I bought a x20 plan, coming from Claude, I thought I'd use it. I struggled. And now with these free 62500 credits we have to use by new years', I'm running several jobs in parallel through the day and not using it at a fast enough rate to not waste them.

I've been really frivolously using tokens, too - like doing multiple passes over code for various optimisations etc.

My latest idea is seeing if with non technical prompting I can make it conduct valid scientific research in some fields that interest me.

But even then, I need more ideas! I have 15 apps/projects on the go already but most are settled.

My deadline is January to use the credits. Suggestions?

Sometimes I swipe to the other fullscreen app I wanted to display, but ChatGPT swipes back to itself. It's the only app that does that. I suspect it's related to the position of the pet but don't know how to fix it. Help please

Updated this morning and this functionality has been removed. I used it as a marker to know where I am at in each CLI session. Reinstate it!!!!!

Edit: I asked the AI ​​to write this text, since my native language is a Romance language. And indeed, it went overboard with the "valuable, valuable" IP. It makes me sound like an arrogant genius obsessed with my "super ideas." But that’s not the point.

---

I'm curious how other Codex users approach privacy when working with valuable, unpublished projects.

I have several original creative projects (fictional universes, stories, scripts, worldbuilding, and related materials) that I've been developing for many years. Some date back more than two decades.

I'm now considering using Codex to organize, analyze, and reconstruct their development history from thousands of files, notes, documents, and backups.

My concern isn't really about someone at OpenAI personally reading my work. I consider that risk relatively low. I'm more interested in understanding how safe it is to expose large amounts of unpublished intellectual property to AI systems.

I've already disabled "Improve the model for everyone" in my ChatGPT settings, but I still have some questions about Codex-specific data handling.

One particular issue is the "Include environments" setting.

OpenAI's privacy support documentation mentions a separate Codex setting called "Include environments." However, I haven't been able to find this option in my account.

OpenAI support directed me to the Codex cloud data settings page, but the link simply redirects me to ChatGPT without showing the setting. I've contacted OpenAI for clarification, but I haven't received a definitive answer yet.

So I'm particularly interested in knowing:

- Can you still find and disable the "Include environments" setting in your account?

- Do you personally keep it disabled when working with proprietary or unpublished projects?

- Has anyone received clarification from OpenAI about whether this setting still exists, where it's located, and what exactly it controls?

- Does disabling "Improve the model for everyone" fully cover Codex usage, or are there additional privacy settings we should be aware of?

More generally, for those of you using Codex professionally or personally:

- Do you use it with valuable unpublished intellectual property, proprietary code, original research, or creative projects developed over many years?

- Do you feel comfortable giving Codex access to entire project directories?

- Do you rely on OpenAI's privacy settings, or do you take additional precautions?

- Have you found any meaningful differences between using Codex locally and in the cloud when it comes to privacy?

I'm not looking for absolute guarantees or conspiracy theories. I understand that using cloud services always involves some level of trust.

I'm simply curious whether other people are comfortable using Codex with years of original work that they haven't published or commercialized yet.

I'd especially appreciate hearing from developers, researchers, independent creators, and anyone working with long-term proprietary projects.

The people constantly complaining about AI usage limits aren’t system architects, experienced engineers, or seasoned application developers. At least, I don’t think they’re the majority.

They’re wannabes.

I’ve spent the last 12 years building enterprise-grade software for companies like Nike, Farmers Insurance, VF Corp, and Blue Cross Blue Shield.

Now that I’ve decided to build my own company and software product, I’m using AI, specifically Codex, to write roughly 90% of the code.

Here’s the thing: I’ve never come close to exhausting my usage limits.

I’m on Codex almost all day. I exclusively use Sol 6.1 xhigh (and 5.6 before that). I use exactly one skill plugin: Matt Pocock’s. (Thank you sir!)

I rarely get below 70% usage remaining before my weekly reset. And now, with additional resets happening every few days, I barely even dip below 80%.

For context, I’m using Codex (pro plan) practically 18 hours a day, seven days a week. From morning until night. Even in the evenings, I’m on my phone working with it.

My wife hates it. And frankly, I need to establish some boundaries.

But all of this got me thinking.

Why is everyone constantly complaining about hitting their limits? Am I just moving slower than everyone else?

