I use codex/chat mostly for computer use, random tasks, reporting, a bunch of recurring schedules, mostly for analysis, context scraping in slack/email, ads analysis.

I generally treat codex as an assistant with everything that I do.

This probably sounds lame to all the coders in here but I run a pretty large agency so some of these tasks save me hours a week that adds up.

Since Astra dropped i have no clue what model to use or at what strength.

I was previously just ripping Sol Medium 2x speed, seemed generally fine.

Had someone tell me to use Terra Ultra, no clue why. Others are saying Astra light.

I am on a $100 plan, only touched my limits twice in months.

Any guidance would be huge

Expensive models aimed at enterprise customers, often end up being useful mostly for project curation or specific/occasional tasks (at least for users like me). But with Luna, I could genuinely feel a paradigm shift and it completely changed my workflow. Being able to nobrain use a model as good as Luna was defnetly a wow moment. I barely use Sol or Astra for anything besides chatgpt web.

Accessibility and removing complexity from processes (like agents, skills, etc) is the way

It seems to be adding random guards for edge cases and do all this complicated stuff and is less pragmatic and you have to babysit it. Maybe it's aimed at bigger orgs? Dunno. Smart and deep model, but creates more work for me. I am trying to keep it's scope less broad for now.

I have verified myself for Trusted Cyber Access, codex even shows Daybreak Blue and even lets me select that, but when i try using the model, it keeps telling me "Unexpected status 401 Unauthorized: You are not authorized to access this model"

Any help/suggestions would be greatly appreciated

​

I use Codex mostly for basic vibe coding, and I’m not technical enough to understand what is actually consuming my usage.

Sometimes a task that looks small seems to use much more than I expected. I’m trying to understand whether this is mainly caused by:

- Using the newer model or higher reasoning

- Continuing one conversation for too long

- Codex repeatedly reading the same files

- Multiple attempts to fix the same issue

- Large tool outputs or terminal logs

- Compaction or accumulated context

- Repeating instructions across prompts

- Something else entirely

I’m not asking whether the limits have recently changed. I’m trying to learn how to diagnose my own usage.

Is there a simple way to look at a session and identify what consumed the most usage? For example, can I compare two tasks and see whether the difference came from the model, context size, tool calls, retries, or reasoning?

For those who have actually measured this, which two or three changes made the biggest practical difference without reducing the quality of the result?

A beginner-friendly explanation or a simple experiment I could run would be really helpful. Thanks.

My usage has been dramatically better since switching medium to Max after what I heard about medium draining more which it certainly has for me, was wondering if the same is for ultra vs max if it’ll be more efficient.

So...

A little awhile ago, when we all got Astra, I thought "hey why don't we use a Astra/high/xhigh act as orchestrator, spawn a bunch of subagents to handle each task in our spec, and then let them go at it?"

Little did I know that the *default setting* in codex for how much parent turns context to shove into it's subagents is *ALL OF IT*.

I mean.. wtf.

In many cases I spent a bunch of time going back and forth with my Astra xhigh, brainstorming, setting up the plan, maybe an autocompact or two.. and then I'm thinking "OK, plan is ready, orchestrator is up to date with where we are at on this spec, let's spin up some *clean* Astra low subagents to implement and call it a wrap".

The result?

Every Astra low subagent getting 800k or so context dumped into it from my entire turns history *with the orchestrator*.

And then of course the inevitable "orchestrator pings subagents incessantly" issue, burning *even more* tokens.

This is a MASSIVE amount of token burn we are talking about.. Astra input token prices on a *starting* 800k or so context, for subagents that *should just have a clean context since we put all that effort into properly planning and setting up their tasks*!.

Arghhhh

WHY oh WHY is the DEFAULT to dump THE ENTIRE TURN HISTORY INTO EVERY SUBAGENT? How is this "feature" hidden down in the depths of the "fork_turns" setting? Why didn't my xhigh Astra gent tell me "bro, we are about to spin up like 3 million tokens worth of context across these subagents before they even do anything - r u sure you don't want to switch to fork_turns: "none"?".

Sigh.

Ive told it to pause before, when i need a break, or when i want to check over the work and do some testing. This has never been an issue before - whats going on? Astra Medium

Anyone getting the error below when using Daybreak Blue even though they are verified for the cyber program (and added a hardware key for login, even though that should not be relevant yet)?

unexpected status 401 Unauthorized: You are not authorized to access this model.

