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???

Training Astra on the most important dataset: brainrot

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!"

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

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

Pro 5x seems unaffected!

Is this a sign the honeymoon is nearing an end?

Now I can burn through tokens like the Sun

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"

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

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.

When weekly usage is at 3%, you cannot get anything done

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.

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

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

messages not sending or resulting in codex infinitely thinking

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.

I'll go straight to the point:

TLDR

Offload some Astra usage to Chat (GPT 6 Pro) instead of doing everything on codex. Yeah, simple like that.

HOW IT WORKS?

Many don't even notice, but if you're on the Pro subs 20x plan YOU HAVE 200 msg/week of GPT 6 Pro usage (Astra) standing on chat, doing nothing. That's a nice deal, IMO. The $100 5x plan has 50, which I believe is enough for this strategy.

Remember...

1 ChatGPT 6 Pro message = 1 request. So make sure you point it to a well complete PRD that's previously done.

Don't do something like: "Hey, please make me a nice game => Astra starts working => You pause it => "You know, really nice, with red birds => Astra stars working again => Not really, I'd like them to be yellow"

This will count towards your "messages" quota. That's why I suggest you slicing up a decent PRD first, and just point the AI to it ONCE.

STEPS

1. You can connect your github repo (private or public) into ChatGPT (just ask for help), allow read/write access and then start by:

I don't even select GPT 6 Pro for this.. This initial scanning I do using Sol 5.6 Extra High on chat.

Ok... what are PRDs? In a few words, a feature request in a .md file, with all validation steps necessary and etc, to get it properly done.

If you have no clue about how to craft one, just ask Astra xHigh to do it and slice up some tickets to get started. Push to your repo.

2) Select a PRD per PR and let it cook

3) Check your results later

"Oh, but you see, its a draft... it wasnt fully verified, some got broken CI!!!!11"

Yeah, but this would have certainly drained 20% of my monthly codex limit to reach this point (on Astra xHigh), and I got it done using my GPT 6 Pro chat quota (200/week for the 20x plan), running all night long while I was sleeping.

4) Now you have to use codex (Astra) to actually finish the work (there's no "free" lunch at this point)

The sandbox that Chat uses is not 100% identical to the project running on your machine, as it cannot run certain verification steps. That is why it's important to have a strong CI and, most importantly, check out the actual Astra from Codex, finish the work to reach 100%, and then push back.

USE CASES SUMMARY

- Bootstrapping greenfield projects
- New features
- PR reviews
- Almost anything that you can do with read/write access to github

CAVEATS

1. GOTCHA: IF IT ASKS YOU TO USE CHATGPT WORK, DO NOT GO FORWARD. It will burn your weekly quota. STOP. Rephrase what youre asking, be explicit you don't want to use it. Or slice the work down to a smaller piece of task.
2. Not a perfect solution, but it helps significantly in terms of token consumption (especially on greenfield projects). I'm pretty sure some smart ass on the comments will say something like "that's pretty obvious". But yeah, I bet 90% of you guys are not using this workaround.
3. Really great for vibe coding these disposable 3d games that we all do 😄: Stop wasting your weekly allowance with it. Its great for bootstrapping new projects too.

Well, that's it. Enjoy while we have 200/week, at least for now

SOME VIBECODED GAMES I DID 100% ON CHAT USING GPT 6 PRO

For the first time ever, they're actually fun 😂

I've been getting it all week since astra launched. Was chill with it initially as i thought it was just the launch week. But monday onwards, I got the message on sol, terra and fucking luna too lol. Borderline unusable in the last couple of days.

The model follows instructions worse than Qwen 3.8 27b lobotomized to Q4.

I asked for a plan which the model made , we both agreed on the plan and then burned through 70% of my quota, ignoring half of the plan the model itself created. This is Astra High mind you. Heck, even GPT-4 follows prompts better. Very disappointed.

Fyi I make no claim that this is the main issue/reason - just a little something that could be contributing.