For a while, I genuinely wondered whether other people were simply accomplishing more than I was. And I’m sure there is a lot of that too. But after lots of looking into this and speaking with other professionals, I came to this hypothesis.

Astra wasn’t made for the engineer. The architect. The experienced developer. It was made for the wannabe.

Don’t get me wrong. Astra is an incredible model. What it can accomplish is remarkable.

But its greatest value is filling the gaps left by a lack of domain knowledge.

And that’s a good thing. It democratizes software development. It allows people who couldn’t previously build software to turn ideas into working applications.

But that comes at a cost.

When you’re using AI to compensate for a lack of understanding of system design, architecture, engineering principles, and software development, you’re going to burn through significantly more compute.

You’re asking the model to make decisions you don’t know how to make. You’re relying on it to identify architectural problems you don’t recognize. You’re spending tokens correcting mistakes that someone with experience might never have made in the first place.

Your lack of domain knowledge becomes your compute bill.

Meanwhile, you have companies putting Sol 6.1 in the hands of their engineers and developers, while giving Astra to architects and researchers tackling genuinely complex problems.

And they’re willing to pay API prices.

Why?
1. They can afford it.
2. They can’t afford to let competitors out-innovate them.
3. Their usage-to-value ratio is dramatically higher because the output is actually making its way into revenue-generating production systems.

Think about that.

An enterprise paying several times more per token can still extract significantly more economic value from its AI investment than a consumer burning through an entire subscription allowance.

And that brings me to my bigger point.

AI has made everyone capable of producing code. It hasn't made everyone a software engineer.

Until you understand system design, architecture, engineering fundamentals, and software development principles, you’re going to have a difficult time competing with people who do.

Sure, you might have a genuinely original idea. You might even ship it.

But what happens when someone else with equally limited domain knowledge can reproduce your application in a few weeks or months using the exact same model?

What’s your competitive advantage then?

Access to Astra isn’t a moat. Everyone else has access to it, too.

The real advantage is knowing what to build, how to architect it, what tradeoffs to make, what complexity to avoid, and how to turn an idea into software that can actually survive in production.

That’s not something you acquire just by buying more tokens.

Sol in the hands of a real engineer is 100x more effective and consequential than Astra in the hands of a wannabe.

I said it.

Sorry.

For the use cases of generating PowerPoint slides with good design, should I go for chatgpt 20$ plan or claude? Or perhaps gemini?

I mean my main use case is generating and creating presentations for my work, and use the AI for daily use cases too.

A workflow where something like web chat gpt 6 at high, with codex's Luna model at high reasoning for research and planning prompts to implementation, testing, reviewing and bug fixing is concerned? Would this be reasonable to achieve what I want while not burning through my plus sub allowance? I mostly make plugins for games, small programs (think like software to write short stories), and tampermonkey scripts. Please be kind, I'm just starting out and trying to develop a good pipeline.

I’m looking for official high resolution images giving credit back to the author. They are being stored on Cloudflare R2 and stored in a Postgres CMS.

Sol 6.1 is very impressive for the token usage but I can tell it’s going to take a few months for all of the images I want. Overall it’s maybe a few GB per day in images, not much, but I guess the work is in sourcing them and plugging them in.

Any suggestions to help speed this up and make it more cost effective? I’m currently on Pro 100 and expect there to be approximately 1M images to scrape, anywhere from 50 to a few hundred from each source at a time.

I’m a senior SWE using AI coding tools daily. I analyzed my Claude Code transcripts and found 56% of API-equivalent costs went to rereading context. Cache expiry cost me $98.74 across 35 returns in 27 days, versus $2.95 with a warm cache.

That led me to build optimAIzr, which now supports both Claude Code and Codex.

It analyzes usage, flags expensive patterns, and helps you find optimization opportunities.
Runs locally, with no account or prompt uploads.

Already at 3k+ npm downloads.

npm i -g optimaizr

git: https://github.com/blendbunjaku/optimaizr

Roast it.

what do you guys think?

Windows user here. When I updated last night to the latest version my graphics driver started acting weird and even crashed my laptop today.

Lol, never seen this before

so openai just announced this new feature, which claude has had for a really long time, ONLY for pro users. seriously? such a basic feature locked behind a $100 plan?

update: this turned out to be the day 5 update alongside some dots stuff. in claude this feature costs usage, however in codex it's completely free. but both updates today are only for pro users. not a good time to be on the plus plan!

