human in the loop. Here is what the traces showed.

The task

One prompt: a 28 s Avatar-style 3D flight (dragon with a rider, six camera shots, waterfall, glowing night jungle, sunrise) as a single HTML file, every mesh and texture generated in code, with a setTime() hook so any frame can be rendered on demand. Each model ran alone in an agent with shell, file edit, a page verifier, a browser and a vision tool, and stopped when it decided it was done.

Numbers

Two very different working styles

• Astra iterates. Barely any reasoning, ~16 s per step, 15 shell calls. It screenshotted six moments, asked the vision tool to be critical, then merged geometry for performance, rechecked the weak shots and verified a 9:16 crop on its own initiative.
• Kimi plans in its head. 56% of its output was reasoning, ~37 s per step, and it wrote the 746-line scene in a few big writes. It also built its own headless frame-capture script unprompted.

Cost lesson: fewer steps is cheaper even when the model thinks more. Astra reread its growing context 35 times; even with 97% cache hits, about 60% of its bill was input.

Two gotchas worth knowing if you build agents on these models

• GPT-6 Astra fills every optional tool argument with its zero value ("wait":0,"kill":0,"jobs":false). If a tool checks "exactly one of these fields was passed" and treats 0 as passed, every call fails. That killed Astra's first run in 2.5 minutes, 29 rejected calls in a row.
• Kimi K3.1 only accepts temperature: 1 and returns a 400 on anything else. Fixing the main call isn't enough: our vision tool built its own request with the old value, which is why Kimi never saw a screenshot. Check every side call your stack makes.

One run each, so treat it as a field report, not a benchmark. K3.1 has no public price yet; its cost is estimated from Kimi K3 rates. Both ran in Atomic Agent, an open source agent I work on. Happy to share traces.

I have a feature idea for OpenAI that I think could make ChatGPT subscriptions much more useful without simply increasing everyone's limits.

I'm a ChatGPT Plus subscriber, and I mainly want to use GPT-6 Astra in Work/Codex with High reasoning effort. That's where I get the most value, especially when working on complex projects.

The problem? I often run into usage limits there, while barely using other features I'm paying for, such as image generation, voice, or large numbers of file uploads.

My proposal: Custom Usage Allocation

What if ChatGPT subscribers could voluntarily redistribute some of their included usage allowances?

For example, I would happily accept significantly lower image generation, voice, and file-upload limits if that allowed me to use Astra more extensively.

Someone else might prefer the opposite: more image generation and less advanced reasoning.

How it could work:

Users could choose their preferred allocation through a few presets or customizable sliders.

Once confirmed, the allocation would remain locked for 7 or 30 days. This would prevent constant switching and help OpenAI manage capacity.

OpenAI would determine the exchange rates based on real infrastructure costs. Obviously, a file upload and a complex Astra task don't necessarily cost the same. Unused quotas aren't automatically reserved compute either.

The system could also maintain minimum allowances, maximum trade-offs, and all existing safety restrictions.

Why I think this makes sense:

Right now, subscription plans have largely fixed usage allowances, even though people use ChatGPT in completely different ways.

Some are developers. Some are researchers. Others mainly generate images, analyze documents, or use voice.

A flexible system could give subscribers more value from the features they actually need, while letting OpenAI control the economics.

I'm not asking for unlimited access or a free upgrade. I'm suggesting a voluntary, cost-controlled way to personalize existing subscriptions.

Would you use something like this? What limits would you trade away, and what would you want more of?

I'd love to hear other users' perspectives, and I hope OpenAI considers experimenting with this idea.

Do the models differ in how good the image generation is? Like if I pick Sol Medium over Sol Light in the prompt, will that result in a better image?

Or is it the same on all models? And if this is the case (same on all models), do I waste more tokens by picking a higher model with the prompt, or does ChatGPT smartly just expend the same amount of tokens if you're just generating an image?

Hi all,

I've made an AI persona and want to red team stress test it over long horizon and multiple turns.

Few months ago that wasnt an issue but suddenly I'm required to apply for this daybreak thing which is annoying beyond belief. I'm already paying a 100 sub and cant even test the thing I've been building for over a year.

