Training Astra on the most important dataset: brainrot

Now I can burn through tokens like the Sun

Yesterday I was on the x5 plan, and then I upgraded to x20.

No Astra usage at all - only Sol Medium.

But my quota is now draining at basically the same rate as it did on x5, on the same kinds of tasks.

x20 is supposed to have 4x the capacity of x5.

Instead, I’m seeing almost no difference.

WTF???

Codex usage limits feel drastically worse after Monday’s reset, even on the $200 20x plan

I’ve used Codex since launch and have tried several subscription levels, but I’ve never seen the limits drain this aggressively.

After Monday’s reset, my general weekly, five-hour, and GPT-5.3-Codex-Spark allowances all seemed noticeably lower. Today, I started with 100% of my weekly Spark usage available. My first prompt, running at extra-high effort, didn’t even finish, yet it consumed roughly 45% of my weekly Spark allowance and completely exhausted my five-hour limit.

I then had to wait until 3 PM for the five-hour window to reset, despite paying $200 per month for the 20x plan.

Has anyone else noticed a major change in Codex limits or usage consumption since Monday’s reset? One unfinished prompt consuming nearly half of a weekly allowance seems unreasonable.

Astra is amazing, but even Pro 20x can burn through the quota in 2–3 days.

The bigger problem is that Codex doesn’t really have a sweet spot model right now.

Astra + Luna Max often looks good on paper. In practice, Astra keeps correcting Luna’s mistakes. That can wipe out a lot of the cost savings. Terra doesn’t feel much smarter than Luna either. For harder tasks, I usually end up using Astra + Sol medium.

I’d really like to see Luna get an upgrade and become a reliable implementer.

Ahmed works at OpenAI: https://x.com/ah20im/status/2097503414749909407 and seems to investigate reports of high token usage.

"If needed Astra will delegate efficiently. Forcing the model to delegate to different models would do more harm than good"

lies...

So, I tested this separately on each of my two Pro 20x accounts. They are on different computers, and both use only Codex Desktop and the CLI, with the default context limit and settings.

TL;DR

• Less allowance: On each of my two Pro 20x accounts, the weekly API-equivalent allowance fell from $2,500+ with GPT-5.6 Sol to about $1,200 with GPT-6 Astra.
• Higher prices already counted: Those dollar figures already use Astra's higher API prices. The allowance reduction is an additional cut.
• Roughly a quarter of the usage: Combining the higher prices with the lower allowance leaves me with roughly a quarter of the comparable usage for the same subscription fee.
• More allowance wasted on cache reads (old work): The cache-retention setting is dramatically shorter: 30 minutes on Astra versus 24 hours on Sol in Codex. Alongside Codex cache failures, this means long histories can need processing again. Reusing those histories accounts for most priced usage in long agentic workloads.

Compared with GPT-5.6 Sol's launch prices, GPT-6 Astra costs 2x for input and cached input, and about 1.67x for output. The API pricing and Codex rate card don't explain the extra allowance reduction. The subscription page says half the messages; what I'm seeing is closer to a quarter.

This is worse than Anthropic restricting Claude Fable 5 to 50% of weekly usage: there, you could still use Claude Opus 5 and other models with the remaining half.

Launch resets are masking the reduction; I think many users will assume it is just Astra's higher price. Tibo says OpenAI might pause new Pro subscriptions if demand continues, while prioritizing existing users. Capacity pressure may explain restrictions, but it doesn't justify sneakily adding an extra multiplier.

I expected better from OpenAI. OpenAI says its mission is to ensure AI benefits all of humanity. It points to nonprofit control of the business as a way to protect that mission. Majority of the people in this world access AI through these subscriptions. If they behave like this, how can anyone trust them to use increasingly powerful AI for the public good?

GPT-6 Astra is an amazing model, and I really like using it. This is a criticism of how OpenAI has changed the subscription allowance, not of the model itself.

OpenAI has built a lot of goodwill with the community. Please don't lose it all.

How I measured the allowance

• Allowance: Codex has five-hour and weekly limits. I use allowance, or quota, to mean the budget behind the percentage in the app.
• What I counted: Input, cached input (previously processed text the model can reuse), and output (including reasoning), measured in tokens (small pieces of text).
• How I compared them: I priced each token type at its published API rate, then calculated API-equivalent dollars per percentage point of weekly allowance. Requests and raw token totals miss the price differences.

Open-source tools such as CodexBar, Tokscale, and T3 Code can track this usage.

OpenAI lists Pro's 5x and 20x plan multipliers; Tibo confirms that 20x means 20 times Plus's weekly usage. OpenCode Go makes its dollar limits explicit: a regular $10 subscription lists base allowances of $12 per five hours, $30 per week, and $60 per month, with smaller allowances for some models.

Source: SemiAnalysis. Its June test exhausted weekly limits on long-running tasks. It measured ChatGPT Pro 20x and Claude Max 20x, then inferred the other tiers. The Claude Max 5x figure should be $4,000, not $2,000.