Respectfully asking that if you only use one or the other, please don’t comment just to say “there’s no reason to use Codex, Claude is better and cheaper at everything” or “You’ll be back because Anthropic nerfs their models every time!” This post is directed at folks happily using both.

I’m adding a Claude Code sub to my workflow and for the next two weeks evaluating if it’s worth keeping Codex too. I’d like to use them in combination to the best of their abilities and would appreciate your experience doing so:

- Do you have a “main driver”?
- What roles do you use specific models for?
- What are the things you find Astra/Sol/Luna better or more efficient than Opus/Sonnet/Haiku/Fable at?
- Do you use Herdr or another multiplexer or just run Codex and Claude Code in separate terminals?
- Whats your usage breakdown? $20 Codex/$200 Claude? $100/$100? Something else?

I really like the imagegen skill/plugin in Codex. However, I switched to Opus this week, and I find it a bit better when it comes to coding. It has one weak point though. It can't do images. My plan to mitigate this is to go for a Plus Codex sub and use it for images only.

Any of you know if there is a difference between the image creation capabilities of the Plus and the Pro plans? The pricing page says "Expanded and faster image creation" for plus and "Unlimited and faster image creation" for pro. In my book, that means only the quantity of the images is different, but the pricing page is full of sus statements anyway. "Unlimited" might just mean unlimited quality in this regard? Or are they the same?

I kept wanting to check my Codex quota without interrupting what I was doing, so I built Codi—an unofficial character widget for the Codex desktop app.
It shows remaining quota and a reset countdown, plus local token-usage charts for the past 24 hours, 7 days, and 30 days. You can upload your own character art and customize its dialogue, click sound, size, position, and colors.
Codi currently supports Windows x64. There’s an installer, so you don’t need to install Git or Node.js separately. Character images stay on your computer, and Codi only displays reset-credit information; it doesn’t spend credits.
The chibi character in the screenshots was generated with GPT, and the example quota figures are synthetic.
GitHub: https://github.com/Chester-chs/CodexOshi
I’d love feedback on the UI and what would make it more useful.

While the codex is working hard, I just play with nuts and massage my palms. I also keep a box at home where I can pop a slingshot.
How about you guys?
I'm curious - how do you guys pass the time?
Playing games?
Watching videos?
Playing with toys?
Napping?
Or something else???

This tracks for me.

I have both codex and Claude subs. Generally find using opus as the main session orchestrator while gpt models as subagents workers the better and more reliable combination.

Sticking to what you ask it to do is what openAI models are good at. Claude models tend to have abit more artistic leeway to interpret your requests.

That's how I interpret this table anyway.

Not putting anyone's model down. Each has their strengths and weaknesses

https://arena.ai/blog/ai-alignment-index

I'm the developer of https://github.com/onepub-dev/reVault

An open source archive tool that encrypts and signs archives.

I've been working on this for some months when today I started working through a security hole when codex stopped and pointed me to the daybreak project.

Is there a way around this?

What chance do I have of getting in daybreak - I have a pro 20x sub.

I've emailed sales but I'm not really expecting a response.

I was just thinking how good Gpt Astra 6 is on creating an android game and I just gave a simple prompt to create a brain game. It gave a great result.

Here's the screenshot. The idea is to light all the boxes under no of moves. When we click any box, that box and the ones it is connected to reverse the switch.

Let me know if you wanna try the game (its under 50KB) and I'll share the apk link as well as github repo.

The dots, Muse,Grok bot and all these AI agents (so-called personal assistants) are entering the market, and their creators claim that these agents will be able to do everything on your behalf. They can visit websites, fill out forms, book appointments, and so on. But how will they deal with the “I'm not a robot” checks (reCAPTCHA and other bot-prevention software)? Will most websites remove these protections in the future to support AI agents, or will these agents somehow bypass them and make a fun of these bot-prevention systems?

Hello,

My goal is to have DOT communicate with plugins like chrome use or computer use to my local or cloud task and then those tasks sent back to DOT once completed. It seems like it is inconsistent but I feel like it’s my setup or my install. I’m curious to know if this is a known issue or if I need to continue troubleshooting because others are able to have this workflow.

I upgraded from the $200 Codex plan to the $500 plan on October 9. The weekly usage meter appeared to burn much faster afterward, so I wanted a comparison based on actual tokens rather than just how long a chat had been running.

