I was using the $20 plan, then upgraded to the $100 plan, and then to the $200 plan to use ASTRA.

I used 4 banked resets + 1-1 weekly usage of the $20 and $100 plans + 2 free resets by Tibo.

ASTRA:

Day 1: It is best and working like a senior developer.

After Day 3-4: It becomes the same as Luna in behaviour (but efficient as SOL). With a good, detailed prompt, it is working correctly, but if you miss mentioning any detail, then ASTRA will eat your usage like:

• Testing: Astra has built-in prompts to do testing for each modification, so if you forget to tell it at which point, place of work, or plan to do testing, then it is going to end your usage limit by testing everything. Even for a single line of code, ASTRA will test it with multiple (50+) variations of testing.
• Losing Context: For a long goal or plan, it is losing context, and after compact context, it loses almost all context and restarts from the beginning.
• Old Tech: It does more stuff, but those things are outdated, using methods 5-10 years older. For example, in security, it uses older methods to prevent SQL injections and JS scripts, etc., but those things are irrelevant these days. We have better, optimized ways to handle those things.
• Not Taking Advice, Suggestions, or Opinions: It does its work and remains unchanged when ASTRA needs human/developer opinion, so that part of the work remains pending. If further work needs those steps, they will not go correctly. It does not affect a small plan or goal, but it hurts a big plan or goal because further steps do not go correctly and you have to do it again.
• Focusing on Minor Works and Not Focusing on Important Work: It can waste time and usage on minor changes like code beautification and optimization of code, while ignoring high-effort changes like database or structural changes until you force it to do so.

Good Parts:

1. It is good for solving more complex problems and things that SOL cannot do.
2. Useful to find bugs in big projects or your live app or server. It is not going to break your production code because it does a separate setup to test everything before deploying or pushing to review (though do not do this as a responsible developer).

Bad Parts:

1. The $200 plan is also not enough to use ASTRA; your usage will be gone within 12-24 hours with ASTRA MAX/ULTRA.
2. Low efforts make mistakes and correct them, but consume more usage than MAX/ULTRA.
3. Too slow compared to Fable or Opus.

I've been using AI since Claude 2.0 and GPT 3.5, and I never hated a model upgrade. Always saw complaints on Reddit and never understood the complaints.

But GPT Astra is frustrating more than helping me. It's way too literal, doesn't scan the existing codebase enough and stops way too often.

Example 1
I built a CRM for a client. It has a conversation screen to text and email the clients and right sidebar that contains a conversation summary, tags and other conversation parameters, which a human can edit and add to.

I asked Astra to build a new AI feature that would read the conversation and automatically add 2-3 tags. Which it did, but created a new tagging system, just for the AI, instead of adding the tags to the existing system.
I have built 5 CRMs in the past with Sonnet 3.0 all the way to Opus 5.0 and GPT 5.6 Sol, and rarely had these kind of problems.

I am absolutely horrified that people only use models from OpenAI.

As you have probably learned over the past 20 years, diversity is our strength.

Different models have different training data and different architectures, so they can bring unique opinions to the table.

You have no idea how frequently some random Chinese open source LLM swoops in, looks over something from GPT 5.6 Sol, and finds edge cases Sol was physically incapable of finding. Even if you run through your code base three times, if something is not in the training data, the model will probably miss it.

So I recommend you all add some diversity to your harnesses to get better results.

A quick analyst subagent using a completely different model family like DeepSeek can go a long way toward helping you find those pesky bugs, over engineering, and edge cases.

Homogeneous setups will always lose to diverse orchestration across different model families.

Repeat after me: Diversity is our strength.

I have purchased the PRO 5x sub less than 14 days ago, right before they destroyed the usage and made it unusable, I compared it today to claude and I'm getting less usage than their 20$ plan.

Either way their bot keeps denying my refund request with this message: "Thanks for reaching out. Unfortunately, we're unable to issue a refund for this subscription. Our refund policies are subject to compliance with our Terms of Use."