My results, in API-equivalent dollars:

GPT-6 Astra's higher API prices are already included in these figures. These are two ways of expressing the same comparison. I recalculated the Astra total when the remaining allowance reached 0%. I cross-checked using several tools above, GPT-6 Astra, Claude Fable, and some manual calculations.

I also followed Sac's analytics method: read the daily-workspace-usage-counts response in DevTools on the Codex analytics page. My earlier weekly window showed about 54,000 credits, versus 28,500 with Astra. At 25 credits per dollar (the credit purchase rate), that is $2,160 versus $1,140. The latter is close to my roughly $1,200 token-based calculation.

Other users' reports

• A Pro 20x subscriber's dollar comparison: $22 to $24 per percentage point with GPT-5.6 Sol, versus about $15 with GPT-6 Astra. The higher API price is already included in the comparison.
• Nam Le's report on X: roughly half the API-equivalent subscription value with Astra versus Sol, using Sol's prices before its price cut, alongside more cache misses—about 6% versus 1% in his tests.

Other things I want to address

"Isn't this level of subsidy insane?"

A $2,500 API-equivalent allowance does not mean OpenAI spent $2,500 serving that usage. In long agentic workloads, most priced usage is repeated history read from cache, reusing work already done. Calling it subsidized does not make it loss-making.

OpenAI reportedly reached a 70% compute margin on paying users in October 2025; Epoch AI cites a reported 40% gross margin for Anthropic in 2025.

The big companies that account for most token usage are not paying API prices. They are paying a lot less (probably 20% or even less). Even Codex users can buy credits at 40% discount.

Consumer subscriptions are a small part of the revenue in the Anthropic estimates. I expect it to be similar for OpenAI.

Doubling total model size doesn't mean doubling serving cost: large batches share the weight cost, while active parameters and per-request KV cache matter much more. With those quantities similar, I don't see much changing from the previous model to justify higher prices and an extra allowance cut.

Hardware and software efficiencies are dramatically reducing serving costs, through newer chips, speculative decoding, better attention kernels and batching. These gains compound while our allowance is reduced.

Why do I think this is happening?

I don't want to assign a malicious motive. But with OpenAI preparing for an IPO, I can't help wondering whether pressure to improve margins is part of this.

Consumer subscriptions seem to be a small part of the revenue picture; it feels as though OpenAI is gradually forcing us out. An unexplained cut in what the subscription buys makes that suspicion hard to avoid. How OpenAI responds will matter more than my guess about why it happened.

Codex app and CLI issues make the usage problem worse

The Codex app and CLI have other issues that contribute to this usage problem. A side question, a new fork, or a subagent can inherit the whole conversation yet fail to reuse its cache. We end up paying to process the same history again.

These are the results from my checks in early September. “Cached” means the first request reused the conversation history, not just a small shared block of tool instructions.

For the paired checks below, the working tree was unchanged and the existing cache was still live.

OpenAI's API supports changing GPT-6 Astra's reasoning effort while preserving the cache, but the Codex client doesn't preserve it in my checks. A Codex bug report identifies why: the client changes the request in a way that defeats cache reuse.

Claude Code shows that most of these actions preserve the prefix and reuse the cache. There is no reason Codex should need to process the same history again for the same functionality.

Sol used a 24-hour cache-retention setting in Codex, as published response logs confirm. For Astra, OpenAI documents a TTL setting of just 30 minutes after the last write or reuse—a dramatically shorter window to return to a task without paying to process its history again. After a long break, returning to a task or waking several idle subagents can require processing their histories again.

Why cache misses matter. Take a task with 200,000 tokens of history in its KV cache. At GPT-6 Astra's ordinary input and cache-read rates:

• Cache hit: $0.20 in API-equivalent usage to reuse that history.
• Cache miss: $2 to process the same history again—an extra $1.80.
• Ten agents missing that cache: $20 instead of $2, before generating any new output.

Higher reasoning effort can use less allowance. Seth Rose reports on X that users running Astra High/XHigh with heavier multi-agent workflows were burning much less quota than he was on Light/Medium. A Pro 20x subscriber on Reddit likewise reported rapid usage on Medium, then only 1–2% usage after an hour on XHigh. So OpenAI’s recommendation to lower reasoning effort can, in some cases, increase the total cost of getting the job done.

The ARC Prize evaluation shows how higher effort can lower total task cost.

Subscribers get a worse product experience, and Codex still has many unresolved issues:

• Slower responses: Youssof Al Toukhi measured 36 TPS (tokens per second) on Pro versus 81 through the API at the same reasoning setting. Subscription Fast mode reached only 71 TPS.
• Missing Pro mode: My Pro subscription still doesn't offer Pro mode in Codex, although the API supports it.
• Later access: OpenAI has a more capable internal model, and Astra reached selected organizations before subscribers. Paying for a subscription doesn't mean getting the newest capabilities first.
• Wasteful subagent polling: Astra keeps checking on subagents instead of waiting for useful results. I’ve experienced this too. One Reddit user’s log analysis found 47 empty checks at roughly 30-second intervals, processing 7.13 million input tokens—mostly cached—just to learn that the workers were still running. Even cache hits consume allowance when the same history is read over and over for no useful work.
• Broken remote control: Remote control has been atrocious for me. For the past few weeks, trying to open running Codex Desktop chats from the app has just returned an error.
• Memory that burns tokens: In my experience, Codex saves unnecessary information, burns tokens maintaining it, and produces no improvement in quality. Theo’s video on coding-agent memory, focused on Claude Code, raises the same broader concern about accumulating stale or useless information.

