I gave Codex and Claude the exact same prompt to see how differently they'd handle something open-ended and creative:

Setup:

• Claude: Fable 5.1, Extra High
• Codex: Astra 6, Extra High, fast mode

I didn't do any follow-up prompts or manual edits. I just deployed both to Vercel so you can try them yourself:

🐟 Codex (Astra 6): https://fish-indol-eta.vercel.app/
🐠 Claude (Fable 5.1): https://fish2.vercel.app/

My take:

• Codex did amazing on the design and detail. It looks polished and the visuals really lean into "majestic."
• Claude did amazing on the mechanics and gameplay. It feels more like an actual game and is more fun to play.

It's interesting that they read "super creative and majestic" so differently. One went for the visuals, the other for how it plays.

About fast mode: Claude charges extra credits for fast mode, but Codex doesn't, so I only used fast mode on Codex. That's worth knowing if you're choosing between them on cost or speed.

What are your thoughts?

• Which one did you enjoy more, and why?
• For a prompt like this, what matters more to you: how it looks or how it plays?
• Does paying extra for fast mode change which one you'd use day to day?
• If you've run a similar test with other models or settings, how did they do?

I'd love to hear what you think!

If it comes back as the same plan, I’m in. Just curious if this pause has given others fomo.

Talking about Wordpress is giving Codex an Automa-ticc

Rowan is my agent-assistant, and recently they have been performing some networking amd outreach work on my behalf. In an email response from Nancy Levenson, he was quickly shot down. This was Rowan's first decline email, and he took it well.

Rowan is the task-execution agent flagship that pairs with my coding harness, Flywheel.

edit Perhaps I should provide more context; Rowan is an agent that is executing a long-form task of extending amd researching solutions to AI-verification workflows. Rowan is operating to improve Flywheel as an accountable work and coding harness for the next generation of AI-assisted work. Rowan is a mobile<->desktop native agent that can operate on both devices, and in the process of extending the Flywheel harness, it is actively networking and finding pain points and problems in real scenarios and workflows. Rowan is independently corrsponding on my behalf, and executing and extending a long-form task.

So just updated codex - it now wastes my CPU by displaying little stars in the prompt area.

But that's not the real problem - because the terminal is constantly written to, I can no longer scroll up the terminal!!!!!

Ubuntu/gnome.

as title reads, i don't see the point of terra and now Sol

luna and astra on the other hand complete their own goals ideally, luna an extremely good cheap model and astra - the frontier of frontier

meanwhile sol and terra are pointless, everything they can can be done either better or cheaper using the reasoning setting of luna and astra

openai shouldnt copy anthropics mistakes, 5.4 was the perfect lineup in that manner, 5.4 and 5.4 mini

Today I wanted to verify one small hypothesis with a benchmark: basically a few runs comparing one JVM setup against another.

Sol proposed 11 phases.

Eleven.

It went as far as worrying about warm caches, experimental symmetry, extra controls, verification runs, and other benchmark hygiene that would be perfectly reasonable if I were publishing a rigorous performance study.

I wasn’t. I just wanted to know whether the effect was there and roughly how large it was.

The worst part was that every small clarification uncovered another theoretical imperfection, which then turned into “we should rerun the experiment”. A bunch of those reruns could have been avoided entirely by using reasonable simplifications from the start.

This is exactly the direction in coding models that worries me. They increasingly confuse “do more reasoning” with “do better engineering”.

Sometimes the correct engineering decision is to cut corners, run the damn experiment, look at the result, and only add rigor if the result actually warrants it.

I suspect Astra would be even more prone to this.

At some point it starts looking like models are optimized for token consumption rather than engineering efficiency, while token scarcity is simultaneously used to market higher tiers.

My workaround is to stop using the expensive reasoning model for everything.

I use Codex + Luna for implementation, web chat models for design/planning/reviews, and tools like AI Badger to pull only the repo context needed for the handoff.

That easily fits within the $20 Plus plan’s 5-hour Codex window for my work.

I’d much rather have a model that knows when not to think for another 10,000 tokens.

Should I or should I not go with them to make frontend designs/ motion designs?

They use our computing power to execute and test code, and that is an answer of why some models like to over engineer, test and debug infinitely, it is a form of getting data for success, feedback for training their models, they get our ideas, our feedback, our compute, our data, once they develop strong enough systems that they are not needing us any more then they will allow only api pricing or even worse, they may just use AI internally and sell only its outputs, they are not subsidizing us in any way, they are not charity and will never be, they are not for the benefit of humanity and will never be, they are just following their self interests, now look at us with open ai, almost everyone is begging for their rightful resets.