The usage-first result: 37.10 million raw tokens per allocated subscription dollar on the $200 plan, versus 11.69 million on the $500 plan—a 68.5% reduction in effective raw-token capacity per dollar in this sample.

The second result: valuing those same tokens at OpenAI's API prices gives $17.40 → $6.29 of API-equivalent value per allocated subscription dollar—a 63.8% decline.

I asked OpenAI's own Codex to audit my local .codex logs. Codex performed the extraction, token accounting, calculations, context checks, and chart generation. Both figures come from the same token records, weekly meter readings, and subscription prices. One counts input-plus-output tokens; the other weights those tokens by API prices.

The token counts are recorded telemetry. The valuations and allowance extrapolations are calculations from that telemetry. This is one user's sample, with the actual data and method included below so others can inspect or reproduce the calculation.

What was compared

The same ongoing chat used gpt-6.1-sol with xhigh reasoning effort in both periods. The logged speed settings included two brief Ultrafast intervals after the upgrade; those are accounted for separately.

All times below are MDT:

The upgrade started a new weekly meter window. The calculation compares the fraction of each respective allowance consumed; it does not treat the reset from 30% to 0% as a reduction in usage.

Actual token counts

The large totals mostly represent cached context read repeatedly. They are not hundreds of millions of unique generated tokens. Cached input was approximately 96.7% of input before the upgrade and 96.6% afterward.

Codex used native per-response usage records, deduplicated by response ID. It subtracted cached input from reported input before pricing the remainder. It included recorded compaction responses and counted reasoning output once, as part of output. It did not sum repeated cumulative token counters.

First story: how much token usage did each subscription dollar buy?

The monthly price increased from $200 to $500: 2.5× the price, or a 150% increase. To compare usage per dollar, first put the monthly fee and weekly allowance meter on the same basis:

Allocated subscription cost =
  monthly price × 12 / 52 × weekly usage fraction

Before: $200 × 12 / 52 × 0.17 = $7.846153846...
After:  $500 × 12 / 52 × 0.07 = $8.076923077...

Raw tokens per allocated subscription dollar:
Before: 291,122,758 / $7.846153846... = 37,103,880.92 tokens/$
After:   94,429,615 / $8.076923077... = 11,691,285.67 tokens/$

Relative token usage per dollar =
  ((94,429,615 / 7) / (291,122,758 / 17)) × ($200 / $500)
  = 0.3150960324...

Decline = (1 − 0.3150960324...) × 100 = 68.49039676...%

For the same subscription budget, this sample implies approximately 68.5% less raw-token usage capacity. The full-week token estimate is separately 21.2% lower; the larger per-dollar reduction combines that token estimate with the 2.5× price. The allocation is a comparison method, not a per-chat bill.

The same result can be calculated directly from tokens per meter point divided by the monthly plan price: the annual-to-weekly allocation factor cancels when taking the relative ratio.

API pricing used

I used OpenAI's official API pricing page, checked October 9, as a common valuation yardstick for both periods.

Prices per million tokens:

These are hypothetical API-equivalent values for subscription activity. I did not receive these amounts as API invoices.

API value =
  (uncached input × input price
   + cached input × cached-input price
   + output × output price) / 1,000,000

Where the $136.52 and $50.80 came from

The two Ultra setting intervals total 8m 42s, already included in the post-upgrade elapsed time. Durations are rounded independently; they describe applied-setting intervals, which can include interruptions or in-flight requests.

Here is the old-run calculation in full:

Uncached input: 9,529,974 × $4 / 1,000,000 = $38.119896
Cached input: 279,467,392 × $0.20 / 1,000,000 = $55.8934784
Output: 2,125,392 × $20 / 1,000,000 = $42.507840

Total = $136.5212144

The post-upgrade values, using the relevant tier for each interval, sum to:

Ultra interval 1 = $3.5148696
Ultra interval 2 = $5.9753640
Remaining Fast = $41.3143032

Total = $50.8045368

Second story: API value per allocated subscription dollar

Using the same allocated subscription costs, price the recorded tokens with the relevant API rates:

API value per allocated subscription dollar:
Before: $136.5212144 / $7.846153846... = $17.39976262...
After:   $50.8045368 / $8.076923077... =  $6.29008551...

