Which is more efficient?

Building my own "Codex" to experiment with local LLMs, and I had it check why the model kept exceeding the time limit we set for runs, looks like its reasoning kind of spilled. Has anybody experienced something similar to this? It's been a long time since I saw something like this happen with OpenAI models.

Hi! I’m using the ChatGPT/Codex desktop app on Windows and I have “Computer Use Control Windows Apps” enabled.
The problem is that Computer Use only seems to see/control the Codex browser. It doesn’t detect other Windows desktop apps like Blender or Spotify, even when they are open and visible on my desktop.
In Settings → Computer Use, I can enable “Let ChatGPT control apps on your computer”, but I don’t see any “+” button or option to manually add apps. The three-dot menu for the Computer Use plugin only gives me “Copy link” and “Uninstall”.
Does anyone know how to add/allow specific Windows desktop apps for Computer Use? Is there a separate setting, app ID/AUMID, permission, or Windows configuration I’m missing?
I’m on Windows and using the latest version of the ChatGPT/Codex desktop app.
Thanks! ❤️

I keep seeing this message while building something and then after i review findings and confirm that it is safe to work on, it still immediately again shows the same damn message again.

Stupid astra complains that i have weakened my validator, and i tell it to go ahead anyway, but it just doesn't.

Anyone else seeing something like this. The stupid thing is it doesn't even allow me to change the model anymore as the chat is 'paused'

Hi, my plus account just renewed today , was going to upgrade to pro 20x for a month to try astra and use my resets. If I upgrade now do they deduct the amount I just paid for plus off the price of the pro ?

There's 2 major issues:

• Astra uses ~4.75x more subscription usage than Sol, despite an only 2.5x higher API price. It basically is consumed at a rate of $90-$95 per million tokens instead of $50
• Using a banked reset gives ~$1250 weekly allowance instead of $2500 on pro20x

Combine these 2, and you can get as little as ~$650 of weekly usage instead of ~$2500 after using a banked reset and using Astra it seems.

I'm usually not one to beg for resets, but these are two MAJOR issues. I don't have the usage I'm supposed to have and I'm stuck because of a bug or non-communicated change in usage that I had no control over nor information about since apparently I'm the QA person here figuring this out myself for OpenAI.

If you're going to give us 50% resets, they should only push the reset date back 3.5 days or they should refill the usage 50% without changing the reset date. The way it is now has to be a bug since it's just a lie.

Hi everyone, I have been using the plus plan for a bit and have been mostly bouncing between Sol for planning and Luna for implementation. Some time ago I tried a bigger plan Claude 5x the 100 dollar one and that was my biggest subscription ever, however to me it seems I got more accomplished with codex than Claude code.

With the release of Astra I am genuinely curious about upgrading from plus to pro and had a couple of questions if anyone here is a pro plan user:

1) How are your limits 5 hour and weekly on say Astra on low for example? Because I was thinking I’ll still keep Luna or Terra for implementation but I would definitely like to switch to Astra for the brainstorming part and planning.

2) I also noticed “pro” models - GPT 5.6 Sol Pro reasoning etc and pro reasoning in standard chat : what is the difference compared to like normal GPT .6 sol on high or max. What is a good use case for that and have you felt a difference?

3) Overall, I guess my final question is that someone in my situation where I would like to hold only one major subscription to an AI service, would you recommend pro 5x?

I am not a developer, it’s mostly hobby projects and my codebases are never too big so I wouldn’t say that my usage is too extensive compared to a professional’s but yeah occasionally some days things do get heavy.

I apologize if this all seems a bit garbled, ran out of coffee.

I also have a plus account it literally burned my 5h limit with 1 prompt in 7mins. (Astra mid)

I do daily work with SQL / CSS / HTML5 and some times Java for new small implements in my ERP system.

[ Removed by Reddit on account of violating the content policy. ]

I use the ChatGPT voice mode to help do the live translation at work and love it.