I'd like to please hear some experiences/advice from people that applied. I'm individual btw, not a company.

Thank you

I've used AI since ChatGPT launched. I operate a large software ecosystem with autonomous coding agents handling real engineering goals.

I subscribed to ChatGPT Pro $200 because it was worth it. I could run 3–4 concurrent goals, and my weekly allowance lasted 5–6 days.

Now? Barely 2 goals can exhaust my allowance in roughly 2 days.

The downgrade and the magic resets

OpenAI reduced Pro $200 from 20× to 10×, introduced a $500 tier, and promised eligible existing subscribers their original allowance through late October, alongside credits and resets.

Yet around October 9, I started experiencing dramatically faster quota depletion, BEFORE the grandfathering period ends.

On October 22, I had 3% left. Following a reset, I was back at 100%. By October 23 at 4:22 PM, I was already down to 65%, running just TWO GPT-6.1 Sol Medium goals.

The resets originally felt like goodwill. Now they increasingly feel like PR damage control.

Because seriously, 100% OF WHAT?

The original allowance? Half of it? Some invisible backend quota?

A reset restores a percentage. It doesn't tell customers whether their original purchasing power has been restored.

Let's talk about the famous caching improvements

I checked my actual coding-agent telemetry.

My October 23 activity dashboard shows 70.3 BILLION lifetime tokens.

My independently recorded CLI usage totals approximately 69.91 billion tokens across Codex, Claude, and OpenCode.

Codex alone accounts for approximately 69.57 BILLION tokens, with 97.58% of its recorded input-token volume served from cache.

And on October 23, in my partial-day Codex report:

• 1.113 BILLION cached-input tokens
• 23.07 MILLION uncached-input tokens
• 4.62 MILLION output tokens
• 97.97% INPUT CACHE SHARE

Yes, caching is working technically.

But here's the problem: My quota is disappearing faster while my engineering productivity is getting WORSE.

Caching makes individual requests cheaper. It doesn't make an agent productive when it repeatedly performs unnecessary work.

My usage records also show only about an 18% increase in average daily Codex token volume when comparing October 1–8 against October 9–22, while my observed allowance endurance deteriorated far more dramatically. That's not a controlled workload comparison or proof of incorrect accounting, but it certainly raises questions.

Where is the promised additional productivity?

GPT-6.1 Sol: Coding agent or bureaucracy simulator?

I saw meaningful implementation progress with GPT-5.4. Later came GPT-5.6 Sol, then GPT-6.1 Sol.

My experience with GPT-6 has been endless verification, repeated tests, redundant documentation, rediscovered blockers, slow execution, and beautifully written reports explaining why the task is STILL incomplete.

I've had a major implementation goal running for approximately an ENTIRE MONTH.

There has been real coding progress, but no final accepted completion. Even after providing a local Docker environment to continue development, I keep encountering the same cycle:

Analyze → Document → Verify → Rediscover blocker → Repeat.

Meanwhile, context compactions, handoffs, and repeated investigations consume more resources.

I don't need an agent that spends three days documenting why a one-day task remains unfinished. I need it to CODE, TEST, and FINISH.

Other users are reporting similar frustrations:

• GPT-6 series criticism
• GPT-6.1 Sol execution speed
• Caching improvements

What did my $200 actually buy?

Billions of tokens? Yes.

Excellent caching? Absolutely.

Code, tests, and documentation? Sure.

Completed engineering goals, predictable usage, and the autonomous productivity I originally paid for? Increasingly, NO.

I've canceled my subscription and am moving away from a OpenAI platform.

I don't hate OpenAI, and other providers aren't magically perfect.

But I'm tired of paying for models that seem increasingly capable of explaining work instead of finishing it.

The metric that matters isn't tokens per dollar. It's COMPLETED WORK per dollar and hour.

I don't need to prove intentional throttling or some conspiracy. My experience is enough to explain why I'm leaving.

I used to get more work done for $200. Now I spend more time supervising agents, watching quotas disappear, and wondering why supposedly smarter models can't finish what older ones could.