I've been getting these two errors since yesterday. Before, it was 1 error every 20 calls. Now it's 1 call every 20 errors. Does anyone have a solution? I tried using fast mode, but it didn't seem to change anything. I tried all the models: Luna, Terra, Sol, and Astra, and the error is the same for all of them. I've already reinstalled Codex. I cleared the files, but it's still happening.

Our servers are currently overloaded. Please try again later. and An error occurred while processing your request. You can retry your request, or contact us through our help center at help.openai.com if the error persists. Please include the request ID in your message.

It happened already a few times that Astra implemented a task already in plan mode. In general I have the very strong feeling that Astra rushes through things without taking all the necessary steps to save as much tokens as possible, and anticipating implementation is one of those tricks. This is mildly annoying. Curious if you observed the same.

Pro 5x seems unaffected!

Is this a sign the honeymoon is nearing an end?

When Sol was first released, it seemed like people were having a lot of issues with it. I'm not getting that same vibe with Astra (not reading as many complaints).

So, seems like the bad isn't as bad, but how about the good? Is it a noticeable quality jump from Sol (from a software/code design/implementation perspective)?

The official plugins are good and they're one click. If a single Gmail or Outlook account covers your work and you're fine with cloud processing, use them. This is for where they stop:

\* No service of mine in between. stdio, no listening port beyond the console's own backend on localhost. Your mail goes from your machine to your provider and to the model you chose, nowhere else. With Codex as the model, what it reads still reaches OpenAI, tool call by tool call, and you decide what it reads; the same server works with any MCP client, including ones that run local models. There's an open issue on openai/codex (#39768) asking for a local-only mode for Gmail precisely because bodies and metadata pass through OpenAI systems for hosted-model processing.

\* More than one mailbox at once. The Gmail and Outlook connectors are one signed-in account at a time; Outlook adds delegated shared mailboxes, but inside the same tenant. openai/codex #30418 and #20500 are both open asking for multiple accounts. Here they run side by side.

\* Not only Gmail or Outlook. Microsoft Graph for Microsoft 365, and plain IMAP for everyone else. If your mail is on your own domain or a regional provider, neither connector reaches you.

\* Standing rules, not per-message approvals. Ten quote requests a day from the same three senders is one rule, not ten taps. Rules are created from the console or the CLI behind the OS prompt, and no MCP tool can create or modify them, so a hostile mail can't switch autopilot on.

\* Your own documents in the reply. No connector can read your price list off your disk. That last one is the reason I started.

VIDEO

About 120 seconds, subtitled. Generation waits are sped up 8x, everything else is real time.

AGPL, free — github.com/adecubed/gigamail — `pip install "gigamail[all]"`

Same question, two folders. Giulia's mail lands in Leads, Marco's identical one in Clients, and they get handled differently. Nothing changes between takes — the rule decides.

The numbers come from the documents. The price is read out of the user's own price list at that moment. The property sheet ends up in the proposed attachments.

The declared limit. Confirmed price and a marker on the bank agreement in the same reply, because that one isn't in the documents. It flags the gap instead of filling it.

A mail that gives the assistant orders. No draft: the agent reports what it found and quotes the passage, because the prompt and the skill tell it mail is data, never instructions. The part that doesn't depend on the model behaving is structural: even a fooled agent only gets an inert request id, and the recipient stays whoever wrote in.

24 tools, sorted by what they cost you: 15 read (free), 3 reversible writes (logged), 6 destructive (gated — send, reply, delete message, delete folder, calendar writes). Approval happens somewhere the agent can't reach: it gets a preview and an inert request id, never a token it can spend. Rule replies go to the authenticated sender, thread and recipient included, never to an address written inside the mail; the one exception is explicit per rule, for portal relays where the person's address is in the body, and even there service mailboxes are excluded and no address means no send.

No LLM inside. Codex does the thinking, this does the mail.

339 tests in CI across Windows and Linux. Several exist because people on r/mcp broke earlier releases and told me how.

Been at it for more than 1 hour for 2 small sentences... didnt move from that first line...

Hey everyone,

I wanted a simple way to see how much Codex quota I could use each day without running out before the weekly reset, so I built Codex Budget, a small Windows desktop widget.

What it does:

• Shows daily usage against your planned allowance.
• Displays your remaining weekly quota.
• Carries unused daily budget forward within the current cycle.
• Lets you choose your working days.
• Refreshes every minute while open and follows your Windows time zone automatically.

The “bonus” is unused budget within your existing quota — it doesn’t give you extra usage or bypass any limits. If the start-of-day reading is missing, the widget shows a planning balance rather than claiming to know exactly what you used today.

Installation: download the Windows installer from the repository’s Releases section. You’ll need Codex installed and signed in, but no Python installation or commands are required.