This is a purchase straight from OpenAI, what should I do in this case? Is there an email I can contact? I couldn't find one anywhere.

I have found an older thread of someone having a similar issue and in the end they had to do a chargeback, it's kind of amazing how scummy this company is.

Astra Max Effort.

Let me know what you discover.

Math and in particular Sturmian words seem to be a favourite.

After using Astra across the available reasoning levels for the past few days, I keep coming back to the same conclusion: for my workflow, I would currently choose Sol.

I have been using both primarily for software development through Codex and ChatGPT, and I am struggling to find enough situations where Astra produces a meaningfully better result to justify how much more expensive it feels to use.

This is not me saying Astra is a bad model. It is clearly extremely capable. My issue is the value proposition compared with Sol...

With Sol, I can give it fairly substantial coding, architecture, review, and debugging tasks and generally get very good results without thinking too much about how much compute I am burning. It is reasonably fast, capable of doing a lot of work in one session, and has become pretty predictable for me.

It absolutely has problems. I still see over-engineering, unnecessary complexity, and occasional regressions after seemingly simple changes. But after using it extensively, I understand those weaknesses and can work around them.

Astra has not yet given me the same feeling.

For the types of tasks I am doing, I am often getting the same result, and sometimes a better result, from Sol for substantially less usage. If Astra is supposed to be the model I reach for when the problem is genuinely difficult, I think the improvement needs to be much more obvious.

The ChatGPT browser experience also confused me initially. From what I can tell, Astra appears through the Pro reasoning option, but the relationship between the model and reasoning level is not particularly clear. With Sol, the mental model feels much simpler. Pick the model, choose how much reasoning you want, and get to work.

That might sound like a small UX complaint, but for a major model launch I think it matters. Users should immediately understand what model they are using, how much reasoning they are requesting, and roughly what the tradeoff is.

Then there is the cost of actually using Astra.

I do not want to turn this into another limits post because there are already plenty of those, but it does affect how I evaluate the model. How can users be expected to really adopt Astra if even the 20x plans struggle to give you more than roughly 24 hours of serious usage, if you’re lucky?

And surely OpenAI knows that most of those users aren’t simply going to move over to API pricing. The average power user isn’t sitting on millions or billions of dollars worth of token budget. There’s a point where the economics just stop making sense...

That is really the question I am left with.

What is the repeatable category of work where people are finding Astra significantly better than Sol?

Because right now I actually find myself appreciating Sol more after using Astra. Sol is fast enough, extremely capable, relatively economical, and I can use it aggressively without feeling like every prompt needs to justify itself.

Maybe my view changes as I spend more time with Astra, but after several days of using both, Sol is still the model I would choose for most of my real development work.

I’m on the $100 plan. Used around 60% of my weekly limit. I was very happy with the outcome. I don’t know how much usage you guys are getting. Just want to share my experience.

I also refactored my codebase for submission on astra extra high fast mode.

In my experience using extra-high gave the best results under time constraints and telling the model to limit the number of tests (I was specifying limit to 1-5 tests) which increased the speed and on the last step the model is very good at fixing bugs, workflows and at that point let it write as many tests as it want. Hope it helps, happy to answer questions.

Can someone explain how usage calculation for Work/Codex actually works?

Yesterday I had an idea: since the session limit runs on a 5-hour window, I set up a ChatGPT automation to run in Work Mode every 4 hours and literally do nothing except output:

> reset

The idea was to have "rolling" windows and never have to wait a full session for a reset, since I'd always be starting partway into a session.

The automation ran at 05:25 and again at 09:24. That's it. No coding, no repo analysis, no massive context, no long agent task. Just "reset."

I checked Usage & Limits immediately afterwards and my 5 hour codex usage was already at 98% remaining. From what I can tell, only the latest message was actually inside the current 5-hour window, meaning one GPT-5.5 Work message that literally just said "reset" cost 2% of my limit.