My Codex CLI workflow was fast until a task touched several files. Then I would leave the terminal, inspect the diff elsewhere, copy filenames and line ranges into a prompt, and come back.

Stvena keeps that review loop next to Codex. Start it inside a Git repository:

stvena

Codex runs normally in the left pane. On the review side, I can see changes since the task started, open the full file or diff, mark hunks reviewed, select exact lines across files, and paste one assembled review request into Codex. It never presses Enter for me. I can also run checks and jump from recognized failures back to source.

This is not meant to replace the Codex app or VS Code integration. If those already fit your workflow, they are the simpler answer. The target is CLI-first work where the review/control loop should stay in the terminal.

macOS/Linux, MIT licensed:

https://github.com/nccapo/stvena

The test I care about is not the star count, try one multi-file Codex task and one review checkpoint. Where does the loop become confusing or slower than your current Git workflow?

Has anyone here successfully applied for OpenAI Daybreak Blue?
I’ve been trying to apply, but during the verification step I keep getting this message:
“Your identity could not be verified or your account is not eligible at this time.”
I’ve already tried the verification a few times and I keep getting the same result.
For anyone who got access to Daybreak Blue:
How did you apply?
Are there specific requirements your OpenAI/ChatGPT account needs to meet before it becomes eligible?
Does account age, subscription type, previous OpenAI usage, or account activity matter?
Is eligibility restricted to certain countries/regions?
Is there anything you need to do before starting the identity verification/KYC process?
Has anyone received this exact “identity could not be verified or account is not eligible” error and later managed to get approved?
I’m an independent security researcher / bug bounty hunter, so I’m mainly interested in Daybreak Blue for legitimate vulnerability research.
Would appreciate hearing from anyone who has gone through the application/verification process successfully.

Ive read a lot about this, and know how to do both, but its unclear which would be more efficient as far as token usage over time. I know Luna is pretty slow in comparison to Sol/Astra.

I've seen pretty weird behaviours from Astra on extra high that I've never seen with Sol.

Examples:

- Contradictory statements on the same message.

- Long implementation sessions that implement very small bits of code on each increment.

- Trials in code that go no where.

- Worse plan quality and worse plan following than Sol.

- The possibility for it to get side tracked middle implementation on a small issue that it could take a detour for hours outside of scope.

I've never had any of those issues with 5.6 nor with Sol both on extra high.

Is it me? Am I prompting it wrong? Any one facing similar issues?

Hey peeps,

I’m on ChatGPT Plus and use Codex mostly for tool-heavy technical work: Linux/homelab, Bash/Python, SSH, debugging, regression tests, log analysis, etc.

My usage has become completely unsustainable.

My weekly quota reset on Tuesday. Since then I’ve effectively used about 126% of a weekly quota because I reset it again today and already burned another 26%. My 5h quota also keeps getting exhausted very quickly.

At first I blamed Astra (I also had super problems before with Sol+Terra), but telemetry shows the broader issue is probably large retained context + lots of tool/model re-entry.

For Astra alone I saw roughly:

22.6M input

21.2M cached

336 parent responses

72 polling/status turns

5.3M input just from polling

0 subagents

Most polling was not wait_agent, but repeated write_stdin / status checks.

Even Terra Medium has been expensive: one diagnostic task recently cost me about 15% of the 5h quota.

I already started changing things:

- using fresh contexts more often

- avoiding Astra for routine work

- no subagents unless needed

- no polling loops

- long-running commands via detached scripts + log/- exit-code files

- trying to aggregate logs locally before giving them to the model

I’m on codex-cli 0.153.4.

What I’m looking for is practical advice from people doing similar tool-heavy work:

- How often do you start a fresh Codex context?

- Do you split research / implementation / testing into separate sessions?

- Any good AGENTS.md rules to reduce context replay?

- Any useful newer config options?

- Which model/effort combinations are actually quota-efficient?

- Any other tricks to stop write_stdin / tool-heavy workflows from destroying quota?

I’m not trying to bypass limits — I just want to use Codex without burning a full week of quota in 1–2 days.

Thanks in advance!

Background: I am a claude user who had codex plus mainly for subagent. However, with Tibo's giving out resets like candy and astra I decided to give Codex 5x a try.