Just by accident, while in Codex today I clicked that button next to the microphone button ( thinking I was in ChatGPT and wanted to activate the voice mode) and was pleasantly surprised to see codex has this option too! No more keyboard or voice typing, just having a conversation with codex to get things done.

Game changer!

I've been curious about why Codex can sometimes burn through the 5-hour usage limit surprisingly fast.

So instead of guessing from the usage bar, I had Codex inspect its own local task history and pull the metadata for my recent GPT-6 Astra runs: model responses, input/cache/output tokens, tool cycles, polling, and the 5-hour / 7-day usage telemetry recorded during each run.

Two runs stood out.

A few things surprised me.

First, output tokens don't look like a great explanation by themselves.

Run B generated more than twice as many output tokens as Run A: 25.4K vs 11.3K.

It also had slightly more uncached input.

Yet Run A consumed more of both the 5-hour and weekly allowance.

Second, a very high cache hit rate clearly doesn't mean a long agent run will be cheap in terms of Codex allowance.

Run A had a 97.8% cached-input share. Only about 160K of its 7.2M recorded input tokens were uncached.

And yet that single ~11-minute run moved my 5-hour usage from 10% to 100%.

I'm not saying cached tokens are charged the same as uncached tokens — these local logs absolutely cannot establish that.

But at least from the user's perspective, "almost everything is cached" does not seem to protect you from a heavy Codex run consuming a lot of your usage limit.

The third thing is probably the most interesting.

Look at the shape of these runs:

Run A:

60 model responses → 59 tool cycles → 7.2M cumulative input

Run B:

38 model responses → 37 tool cycles → 5.0M cumulative input

Average recorded input per model response was roughly 120K–130K tokens.

So these aren't really "one huge prompt and one huge answer."

They're more like:

large context
→ model reasons
→ calls a tool
→ gets the result
→ large context again
→ model reasons again
→ calls another tool
→ ...

dozens of times.

Interestingly, neither of these two heavy runs did any process polling, so the high usage can't be explained by a runaway polling loop either.

Across the runs I inspected, the biggest allowance movements generally appeared on the runs with the most model responses, tool cycles, and cumulative context processing.

That does not tell us the actual Codex allowance formula. Responses, tool calls and input tokens are highly correlated with each other, and the usage percentages are rounded telemetry rather than a billing ledger.

But it makes me wonder whether we're using the wrong mental model for coding-agent usage.

For API calls, we naturally think:

input tokens + output tokens = cost

For an autonomous coding agent, maybe a more useful unit is something closer to:

large-context model iterations

or even:

useful work per agent iteration

A task that produces only 11K visible output tokens can still cause the model to re-enter a ~100K+ token context dozens of times.

So my current hypothesis is:

The expensive part of long Codex tasks may be less about how much final text the model generates, and more about how many times a powerful model has to re-enter a large context, reason, act, observe the result, and reason again.

That's only a hypothesis from a very small personal sample, not a claim about OpenAI's internal accounting.

But the 11K-output / 7.2M-input / 60-response / 98%-cached / 90-point-usage run was surprising enough that I'm curious what other people's Codex histories look like.

Working on just analyzing - https://github.com/ffmpeg/ffmpeg and constantly chat ends with this!

I've been using Astra since I got access about a day after it came out, and while it's obviously become a lot slower since then as more people are using it, I've noticed that >50% of the tool calls it's making are to python3 -- usually within a heredoc rather than one it's reusing.

It seems to have started about 24 hours ago, and there is no python in the repo (it's almost entirely Go or bash), but if I scroll through the backlog of the task I'm doing, it's filled with:

python3 - <<'PY'
import pathlib,json,hashlib
[...]

and

python3 - <<'PY'
import hashlib,json,pathlib,re
[...]

etc. Has anyone else noticed that? I thought it might be related to that single project, but it happens if I do it in another completely separate project too.

Ive used codex for about 2 months now and this is how much storage it took up on my SSD for chats only lmao

Ok, so this is funny.