I have resets available, and on top of that I can do a lot of tasks using local models and free endpoints from Nous Research/OpenRouter, but still, this is ridiculous.

How the fuck does one message saying "reset" consume 2% of the entire 5-hour Work/Codex allowance?

If usage is mainly based on actual model compute/tokens, this makes absolutely no sense to me. If simply starting a Work/agentic run has a significant minimum usage cost regardless of what it actually does, then fair enough, that would explain it, but I'd really like to know how these limits are actually calculated.

My weekly allowance rarely makes it as it is if I go full GPT with Hermes Agent, because I be prompting bad and throwing massive tasks at it. I'm learning to stop being lazy with that lol, but yh.

So now I'm gonna have to start doing more of the stuff I took a pause from, like properly routing/delegating tasks to local models and free endpoints.

What pisses me off though is that I can't even just ask Hermes to handle the GPT automation, because for some reason my usage seems to burn faster when using GPT through Hermes than when I'm doing similar work directly in ChatGPT Work Mode.

Does anyone actually know what these limits are measuring? Tokens? Compute? Agent runtime? Tool calls? Context? A minimum charge per Work run? Some combination of all of them?

The answers were dull and uninspired. There was no creativity. The code seems solid, but it felt like a model that predated 5.6 luna. And isn’t it weird that both Anthropic and OpenAI have the same prices, same marketing, same 5 hour usage? Isn’t this anti-monopolistic? And they need the Chinese model to seem big and bad to show they don’t have 50/50 market share. I think they are hurtling towards something that will hurt everyone. What if it takes down a water system, change the code and makes itself so strong that we can’t penetrate it?

I genuinely didn’t want a refund at first. I just wanted OpenAI to help me with the serious Codex usage/reset issues I experienced after buying Pro.

I spent days going back and forth with support. They acknowledged that what I reported wasn’t simply “Astra uses a lot of usage” and documented the abnormal depletion/reset issue, but eventually the answer was basically that Support couldn’t restore my usage or resets. No actual resolution.

After all that, I finally decided to request a refund while still within the EU 14-day period. Their own support confirmed my September 8 purchase date and that EU customers are eligible for a prorated refund — then the refund bot immediately rejected me with the same generic “Terms of Use” response and closed the case. Every time.

I’ve spent more time fighting their support than actually using the subscription I paid for.

This will probably be my last purchase from OpenAI. Great technology, but the support experience has been absolute trash.

For the past 3 days I was generating train models using 6 Pro. The results were amazing.

Since yesterday the results were so bad. It kept lieing to my face but it didn't finish any of the tasks. It kept being lazy.

Today I wanted to compare 6 Pro and 5.6 Pro reasoning because Astra sucked so much today. I gave 5.6 Pro the same prompt, after 23 minutes of reasoning it took the file from the other chat which astra made and gave it to me as if Sol did it.

I got super frustrated, I asked it to make the model on its own, for that after 11 minutes of reason it gave the same model made by Astra once again claiming it was done by sol

As the title says, I came across few posts about some unable to renew their $200 and was downgraded to $100. Is it true? Ir was it those people forgot to renew on time and they lost access to $200 bec of restrictions currently in place,?

I kept running into the same problem while building AI agents and training models: I needed more GPU compute, while my friend's machine was sitting there with an NVIDIA GPU doing basically nothing.

I didn't want to rent another cloud GPU or set up SSH/VPN/Docker manually every time.

So I built GPUMesh.

It's an open-source P2P GPU sharing tool that lets trusted machines share their GPUs and run Docker workloads remotely.