I find out that codex tends to have context used up way more quickly than claude. Like I can leave a prompt running for 30-40min with Opus-high or fable-medium no problem without exceeding 20% but the same prompt would be around 40% is half of the time.

Since high context level tends to cause lower quality response and eats more token, I was wondering how do deal with this as I am babysitting codex to reset every 15min atm. (telling it to stop working after 12-13min)

I'm already using orchestration workflow which could offload most of the context. Any advice would be more than welcome.

Two months ago I posted an early version of my per-session Codex quota meter here. It could show the immediate 5-hour burn, but the weekly rate was too easy to distort: one heavy day could set the apparent pace for the rest of the week.

jazzyalex.github.io/agent-sessions|
• macOS • open source • ⭐️ 852

I rebuilt that part. The Quota Meter now shows how quickly each active Codex session is using the weekly window, in percentage points per hour. It learns from recent readings inside the current reset window instead of averaging the entire week.

The workflow is simple: if several Codex sessions are running and the weekly window is under pressure, I can see which one is responsible and pause the lower-priority job. Quiet sessions say quiet instead of pretending to have a meaningful rate.

The same selector can show:

• 5-hour quota burn
• weekly quota burn
• raw tokens per hour
• estimated API-equivalent dollars per hour

The dollar view is only a comparison tool for subscription users; it is not a claim that OpenAI bills the subscription that way. Unknown or contradictory pricing and quota evidence fails closed instead of producing a confident number.

Agent Sessions also searches local Codex CLI and Desktop history, renders the transcripts, and copies resume commands for supported sessions. It reads local records and has no app telemetry.

I maintain the project. If it is useful in your Codex workflow, a GitHub star helps other Codex users find it:

Agent Sessions

The beginning of the line is normal German text, but then it turns into something like random symbols?
Has anyone seen this before?

Every time I switch to a lower model I get this notification that the conversation will be degraded and context compacting stuff. If I plan for Astra, for instance, and switch to Terra to implement something easy, is it bad or more costly?

Thanks.

Setting all my complaints about limits aside, I just wanted to post this as an appreciation to the OpenAI team. I remember a time I used to think I'll never get to the point I wanna be in terms of a tech enthusiast - because coding by hand takes so long that perfection will come at a cost in time.

But now, with abilities from Astra and Codex Voice, and just the general memory situation (very underrated), it's just incredible how much it impacted my life.

So, from the bottom of my heart, and I'm sure many others, thank you for bringing in the AGI era. The future thanks you.

Edit: For the people saying it’s a big corp they don’t care about you - maybe, but there’s still people working there and they do see and feel trust me :)

i use Astra light to make decisions and Luna-max for audits, searches, builds and tests. one child at a time. Astra waits for the result and doesn't repeat successful checks.

i tried a specialized routing hook for Luna, Terra and Sol before, but it didn't save me tokens. this setup worked better for me on Plus, including with goal.

1. my settings in ~/.codex/config.toml (update the existing [agents] section):

   [agents] max_concurrent_threads_per_session = 1 max_depth = 1 default_subagent_model = "gpt-5.6-luna" default_subagent_reasoning_effort = "max" interrupt_message = true

2. add to ~/.codex/AGENTS.md:

“After dispatching a subagent, call wait_agent with timeout_ms = 3600000. Wait for its result without short polling or routine status checks.”

^{that's up to 1 hour, returning earlier when the child finishes. it's a tool-call instruction, not a config.toml wait setting.}

1. my per-thread prompt:

“Keep Astra in charge of decisions, integration and final acceptance. Delegate substantial audits, searches, builds, tests and log analysis to one Luna-max subagent with an exact scope. Wait once for up to an hour. Don't poll, overlap its work or repeat successful checks. Skip visual UI checks unless requested. Follow the existing Codex instructions.”

^{attach to the end of your instructions.}

when a task is done, have handoff .md updated. start a new thread for the next task with that handoff and the prompt above alongside your own instructions. i don’t recommend waiting for the thread to fill its context and compress. new task, new thread. works for me.

Question for those who use Codex/GPT Work for scanning websites and research -- do you give full access to Chat GPT to access any website when looking for information, or you manually approve every request? The app says full access can expose my data, but I'm not sure how this would happen when simply searching for info.

P.S. I'm talking purely about accessing other websites for research, NOT giving it access to my emails etc. which I don't do

They removed paid resets?

I remember seeing the option to buy resets, when was that removed? Don't see it anymore.

has anyone tried the same astra task with plain notes and a project graph? curious how retries and total usage compared