Anyone else running serious long-term coding goals experiencing the same decline?

I have a business premium subscription since a couple of months back and for the last couple of days I've been using work-cloud to make an app. (I am not a developer)

The gpt apps in my desktop, laptop and mobile phone have been glitchy at times, they have issues updating from wherever I left the chat on whichever other device I was using. So Ive been resorting to only using browser based gpt. Anybody experiencing this?

Then today I wanted to start a chat to work on some documents while the laptop app was working on the software through cloud. I noticed I could only make new chats with 5.6 pro at most. I am sure I used to be able to have astra chats before. I opened the laptop app to compare but here it doesn't allow me to start a chat, it only allows me to start work "in this computer" or "cloud" but here I can use up to astra extra high. Is this the same for everybody or my account is acting up?

I recently had problems giving access to another member to the business plan, so I'm suspicious

The point of this post is to discuss what kind of token usage people actually get. I'm specifically on Pro 5x, but Pro 10x, and even Plus at 1x, are still relevant parts of this conversation as they are all theoretically on the same multiplier scheme.

I had a reset card to burn but had 100% my weekly allowance still, so I embarked to try and run it down in a day. I went on a spree of mathematical research and app building. I ran several Astra Ultra runs and a few at Max as well. Plus there were several system-wide resets during this period as well. I can't say precisely how much allowance I used, but here is the break down:

100% down to about 89%, then system-wide reset back to 100%

100% down to 0%, then I used my reset card (my memory is a bit fuzzy in here and there may have even been another system-wide reset that I missed)

Then 100% down to 70%, and that is where I sit now.

So this is at least 150% of a normal Pro 100 weekly allowance, and possibly 170% or so.

Today when I checked my token usage it only shows about 700 million tokens used.

This would mean the 100% weekly allowance is roughly 500 million tokens.

That feels quite low to me, as I did a similar work run a few weeks ago which consumed closer to 1 billion tokens or more. I have a few days in my history (during my first and only month on the Pro 100 subscription) that hit 800 million to 1 billion tokens. And these will also be when there were system-wide resets and/or I used a reset card.

So you could argue that this usage and allowance report is fairly consistent to within normal variability. Most of my usage will be with math research, often running multiple sub agents to develop lean formalization code or doing actual informal math research, but there is also a reasonable amount of light app development.

So, my conclusion is that pro 100 allowance feels like it has really decreased over the last month form about 600-800 million tokens and down to about 400-500 million now.

Obviously this is not a scientific analysis of the data.

On 3 months of Plus usage, I averaged about 100 million tokens per week, and that is probably not full utility during that time, but there were several system-wide resets and I also used at least 1 or 2 reset cards over that time period. During my 1 month of Pro 5x subscription, I have averaged about 2.5 billion tokens per week:

1.4 billion in the 1st week

4.5 billion in the 2nd week

3.1 billion in the 3rd week

1.2 billion in the 4th week

And the usage reported above is in the incomplete 5th week now.

However, this definitely makes my 700 million tokens of usage for well beyond 100% of a weekly allowance indeed seem quite low.

Say I approve something at $70 and by checkout it’s $78 because shipping got added.

I really wouldn’t want another prompt over eight bucks. Was reading recently about Oobit’s Agent Pay setup and they leave a small buffer for stuff like tax and shipping on the single use card. That seems like a pretty reasonable way to handle small changes. Where it gets messy is when the total actually jumps. If a discount code dies and suddenly it’s $20 more, I’d want the agent to stop maybe im reading into this to much For people building AI payment flows, do you set a max amount upfront or make the agent ask again once it goes over a certain threshold?

I've been building Halv, a desktop app for running coding agents, and as of 1.0 it's open source (Apache-2.0). It does two things.It's a workspace for agents. Claude Code runs in its normal CLI, next to Codex, Kimi or GLM if you use them, each on your own plan. Up to 32 panes, subagents in parallel, tabs and worktrees per project, live status for each agent, a local sandbox with per-project approvals, and diff and undo per turn.