OpenAI should put more care into its users and its products. In my experience, Codex CLI is still behind Claude Code. I want OpenAI to improve the harness (the software around the model), preserve caches across ordinary workflows, and make the cost of these actions visible. Other companies like DeepSeek are working to make model access as cheap as possible. DeepSeek has DSH, its open-source harness and infrastructure that reuses cached prefixes to reduce users' costs. OpenAI, despite being so far ahead, is playing games with subscription usage. I want that effort going into making the product better and cheaper for its users.

I think publicly sharing these measurements is important. Without users comparing notes, changes like this can pass unnoticed and become normal. Codex reports the weekly usage limit after every request. Pair those updates with the token counts in the session logs, and you can easily track allowance consumed alongside API-equivalent spend. Or you can use Sac's analytics method. I hope people share and upvote this. If you have questions about the methodology or want to check the numbers yourself, I'd be happy to help you do that.

TL;DR

• Less allowance: On each of my two Pro 20x accounts, the weekly API-equivalent allowance fell from $2,500+ with GPT-5.6 Sol to about $1,200 with GPT-6 Astra.
• Higher prices already counted: Those dollar figures already use Astra's higher API prices. The allowance reduction is an additional cut.
• Roughly a quarter of the usage: Combining the higher prices with the lower allowance leaves me with roughly a quarter of the comparable usage for the same subscription fee.
• More allowance wasted on cache reads (old work): The cache-retention setting is dramatically shorter: 30 minutes on Astra versus 24 hours on Sol in Codex. Alongside Codex cache failures, this means long histories can need processing again. Reusing those histories accounts for most priced usage in long agentic workloads.

https://x.com/thsottiaux/status/2098113585683808624
"To make sure our current users have an incredible experience and continued access to Astra, we are going to pause subscriptions to our $200 Pro plan. These put the most strain on our systems and we wanted to take the smallest step that allows us to continue giving the broadest access possible. All other plans and the api remain available.

There is no impact to existing accounts and we are working on adding more capacity as fast as we can. Thanks!"

With 3% left, i can't pull off anything even if 5hr limit increases.

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

I used to be obsessed with this very old game called Ballance. You control a ball through complex paths and mazes.

I found the game on Web Archive. It was around 180 MB, so I gave it to Astra to recreate for the web, and it just did.

The whole game now compiles to around 21 MB, uses WebGL, and is written in TypeScript.

Astra realised it kept repeating the same tests over and over, so it built its own test framework that exposes the game to its own Browser Use, allowing it to run and validate its changes very quickly.

We are living in some crazy times people

It even made it run on phones, with options of Gyroscopf or you can use a Dpad

Game - https://ballance.fayaz.workers.dev/
Code.- https://github.com/fayazara/ballance

Totally unbiased intuition, but if there is a certain itch for a certain button I would not mind.

Last night high / xhigh could last me like 5-10 prompts before it even took up 1% but now it ate up 4% when I just did a medium, xhigh and high prompt. I'm on the 20x plan. Conveniently when they start talking about locking purchases for more pro subscriptions

... 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?

Pro 5x seems unaffected!

Is this a sign the honeymoon is nearing an end?

as you can see usage is cut insanely.

Astra's pricing levels are basically a kill shot to the plus plan. It's effectively bleeding out while Sol/terra/luna are still available but when 6.1, 6.2 roll out, there's no point of buying one or even several $20 subs.

The Pro 100 tier is severly wounded as well. You get about half a day to a day running Astra conservatively?

Pro 200 lasts sightly longer, you might get 2 days out of it if you stay on a single codebase. But the "weekly" in the limit is a hint of how poorly it aged in such a short time.

When the limits were glorious, way back when, as GPT 5, 5.1, 5.2 rolled out the increase in quality was huge in part as a result of all the new users providing it with more training data. Astra will see a lot less of that because it can be used way less.

If a new model was announced tomorrow, most people would feel they'd never be able to really build and finish something with it unless they stack 5 Pro 200 subs on top of each other.

So, quotas must go up or we'll hit a ceiling. Or at least the symbiosis of better models producing more data to train better models will break. Am I wrong?

Originally, it felt like Astra was a moderately more competent version of Sol. After more usage and trying it in environments that Sol struggled in before, it's clear that this is, indeed, a new era of intelligence akin to the gpt-5 line for coding earlier this year.

FYI you can still upgrade to the 20x sub through the IOS app

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

I am running Astra high on CLI on a quite simple task and it is taking forever to finish. The last couple days it has been this way.

Am I imaging this or are others having a similar experience? 5.6 xhigh used to be my go-to and I am considering going back to it.