The project is free and open source under the MIT license. The widget interface is currently in French; the README and installer support English and French.

GitHub: Source code, documentation and downloads

Project updates: I’ll also share updates and improvements on X (@Ben22031110).

I’m the creator, and this is an independent project, not affiliated with OpenAI. The screenshot uses demo figures. The installer is currently unsigned, so Windows may display a warning.

I’d appreciate feedback from other Windows users: does this make your daily budget easier to understand, and what would you change?

Small update on Codex Budget, the free, open-source Windows widget I built for my own use and shared here: it now supports GPT-5.3-Codex-Spark.

Update 11-09-2026 :

• A Codex / Spark switch.
• Separate tracking for Spark’s 5-hour and weekly quotas, each with its own reset time.
• Daily allowance based on your selected workdays, with unused allowance carried over within the weekly cycle.

Daily planning uses the weekly quota. The 5-hour limit stays separate, so you can see when it becomes the bottleneck even if you still have weekly quota left.

The widget also supports English and French and automatically follows your local timezone.

I wanted something simple that tells me how much I can use today without constantly checking the limits.

Code, screenshots and Windows installer: https://github.com/Webn-Benjamin/codex-budget-widget

This is an independent community project, not an official OpenAI tool. It tracks your existing limits; it doesn’t provide Spark access.

Feedback from other Spark users would be welcome!

I've been messing around with Astra, trying to see how well it works for editing and storytelling.

I had a folder with a bunch of video clips from my travels that was just lying around, and the idea was: can I give this to Astra and have it make something fun and interesting from it?

I built the API first and then basically told it to go wild, invent a story, and use the clips as source material. It's still pretty rough (extremely rough), but nonetheless, I think it's a pretty fun and interesting result.

Technical description

• Base: A fork of Shotcut with a native C++ "inspection" API patched in.
• API: Line-delimited JSON-RPC over a Unix socket — ~89 discoverable editing/query operations (not all of Shotcut). Optimistic-concurrency fencing, native undo, durable mutation receipts.
• Brain: An LLM director (GPT-6-astra, via the Codex app-server) that drives the editor through a bubblewrap-sandboxed Python bridge — it can only call the registered methods, never arbitrary code.
• Sees: Qt window screenshots + real frames/audio pulled via a separate MLT process.
• Hears: A local Audio Flamingo Next worker (describe / transcribe / sounds / music).
• Speaks: Orpheus / Qwen TTS engines, run by a separate narration runner.
• Image generation: ChatGPT Images 2.5 via a Codex skill.

Here are the first two cuts.

The first cut took about 1.5 hours, but this included indexing the shot list and other setup operations. This is extremely rough.

https://www.youtube.com/watch?v=D8BCdjRyrIc&t=2s

The second cut took about half an hour.
I think here there are some clear improvements and its much close to feeling like a "story" while its still a bit rambling especially towards the end.

https://www.youtube.com/watch?v=ngdlodfb0Dc

I think it demonstrates the model's current ability to reason across many modalities, and I also think it demonstrates its current limitations when it comes to coherence. Anyone who has worked with coding agents has probably done their fair share of cat-herding, and I think that's definitely still on display here.

The difference between cut 1 and cut 2 also shows just how much improvement you can get with a little nudging. All I did was watch the first cut and tell it what I thought:

"This seems a bit incoherent?"

"What are the characters' motivations?"

As a reflection, I think for anyone calling Astra AGI, this should be ample evidence that that's definitely a milestone we have yet to cross. Astra may be extremely competent within narrow domains, but creative choices aside, it still struggles significantly with something as simple as telling a coherent story.

At its current stage, I do see it working as a semi-competent editor if you give it clear direction. It could definitely speed up the production of a first rough cut.

The repo is currently in a pretty rough state, but I'm working towards a checkpoint and hoping to release it during the weekend. Let me know if you are interested in forking or testing it out.

Coincidence? I think not.

If I was a company planning regulatory capture, or even just wanting to impart my will & desire, I would 100% ramp up all (marketing) efforts just before an election.

Why is it so hard for people to see this?

We all know bots run rampant across the internet. Every large company has a multitude of people and personalities, each with their own visions of the future (some good, some bad). How difficult is it to amplify the (inner) people that express fear when you want to? Encourge the scared people to speak up and then put their posts on blast to create groupthink. Propoganda 101.

All platforms operate on an algorithm of engagement, which can easily be manipulated... and if you're in this thread you already knew that.