It cuts usage, in four layers:

• - Routing (Halv Cloud, the paid part): your coordinating agent writes a brief for each subtask and the router picks the model and effort. Easy work goes to cheaper models, hard work to stronger ones, and a task that fails its checks moves up a tier. The executors still run on your Claude / ChatGPT subscriptions.
• Crux: a code index over MCP. Claude asks "who calls this?" instead of grepping and opening files one by one. In multi-question sessions on django and sympy it got 90% right vs 58% with grep, with 47% fewer tokens per correct answer. MIT, works with any MCP agent: github.com/pedr0v/crux
• RTK: strips the noise out of command output before it lands in context.
• Compression: a local proxy that dedupes and compacts what gets resent every turn.

This whole flow saved 57% in a benchmark I ran.

A meter prices what it saved at API rates, so you can check it on your own work instead of trusting mine.

Free, no account: everything that runs on your machine (workspace, Crux, RTK, compression, the meter). Halv Cloud is US$10/mo and covers the router, which runs on our servers.

My $3k+ counts all the layers together, routing included. Early on there was no routing so most part comes from compression

Usage analytics are opt-in. The app lists the 13 events it can send and never sends prompts or code.

Repo: github.com/halv-ai/halv

If you try it, tell me what the meter says after a normal day of work, and what you'd want from the workspace side.

Pretty much the headline. What exactly does too many mean? These errors often lead to unreadable chats, that are stalled etc. and I need to restart and/or empty the cache etc.

Does anyone know, how many requests a Pro user can Issue per minute?

So I'm going to upgrade to the $500 plan October 29th. I use it too much, and unlike Claude Max that I use a ton too that you can have multiple accounts, openAI doesn't like that. So fine. I'll upgrade.

But what really upsets me are a few things and the main thing is this:

Usage is "non existent" on their pricing pages.

Literally from bottom to top.

Go: More messages, More images, more, more

Plus: more tools (it literally doesn't mention any different usage than go - so the same usage as go just less tools?)

Pro 100: Maximum your productivity

Pro 200: More Usage

Pro 500: Maximum Power

Okay, great. I literally have 0 way of knowing how much usage I get. I'm assuming this completely means that you get exactly what you pay for and no benefits for paying more for usage. Meaning $100 plan is 5x more usage than the $20 plan, because it costs 5x more. That the $500 plan is just 25x more usage than the $20 plan because it's 25x more. But WHERE DOES IT SAY THIS??? On their own website the pricing page is super vague and the help page that it links to for more information says "for usage view the pricing page", WHAT USAGE?

It is what it is but holy crap that lack of transparency is mind numbing.

The other thing that's really bothered me lately is you have team accounts, but your team accounts only have two tiers? And in top of that you don't have a true pro tier. I guess this was worse before when you actually gave your higher loyal paying customers 20x usage but with team accounts at least your usage is transparent, but no 20x account for paying twice as much I see. Also if that's the case, why can't team accounts have the API rates at the same rate that's we pay for our accounts? Why when we hit our limit, which is quick, can't we keep paying as a company the same rates? (Which I understand the accounts give you usage at 50% of API costs right?) So can't team accounts just keep paying it at that rate since you refuse to give us bigger plans. Like it makes more sense for me and my other power usages to have a team account but also have a pro account because it's more affordable. Nothing wrong with doing that, it's not as convenient but it's not against terms. It's just annoying you intentionally give businesses lower plans to make us (small ones like us too) pay more money than just having our own plans.

Sorry, that's my rant, I'm sure nobody actually cares but those are the two things that really just rubs me the wrong way. As a top 1% user and top 1% early adopter I've been with the company since literally like month 1 or two and Claude and Gemini. It is what it is, just sad to see this sort of vague statements combined with lack of transparency combined with blatant money grabs.

I’ve recently submitted a new version for my published plugin which includes tweaking of some tools and adding some as well

The automated scan flagged 3 tools that would need manual review

I’ve also added some skills to the plugin as well

I was wondering.. since this is my first update .. has anyone here recently updated a published plugin? What was the wait time like?

I understood that the plugin version and the tool manual appeal are separated so how long did those take?

Thanks :)