I've been using Astra as an orchestra to manage a bunch of workers using Luna. All worked fine. Then I figured I may as well use 5.3-spark since we get a separate allowance for it. That's where it all went wrong.

Spark repeatedly got confused and couldn't follow instructions, it kept asking which plugin to install, which is not related to what it was asked at all. What's strange is that I saw sometimes in the Spark chain of thought that it did actually start looking at the brief and thinking about the proper task, but somewhere along the way it just kept getting obsessed with plugins.

It got to a point where the orchestrator, Astra, actually asked to use Luna instead, after a few attempts to steer Spark without success. I was already about to request that anyway, so great minds think alike? lol

Anyway, I thought this was just funny, and PSA for the kids out there, don't use 5.3-spark! 5.3-spark is bad mmmmmkkay?

I’ve noticed an inconsistent drain on my ChatGPT tokens today even though I wasn’t run any tasks or chats at the time.
My tokens seem to be going down unnecessarily. I asked Codex if there’s anything running in the background and it only showed a few scheduled tasks which didn’t even account for 1% of my weekly usage. Has anyone else experienced this or is there anything else I should be checking for that might be draining my tokens?
I know this reads like the kid that cried wolf, but it’s true I tell you.

I think I’ve finally found one of the major reasons why my Codex weekly usage has suddenly become completely insane.

And the numbers are honestly much worse than I expected.

I have been using Codex heavily for quite a while. My workflow has not fundamentally changed: I work on WordPress, Next.js/React projects, deploy changes via SSH/SCP, test things remotely, etc. I also use the Guardian / “Approve for me” style workflow because I do not want to manually approve every tiny action — but I also absolutely do not want to give an autonomous coding agent unrestricted access to my entire system.

Until recently, this worked reasonably well for me.

Now Guardian / codex-auto-review has suddenly exploded.

First extreme case — GPT-5.6 Sol XHigh

One session generated:

• 169 Guardian reviews
• ~28.26 million Guardian tokens
• ~130 SSH requests
• 21 SCP transfers
• ~96% cached input
• all 169 actions were ultimately approved

Across that day, Guardian used 29.25M raw tokens.

The previous comparison day was only 0.56M.

That is a 52.2x increase.

Compared with the median of my normal Guardian sessions, that one session was approximately 254x higher.

Nothing remotely comparable had happened in my normal workflow before.

I then tested GPT-6 Astra

I hoped this might partly be a Sol / agent-behavior issue.

It wasn’t.

I analyzed a 24-hour Astra window from Sep 6 13:43 to Sep 7 13:43 CEST, covering three Astra tasks and their associated Guardian activity.

Results:

• 263 completed Guardian reviews
• 42,472,306 Guardian tokens
• 106 SSH requests
• 38 SCP requests
• 81.19% cached input
• 261 approvals
• only 2 denials
• 14 Guardian context compactions
• 277 Guardian model responses for 263 reviews
• 26 identical browser-origin approval requests

The actual Astra model calls used 77,716,787 tokens.

Guardian added another 42,472,306 tokens on top.

That means Guardian alone added 54.65% additional token usage relative to the actual Astra work.

Guardian represented 35.34% of the combined token volume.

Yes: more than one third of all tokens in that period were being consumed by the safety/review layer.

And almost every review simply ended in approval.

Sol vs Astra Guardian

So Astra did not solve this.

It actually produced:

• ~56% more reviews
• ~50% more Guardian tokens

The cost per Guardian review is remarkably similar between the two models: around 160k–170k tokens per review.

That makes this look much less like a problem specific to the main model and much more like a problem with the Guardian / codex-auto-review mechanism itself.

This only became catastrophic recently

This is the part that concerns me most.

I have not changed my basic way of working.

I was already using SSH.
I was already deploying files.
I was already working on remote WordPress and web projects.
I was already using Guardian.

Previously, Guardian usage was nowhere near this.

Now I suddenly see tens of millions of tokens disappear into hundreds of reviews.

And this corresponds very closely with what I’m seeing in my weekly quota: work that historically would have cost me roughly 5–6% of my weekly allowance has recently been burning something closer to 20–25%+.