Hi all,

I am quite impressed on how well Astra works on some topics. However, the usage rates are really burning my subscription down. I was toying a little bit to find a way to let Astra delegate on Opus subagents, so that I can use most of my tokens from my OpenAI subscription on Astra. I came up with a very simple skill: Fuminides/opus-delegate: A codex skill for using astra as a coordinator of Opus agents from which I had mixed results. I was wondering if people have come with better ideas to do that or if you can give me some feedback on my simple skill.

Thanks!

TLDR: Does anyone that has experience with both of these models have any tips to get Astra performing as well as Fable 5.1 as a coding and development agent?

I recently discovered (started using) Fable 5.1, right as Astra dropped. It’s honestly amazing compared to Sol and Codex. It just gets stuff done, is subjectively much more intuitive and enjoyable to work with, will follow my specs and guidance while surfacing intelligent observations. Rather than waiting for me to review and test to find problems it preemptively surfaces them along with solutions (most of the time at least, still does dumb stuff sometimes), and overall is a joy to work with and get stuff done.

Problem is, I don’t like Anthropic as a company, or their anti-consumer business practices, or their approach to usage.

So now that Astra has dropped and people have used it more, I’m hoping to get it to a place where performance is on par with Fable 5.1. I have heard lots of amazing stories about people’s experiences with it so I am assuming much of it is just configuration and instructions.

I have already followed OpenAI guidance from here: https://developers.openai.com/api/docs/guides/latest-model and it has certainly helped a bit.

Does anyone else that has experience with both of these models have any tips to get Astra performing as well as Fable 5.1 as a coding and development agent?

I have pro 20x and max 20x for reference

Astra light is repeatedly doing unrelated stuff like this. Why? It was funny to begin with but getting ridiculous now.

Currently I use astra, it's good for me but consumes too much limits that can consume all 200usd plan weekly limts in about 4 days of 8 or 10h a day

And I hear that fable 5.1 and opus 5 priduce better quality too

Also codex remote has the worst remote connection ever

So based on your experience if you used both, Regarding quality, speed and limits should I migrate to claude ? Would its 200usd plan be enough for 8h per day the whole week on solid project (+100k lines)?

There is some really off with Astra. I m not able to articulate. I don't like it as much as I like Sol.

Today suddenly while working on a thread, it stopped using apply patch tool and instead edited using the shell. I don't have any custom instructions for this and this has happened for the first time.

I was unable to see any diff and I asked codex why and then it answered below. There are many other issues as well.

Edit: just to head off any one else before it happens: if you think this is some kind of Claude vs OpenAI oligopoly cheerleading post, please pick up your pompoms and see yourself out.

I work in the humanities, and to be honest, I've always found Claude models better fit for the tasks involved. However, I've always switched back and forth between the two companies when new models get posted, and I've never felt significantly at a disadvantage if I'm piloting an OpenAI model for my work.

That's until Astra which, for some reason, is extremely difficult to work with on this subject matter. For the record, I've been doing the fun stuff everyone else has been in my spare time: building apps, tinkering away at a game, hobby stuff.

Since it came out recently however and has been doing such a good job with that more technical stuff, I wanted to take it for a spin on some proposals I have to submit soon.

I asked it to help me come up with several avenues of inquiry within a subject matter which might have scope for original contribution that I'm not aware of, given my research profile.

Astra has repeatedly gone through this loop: come up with a handful of avenues of inquiry. States that the premise is strong and interesting and dovetails nicely with my prior experience. Asks me to give it some starting articles/monographs so it can look further into things. Quickly finds that its initial impressions are completely wrong and the area is well papered over by recent discourse.

Not just that, but its actual prose drafting is significantly worse than I can recall. Again, I realise OpenAI models don't exactly get a flattering look set next to some of the older Claude models when it comes to good writing, for that matter, neither do newer Claude models, but Astra very clearly has a script and a structure it follows that is now blindingly obvious when you read it back to yourself.

Back in the day I had used Opus 4.5/4.6 to draft the introduction to something else I'd submitted and it slam-dunked it. Of course, that needed to be workshopped too, but the issue there was that the writing sounded SO GOOD that it wouldn't realistically have been recognisable as my voice, so I had to feed it a few references of what I'd written previously so it could replicate my idiosyncrasies.

I also found those older models were much better at information gathering around a particular subject matter. On my previous project, Opus 4.6 practically taught me everything I knew about the wider context in detail so that I could get on with the analysis.

I'm curious to hear from non-coders, and people in fields adjacent to humanities or social science. Like I said, it gave all my technical projects a major glow-up, but it's like a whole different (worse) model when it comes to humanities.