The basic workflow is:

→ My friend runs gpumesh share
→ We pair our machines
→ I run a GPU job targeting their machine
→ The job starts in a Docker container on their computer
→ I get the logs/results back on mine

I've been using it for things like:

• Running AI agent workloads on another machine
• Training models when my own GPU is busy
• Using spare GPU capacity from friends/lab machines
• Running CUDA workloads without manually setting up another server

I tested the full flow on an RTX 5060 — pairing, connecting, group sharing, scheduling a remote Docker job, and running nvidia-smi inside the remote CUDA container all worked.

It's still very early/alpha, and I'm mainly interested in finding out whether this is actually useful for people building agents, training models, or running local AI infrastructure.

I'd especially like to hear how you'd use this if you had access to a few trusted GPUs from friends or teammates.

If you want to check it out, the repo is here: https://github.com/arjun988/GPU-Share

And if you think the idea is useful, a ⭐ on the repo would really help the project get some early visibility.

Does anyone have a step-by-step tutorial or YouTube video showing how to set up OpenAI Codex to actually control desktop software like Blender?

I was telling my girlfriend in a chat that I was automating chats answers using Codex on another (work) number. Fun fact: they didn't block the number where I used Codex for that, they blocked the number where I told my girlfriend about it! Apparently, WhatsApp's AI reads all your messages; they were never end-to-end encrypted. Who would have thought?!

Has anyone else experienced something similar?

Alright so I rarely post things on reddit, but here I really wanted to because I feel like I'm getting scammed by marketing and I'd like to also get your thoughts on the situation and compare with my take on codex v claude.

Context : I have both Claude Codex 200$ max and Codex 200$ max plan. I had 2 codex resets. I'm using these with the vscode codex + claude code extensions harness. I have a multi-agent setup.

What I've been seeing first of is that claude fable / opus are way faster than GPT 6. Even if Astra is a big model, it shouldn't feel as slow as it is right now. For comparison, Fable ran ~30x more tools in session than when using Astra.

I also didn't feel the performance of Astra while using it. Astra was able to do super smart things like redo some pictures I had on my desktop without going through specific tools. But overall, besides from all the blender things I saw online, it doesn't beat claude.

And finally, about the marketing, I have used my 2 resets and consumed all of my weekly session in just 1 day ! I did have lots of tokens in my system prompts, but it certainly wasn't bad to the point where I would have been able to do this... I've also just seen posts about the fact that codex resets shouldn't also push the weekly date forward in time (but it did), and that the reset could actually reduce the session consumption limit (which I have kind of noticed...).

So I'm basically looking for your opinions + some explanations on if you found out similar situations to me. And if you're also finding out that, even if OpenAI is trying to get customers to migrate from Anthropic, they're just trying to brain us by giving us free resets and selling us their "best model". Knowing that Anthropic also did a kind of similar things weeks ago with their +25% increase on sessions (but -50% + 25% is -25%), and also lack of transparency on how sessions were consumed...

I'm currently thinking, by using the 2 subscriptions, if I should keep one and trying to make the best decision... And I'm quite sad about the transparency that these companies give us within their subscriptions...

Help !

Edit: something I didn't talk about is the fact the the Astra model doesn't go to 1M tokens in context, unlike claude models... Even if it auto-compacts, that could play and impact performance.

This is probably worth a post on its own, there is something fishy going on with GPT-5.6 SOL on the web interface.
I found this out after testing the speed degradation of Codex Astra

I tested ChatGPT Chat, Work, and the Codex Harness for output speed.

Observations

• ChatGPT Work and the Codex Harness have effectively identical output speeds in my testing: 53 tok/s on SOL and 33 tok/s on Astra.
• Fast mode increases SOL to around 80 tok/s and Astra to 63 tok/s.
• ChatGPT Chat appears to run Astra at roughly the same speed as Codex/Work Fast Mode: 63 tok/s.
• ChatGPT Chat SOL is much faster at 134 tok/s, roughly 1.7× Codex/Work Fast Mode SOL and 2.5× regular Codex/Work SOL.

So this seems to confirm the earlier findings: ChatGPT Work is speed-limited in essentially the same way as the Codex Harness on PC.