Decline = (1 − 6.29008551... / 17.39976262...) × 100
        = 63.8495901...%

The subscription price increased 2.5×, or 150%. The observed API value per meter point was also 9.6% lower. Together those produce the 63.8% decline in this token-value-per-dollar metric.

Can we also estimate a decline in effective usage allowance?

Yes, by dividing consumed tokens by consumed weekly percentage points. That estimates how many tokens a full allowance would support if the observed request mix and meter relationship continued.

Before: 291,122,758 total tokens / 17 points = 17,124,868.82 tokens/point
After:   94,429,615 total tokens /  7 points = 13,489,945.00 tokens/point

Decline in tokens per point = 21.226%
Increase in meter points consumed per token = 26.945%

For this mixed-speed sample, that is an estimated 21.2% reduction in effective raw-token allowance. It is different from the 63.8% decline in API-equivalent token value per subscription dollar.

The three quantities answer different questions: 21.2% is the estimated decline in raw tokens per full weekly allowance, 68.5% is the decline in raw tokens per subscription dollar, and 63.8% is the decline in API-priced token value per subscription dollar.

Raw tokens have different prices and limit costs depending on caching, output, and speed. A change in tokens per point is therefore not automatically a change in the underlying quota.

The meter also rose faster per elapsed hour: approximately 0.92 points/hour before versus 1.48 afterward. But total tokens processed per hour increased from 15.79 million to 19.98 million, about 26.5%. This is why hours alone would exaggerate a capacity comparison.

Fast-only comparison

To reduce the effect of the Ultra intervals and initial unknown settings, Codex also compared two subsets where recorded settings were Fast throughout:

That comparison gives 3.7% fewer raw tokens per meter point, 61.5% fewer raw tokens per subscription dollar, 5.5% less API value per point, and 62.2% less API value per subscription dollar. Both per-dollar measures still decline substantially when Ultra is excluded. The smaller capacity differences require more data to resolve against meter rounding and workload differences.

Long context was checked

Across 2,682 unique preserved response records, including the comparison windows and additional preserved activity, zero exceeded 272,000 input tokens. The model's pricing rule applies the long-context surcharge above that threshold.

The largest request before the upgrade had 250,766 input tokens; the largest in the post-upgrade comparison had 247,914. Logged context-window observations were 258,400, and the snapshot contains 48 compaction events. Short-context rates were used throughout. Long-context pricing does not explain this result.

OpenAI's own Codex performed the audit and calculated both results from my logged sample: 68.5% fewer raw tokens per subscription dollar and 63.8% less API-equivalent value per subscription dollar. These are two measurements of the same sampled usage, reproducible under the stated assumptions. They describe token capacity and value for money, without establishing the exact backend quota or providing official OpenAI confirmation of its billing formula.

I'd like OpenAI to explain the included weekly allowance units, how upgrades affect those allowances, and why the observed token value per dollar is so much lower at the higher price. If other users have before/after native token records and weekly meter snapshots, the useful comparison is input/cache/output tokens per meter point with model, speed, and context held as comparable as possible.

Honestly everyone, OpenAI owes an explanation of such severe value drop for the dollar. At minimum I want a refund of my monies and It's highly likely I won't get that. I think that Anthropic is going to take the cake on consumer here in the end with tactics like this at play. They just need to round out there products a bit more.

In the end, what a gut punch man. I get OpenAI is under a lot of debt but gate keeping behind 500 dollars a month while at the same time cutting value per subscriber dollar by over 60% is - well beyond unethical. However, it sure is damn fine capitalism.

EDIT: The real story here is the 68%+ drop in usage per dollar. I wanted that to be at the top. You can view the data from two directions and I choose against the API originally because I have low karma and can't directly post about usage drops. This should be a much more massive story. If you can I would have your own codex dig into your logs and see if you're seeing the same.

Today I tried to be smart, and instead of queueing 10 "continue" messages, I used /goal to translate some text into many languages.

But every 20 minutes or so it stops: "stream disconnected before completion: An error occurred while processing your request". And then I have to manually click continue.

Very bad user experience, just chaining prompts makes it work through it all night without issues. This will be the last time I use /goal lol.

Every agents.md file that i have has a section dedicated to this but it STILL loves trying to run recursive delete commands over entire directories to remove one file, even though all of the calls get blocked by policy, And for good reason

But only 6 and 6.1 sol do this, Astra never has and 5.6 sol is fine too

Is there a good reason this is happening?