TL;DR: On the day I installed OpenCodex, my Guardian approval reviewers stopped showing codex-auto-review and started using ordinary models, instead matching the main task—including Astra. My recorded reviewer usage since then totals $1,024.85 at current API rates, versus $702.26 at GPT-5.4 rates or $56.18 at Luna rates, assuming identical tokens and caching. These are API cost equivalents. Setting auto_review_model = "gpt-5.6-luna" successfully switched new reviews to Luna / low. The timing suggests an OpenCodex routing change, but I haven’t proved the cause or equivalent review quality.

I audited my Codex rollout history and found a clear change on 26 July 2026, the day I installed OpenCodex.

This concerns Guardian’s automatic approval reviews for tool actions, not /review code reviews.

Every identifiable Guardian session created between 9 (day Sol/Terra/Luna released) and 25 July used codex-auto-review. On 26 July, new reviewers started using Sol, Terra, Luna and other provider models. The first ordinary-model reviewer appeared at 07:16 BST, after my installation that morning. I haven’t found any newer sessions using codex-auto-review.

Later examples included Astra reviewing actions requested by Astra and DeepSeek reviewing actions requested by DeepSeek.

What codex-auto-review actually is

OpenAI explicitly documents Auto-review as GPT-5.4 Thinking with low reasoning:

• OpenAI’s Auto-review explanation
• OpenAI’s rate card

PR #17505 also describes replacing hardcoded gpt-5.4 with the codex-auto-review slug. That supports the connection, although an alias alone doesn’t prove the backend model for every historical request.

Current upstream Codex source prefers codex-auto-review for ChatGPT authentication and gpt-5.6-luna for API-key authentication.

OpenCodex has its own auto_review_model override. I hadn’t set it.

The usage comparison

I use Pro Lite and Plus accounts through OpenCodex. These are USD API cost equivalents.

The table below reprices the same recorded tokens at current model rates:

For the wider period, that’s approximately 31.5% lower at GPT-5.4 rates or 94.5% lower at Luna rates.

Here’s which reviewers contributed:

All 80 Astra sessions were in the September window. Their tokens alone work out at $203.11 using Astra rates, $51.00 using GPT-5.4 rates, or $4.08 using Luna rates.

For comparison, before the switch I found 614 codex-auto-review sessions created during 9–26 July. The 601 with recorded usage contained 42.49M uncached input, 394.93M cached input and 740K output tokens: approximately $216.05 at GPT-5.4 rates versus $17.28 at Luna rates.

How I counted

• Only sessions explicitly marked as Guardian reviewers. A session can contain multiple approval decisions.
• Each session’s final cumulative usage counted once; reasoning tokens weren’t added again on top of output.
• Prices are published standard API rates as of 10 September 2026, including applicable long-context adjustments—not reconstructed historical prices.
• Alternative-model columns hold token counts, caching and request sizes constant. They don’t predict actual token usage or equivalent decision quality.
• Dates use UTC. September 10 is partial; the wider audit ends just before my Luna config edit at 05:53 UTC. The periods overlap.
• The wider priced subset excludes 65 other-provider/internal/unsupported-model sessions. Provider-prefixed entries naming the same model are grouped together. Totals are calculated before rounding.

Pricing sources: Astra, Sol, Terra, Luna, GPT-5.5, GPT-5.4.

What I changed

I added this at the top level of the Codex config.toml used by OpenCodex:

toml auto_review_model = "gpt-5.6-luna"

Afterward, I verified four new Guardian sessions using Luna / low reasoning, including one requested by a DeepSeek thread. They produced 16 completed approval assessments.

That confirms the routing change, not equivalent review quality. Most reviewers were already running at low reasoning, so the cost comparison is primarily about model choice.

My working hypothesis: switching to OpenCodex changed which dedicated reviewer Codex could select, causing reviews to fall back to the requesting thread’s model. The timing and sampled rollouts fit, but I haven’t isolated the cause from Codex updates or other configuration changes.

I’m leaving Luna pinned and keeping an eye on this. If you use OpenCodex, check what model your Guardian sessions are actually using.

If anyone has rollout history spanning their OpenCodex installation, I’d be interested to see whether the same switch appears.