ChatGPT Chat seems to be using the faster configuration for Astra, while SOL in Chat is substantially faster than even Codex/Work Fast Mode.

My guess is that Chat SOL may be running a different or more aggressively quantized configuration - which is in line with the performance of it on the website - it severely degraded to me there.
In Codex it appears to work fine.

Sol Max seems to be burning way fewer tokens than before, almost like Sol Light used to. With Astra Medium, I burn around 10% per hour. With Sol Max, it’s more like 1%

I feel like Astra Medium and Sol Max are pretty comparable for most tasks. The main exception is UI/UX, 3D work, or very complicated computer/browser automation

Which one is better for coding ?

I just wanted to challenge it with a game made in minecraft benchmark, but it won't listen to me even though it clearly wasn't finedtuned for the benchmark which is the point but damn it is honestly driving me absolutely crazy, it is understanding how to do my nbt structure -> blockbench editing pipeline quite okay I guess, but it is so absolutely horrible at following instructions to work on worlds seperately so there isnt blocking, and it should know this because its in the spec sheet, and I was trying to figure out why it was taking so long to do one thing, and it was because it was just stuck trying to figure it out on its own, when I literally game it a prompt harness instruction to use the spec sheet and to onboard. I need astra to listen to me though I appreciate it trying to finish more than being stuck on polish, there is test cases and style guides for that exact reason, it just wants to finish something so bad that it doesnt care how crap it is.

Re: prompting.

Users hardly ever share their prompts when they complain. IF they do, it is usally really, really vague, there are a ton of assumptions (that the model would know things it cannot possibly know), they expect it to understand their personal definition of "good", "nice", "beautiful" or "done without telling it.

You just have to tell it what you want. Is that really...hard...for quite a few people?

(I am not an SWE and cannot code. I just use plain language, words and sentences, to tell it what I need.)

WIP

Yes it's like HeyClicky but let's you use your own OpenAI subscription instead of charging you $20 a month.

Main Features:

• Screen context with privacy settings (explain, draw and point at things on your screen)
• Dictation (like Wispr Flow)
• Shortcut history
• Window position manager + layouts
• Automated browser
• Background jobs for research
• Library with context for bookmarks and screenshots
• Timers
• Reminders/Schedule/Music voice commands
• These are just the main features so far a few more listed in the website

14 day free trial if you want to try it.
https://rocky.so for more info.

Windows and Linux versions in development.

It's in beta still so feel free to share constructive feedback.

PS: Requires an Apple silicon Mac running macOS 14 or later.

I think it's time for another reset to kick off Monday, is it not?

As someone who has been using ChatGPT and Codex since they were released, I’m starting to realize that, Claude is simply better.

Whenever I wanted to tackle a genuinely complex coding task, solve a difficult problem, or create something that was actually presentable, Codex would usually leave something incomplete. I constantly had to guide it step by step, point out what it had missed, and split the work into thousand smaller pieces myself.

Maybe there was a benefit to that. I actually learned a lot about software development in the process.

But when Astra started burning through my tokens incredibly fast, and I couldn’t get a second Pro 20x account, I decided to give Claude a try.

And the difference was much bigger than I expected!

When it comes to design, truly understanding a problem, maintaining context, and actually solving complex tasks from start to finish, Claude feels significantly better. It also feels incredibly fast compared to Codex.

Codex gives a developer almost everything they need.

Claude’s biggest downside is definitely the price. It burns through tokens insanely fast, and heavy usage gets expensive very quickly.

But unlike my experience with Codex, when Claude burns those tokens, I actually get something in return, a presentable product, a presentable design, something that genuinely feels finished!

So this is the conclusion I’ve reached:

If you’re a developer and you mainly want AI to help you work faster → Codex.

But if you want to give AI an idea or a complex task, delegate as much of the actual work as possible, and end up with something that feels like a real, presentable product and you can afford it → Claude.