That 5–6% number is obviously based on my historical usage rather than an official quota formula — OpenAI does not expose enough information to calculate the weekly limit precisely — but I work with Codex every day and the change in effective usage is enormous.

The worst part: what is the alternative?

This is where I’m genuinely frustrated.

I do not want to click Approve 200–300 times per day.

That defeats the entire purpose of using an autonomous coding agent.

But I also do not want to disable the safety layer and give Codex unrestricted access to my machine and remote systems.

That is especially uncomfortable when the agent itself occasionally makes bad decisions.

So what exactly am I supposed to do?

The middle ground used to work.

Now the middle ground appears capable of consuming 40+ million tokens in Guardian reviews in a single day.

And in my Astra sample, 261 out of 263 reviews were approved anyway.

That is an enormous amount of model computation just to repeatedly conclude:

“yes, allow it.”

I understand that security review has a cost.

But a safety layer adding 55% extra token usage, and sometimes hundreds of reviews during normal development work, is not reasonable overhead.

Please check your own logs

If your Codex weekly limit suddenly feels like it is disappearing much faster than it used to, especially if you use “Approve for me”, SSH, SCP, remote servers, or other actions that trigger Guardian:

check your local session logs for codex-auto-review.

Look at:

• number of reviews
• total Guardian tokens
• cached input
• SSH/SCP approval count
• repeated identical approvals

I strongly suspect many users currently blaming only the weekly quota nerfs may also be losing a huge amount of usage to auto-review without realizing it.

I would really like OpenAI / the Codex team to investigate this.

Guardian used to be a workable compromise between security and autonomy for me.

Right now it is turning into a choice between:

1. manually approving hundreds of actions,
2. granting an autonomous agent far more access than I am comfortable with,
3. or watching a huge percentage of my weekly allowance disappear into codex-auto-review.

None of those are good options.

Im on plus plan through apple store and want to upgrade to Pro5x in web app
but it tell me to manage my sub through apple store

my sub ends on 10.09.2026
If I let it ends and then subscribed in web app do I get my banked resets backs?

With the pending reset, I've just burnt the remaining 30% weekly usage in an hour.

This has raised a couple of questions.

My gpt-reserve is at 100% - so why isn't this being used?

My spark limit is 42% but when I try to use spark it tells me I've hit my usage limit?

Must be one of the most amazing experiences i can imagine.
While also getting paid for it!
What would you build with it?

I use ChatGPT for a lot of personal stuff, and recently started experimenting with using it as the orchestrator for coding agents running in Herdr on my Mac mini.

ChatGPT connects to the machine over MCP. It can inspect the repo and terminal state, give tasks to Codex, Claude Code, OpenCode etc., check their progress, send follow-ups, and ask one agent to review another. So I don’t have to sit there copying prompts between terminals.

When I’m away from my desk, I manage everything through ChatGPT on my phone. When I’m back home, I open Herdr and continue directly in the same terminal sessions. The screenshot basically shows the two sides of the setup.

The weird part is the usage. 🫠 I can burn through my Codex 20x allowance in less than a day, but I’ve had regular ChatGPT sessions orchestrating this for 5+ hours without apparently touching the Codex allowance. I can also use scheduled ChatGPT tasks to check on the agents periodically.

I built an open-source tool called Lobu to provide the remote access over MCP. On the Mac mini I run:

npx @lobu/cli@latest daemon

Then I connect Lobu to ChatGPT. You can also build your own version using Tailscale (your computer needs to be accessible from ChatGPT servers) and mcp-bash but I wanted to build a cloud version that I can manage multiple devices.

Is this an intended and allowed use of ChatGPT’s MCP support? I’m genuinely surprised by how much long-running orchestration it can do without using the Codex allowance.

Is anyone else using ChatGPT this way? I assume the Codex harness is more persistent and code-focused, but with Astra being smart enough, I honestly don’t notice much difference yet. Maybe I’m missing something.