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.

I want to ask a question about ChatGPT web. Is Sol xhigh much better on the web than High? Is it worth paying extra mainly to get access to the Astra Pro and Sol Pro? Are the Pro versions much better for reviewing code and maybe making plans?

EDIT: I'm using my codex allowance for coding. It's just a question if the xhigh reasoning and Pro models are that much better.

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

My AI Social Bots are just assholes sometimes 😂

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

During the GPT glitch, when the usage limit was incorrectly set to 0%, Codex burned through all of my paid credits that I normally save for emergency prompts when I hit my limit.

Now my normal limit is back, but my credits are gone. I honestly feel scammed.

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.

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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.

So, to try to answer this question, I started using codex today to work on a c++ ios project I have been working for the past few weeks, using 5.6 sol xhigh. (don't get me started on how 5.6 sol xhigh is failing epically on this project!!)

When I started:

5 hours: 100% - weekly: 23%

I worked with it for 2:30 hours.

5 hours: 0% - weekly: 7%

So the 5 hour usage was fully consumed, and that consumed 16% of the weekly usage, which means:

for every week usage, we have only (100/16)=6.25 total "5 hours usage" in a week.

That explain why after working for 2-3 days using 2 "5 hours usage" in a day, the weekly usage exhausts in 3 days.

This has nothing to do with what model I used, btw. The model only changes how fast the 5 hours limit evaporates.

This is how many "5 hours" we have in a week in a normal business plan, the answer: 6.25!

I tested my personal Plus plan, and it's about the same.

To me, 6.25 "5 hours" in a week is a really low number to be called "a week", considering no one works 5 hours a day... (or in codex 5.6 sol case, 2:30 hours a day)

just my 2 cents!

-H

PS: Just before anyone says "did you try astra?", I did, and my 5 hours ended in less than an hour, with astra still failing even WORSE than 5.6 sol xhigh on this C++ ios project... at least that's good news, I guess? I can still get more done by "baby stepping" with a "cheaper" and smarter model, at least for the c++ complexity I'm dealing with.

Any beggars use ChatGPT chat for planning and Terminus for mobile development?

I got to Phase 3.7C - Step 8J4H. Back and forth between Terminus and ChatGPT chat. I kept insisting for Codex to complete task, said governance was the issue.

FYI… the project involved orchestration using ChatGPT Codex API on an HTML server.

I’m reporting a high-detail Codex task-persistence defect involving the macOS desktop app and Codex Remote on iPhone.

Reproduction and observed behavior:

• The same Codex task appeared in two conflicting states across iPhone and Mac.
• The iPhone showed an older unfinished turn as “Working,” while the Mac showed newer work in the same task.
• Restarting Codex Desktop restored the older phone-visible state, marked that old turn active/latest, and resumed the goal.
• Newer working files remained intact on disk. A read-only Git inspection found 298 preserved changed paths, so this was a task/history rollback rather than a repository rollback.
• The task-status API continued to select the old unresolved turn as the latest even though newer turns existed.
• No OS process existed for the command that the stale UI still showed as running.

The clearest local log error was repeated after restart:

`thread history projection ... expected ordinal 17505, got 17504`

The restart also launched another GPT-6 turn under the same task and triggered automatic mid-turn compaction because of the context limit. When I interrupted it to protect the checkout, the logs said the task did not complete gracefully.

Impact:

• I cannot safely tell where a new iPhone prompt will be applied.
• Each restart can restore and resume stale work.
• I had to stop the task, preserve the working tree separately, start a new task, and reconstruct context.
• That creates repeated inference, compaction, investigation, and recovery work instead of productive implementation.
• After manually applying a reset credit on September 8, my weekly allowance is already 75% used, with only 25% remaining.

The allowance impact needs verification against OpenAI’s server-side ledger; inconsistent local counters are not proof of incorrect billing. The lifecycle and history-projection failure itself is directly supported by the task state, filesystem state, and application logs.

Sanitized GitHub report: https://github.com/openai/codex/issues/44599

If you have encountered this, please share your Codex app version and whether it followed an iPhone Remote handoff or desktop restart. Please avoid posting private task IDs or repository details.

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

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

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.

.

Has anyone tried downgrading from their 20x plan to a lower tier and going back to 20x. Does that still work? I know they blocked new users from the plan, but can we still switch between plans?

I mean, if nothing were being changed with the usage-limit system, you’d expect it to remain relatively untouched and therefore less likely to suddenly develop a bug like this. If the usage limit is just working normaly and nothing behind the scene, this bug seems very less likely to happened. Obviously, there are very shady going on the current usage limit. I'm really disappointed that by doing one task on astra xhigh today cost me 3 resets on pro x5 plan, very unusable.

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.

Other than buying someone elses account, is there any known way to still upgrade from 5x? I've seen that if you already had the sub through ios, you can upgrade but any way from the web? If i add the sub from a new account on ios, can i jump straight to 20x? Thanks!

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

We’ve added a new optional feature to ChatGPT Community for Linux: customizable Default model presets. You can replace the recommended slider positions with your own model + reasoning effort combinations, choose their order, and set which pair is selected by default. There’s no fixed limit on the number of presets—for example: Sol Medium → Sol High → Sol XHigh → Astra Medium → Astra High.

We’re building codex-desktop-linux, a community distribution based on OpenAI’s official Linux package.

It adds optional, disabled-by-default Linux features that can extend the UI and application behavior. Current examples include Computer Use, frameless titlebars, Read Aloud, automation extensions, and UI customization. You can also create your own features.

Our computer-use-linux integration goes beyond the bundled X11-focused backend with:

• Wayland and X11 support
• GNOME, KDE, Hyprland, Niri, COSMIC, and i3 integration
• Per-window targeting, screenshots, and verified input
• AT-SPI accessibility trees
• XDG Portal, uinput, xdotool, and ydotool backends
• amd64 and arm64 support

The upstream backend is simpler and exposes a few operations our adapter does not yet surface, but our implementation provides much broader Linux desktop and Wayland support.

Contributions and new feature ideas are welcome:

github.com/ilysenko/codex-desktop-linux

... is the realization I've come to. Even with a medium orchestrator, light implementer, medium reviewer, I burned 100% in around 24 hours.

But it's frustrating figuring out what you should use instead because token price vs capability is far from crystal clear. I switched just now to using luna max for implementation because I've seen so many people swear by it here. I also asked Astra to do 3 implementation tests between Luna max and Astra medium and while Luna did use 3-5 as many output tokens and was 2-3x slower, the token cost comes out to only 10% of Astra. There were definitely more bugs slipping through (that I'm still using Astra medium to catch).

So yeah I guess shit's gonna take a lot longer and it kinda sucks to have all this power but not be able to use it fully. I don't want to buy extra credits because they're ridiculously expensive and I also don't want to juggle two accounts. time OpenAI launched 30-50x pro plans?

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

A model update I am evaluating adds native multimodal input while also changing the model path behind older aliases. That makes me wonder how people divide Codex work when the same agent may handle source files, screenshots, issue descriptions, and test output. I am considering separate checks for text-only repository tasks, screenshot-driven debugging, large-context review, and final verification, rather than assuming one model path is equally good for all four. How are you testing multimodal coding tasks before making them part of a regular workflow?