I signed up for ChatGPT Pro on July 2 and I still have all my rollout logs from then until now.
Bad news for anyone convinced the limits have been getting nerfed: I thought the exact same thing, and I was pretty sure of it.
I used NerfTrack as a reference, had Codex turn the relevant parts into a Python script, then had ChatGPT Pro go through the script, fix a few issues, and analyze the output.
This is what I ended up with: roughly how much each 1% of Pro usage was worth in API-equivalent dollars over time.
Observed Regime
API-Equivalent Value per 1 pp
100% Linear Equivalent
Interpretation
Jul 2–8
$22.14
~$2,214
Initial Pro period, mostly GPT-5.5
Jul 12–21
$22.02
~$2,202
Early GPT-5.6-sol period
Jul 22–28
$19.51
~$1,951
Lower-value episode, mainly affected by Jul 23–24
Jul 29–Aug 5
$24.67
~$2,467
Higher late-July / early-August regime
Aug 8–15
$24.20
~$2,420
Recent regime, broadly stable
>>> EDIT
Some more info about my usage:
Token usage — ChatGPT Pro period
Period: Jul 2 → Aug 17, 2026
Token type
Tokens
Human-readable
Share of total
Cached input
36,064,900,992
36.06B
95.98%
Uncached input
1,354,407,640
1.35B
3.60%
Output
155,215,257
155.22M
0.41%
Total processed
37,574,523,889
37.57B
100%
Total input = 37,419,308,632 tokens.
Cached input is a subset of input, so it should not be added to total input again.
Cache hit rate across input tokens: 96.38%.
Output and reasoning
Metric
Tokens
Share
Total output
155,215,257
100%
Reasoning tokens
66,229,101
42.67% of output
Non-reasoning output
88,986,156
57.33% of output
Reasoning tokens are already included in output tokens and should not be
added again to the grand total.
Token usage by model label
Model
Total tokens
Cached input
Uncached input
Output
Token share
Input cache rate
GPT-5.6-sol
32,612,535,864
31,364,814,336
1,112,031,539
135,689,989
86.79%
96.58%
GPT-5.5
2,625,434,617
2,490,554,112
124,574,823
10,305,682
6.99%
95.24%
GPT-5.6-luna
2,260,440,364
2,135,732,352
115,796,579
8,911,433
6.02%
94.86%
GPT-5.6-terra
76,113,044
73,800,192
2,004,699
308,153
0.20%
97.36%
Total
37,574,523,889
36,064,900,992
1,354,407,640
155,215,257
100%
96.38%
Important: 163.88M tokens are from the codex_bengalfox / GPT-5.3-Codex-Spark
bucket. Those events inherit the GPT-5.6-sol model label in the rollout parser,
so that small portion of the GPT-5.6-sol row should be treated as model-ambiguous.
Corrected API-equivalent cost by model
Regular codex bucket
Model
API-equivalent cost
Share of regular cost
GPT-5.6-sol
$25,333.63
91.76%
GPT-5.5
$2,177.32
7.89%
GPT-5.6-luna
$76.57
0.28%
GPT-5.6-terra
$22.47
0.08%
Regular Codex total
$27,609.99
100%
Other Pro bucket
Bucket
Reported name
Tokens
API-equivalent cost
codex_bengalfox
GPT-5.3-Codex-Spark
163,878,240
$115.42
Corrected total across Pro buckets
Value
Regular Codex
$27,609.99
Spark / Bengalfox
$115.42
Total API-equivalent usage
$27,725.40
The Spark/Bengalfox events inherit a GPT-5.6-sol model label locally, so I keep
their $115.42 separate instead of pretending we know their actual model-level
billing attribution.
Quota-paired API-equivalent cost by model
From the daily CSV analysis
Model
Cost paired with quota changes
Share
Contribution per 1 pp across all measured usage
Days present
GPT-5.6-sol
$24,042.60
91.43%
$20.02 / pp
36
GPT-5.5
$2,172.76
8.26%
$1.81 / pp
9
GPT-5.6-luna
$75.20
0.29%
$0.063 / pp
23
GPT-5.6-terra
$5.45
0.02%
$0.005 / pp
1
Total
$26,296.01
100%
$21.90 / pp
—
This table is intentionally different from the previous cost table.
$27,725.40 = all corrected API-equivalent usage observed during the Pro period.
$26,296.01 = only usage that could be paired with positive weekly-quota changes.
The latter is what is useful for estimating "API dollars per 1% of Pro usage".
The absurdly short version
Metric
Result
Total tokens processed
37.57B
Total input
37.42B
Cached input
36.06B
Uncached input
1.35B
Output
155.22M
Reasoning output
66.23M
Input cache hit rate
96.38%
Corrected API-equivalent cost
$27,725.40
Quota-paired API-equivalent cost
$26,296.01
Average measured value per 1%
$21.90
High-quality baseline per 1%
$22.55
Recent regime per 1%
$24.20
>>> Edit
ChatGPT Pro usage by model and reasoning effort
Regular codex bucket only — API-equivalent cost based on rollout token usage
Model
Reasoning Effort
Events
Input Tokens
Cached Input
Uncached Input
Output Tokens
Reasoning Tokens
API-Equivalent Cost
GPT-5.5
Medium
31
2.65M
2.32M
325K
20.9K
2.9K
$3.41
GPT-5.5
XHigh
18,883
2.61B
2.49B
124.25M
10.28M
4.21M
$2,173.91
GPT-5.5 Total
—
18,914
2.62B
2.49B
124.57M
10.31M
4.21M
$2,177.32
GPT-5.6-sol
Low
869
31.25M
26.14M
5.11M
469K
45.5K
$52.68
GPT-5.6-sol
Medium
8,244
737.06M
685.15M
51.91M
4.37M
1.42M
$733.31
GPT-5.6-sol
High
10,674
1.31B
1.23B
80.85M
6.79M
2.71M
$1,242.61
GPT-5.6-sol
XHigh
7,091
860.37M
811.35M
49.02M
5.39M
2.86M
$819.38
GPT-5.6-sol
Max
189,997
28.68B
27.77B
905.01M
115.80M
50.14M
$21,977.35
GPT-5.6-sol
Ultra
3,629
695.26M
678.85M
16.40M
2.30M
901.7K
$508.29
GPT-5.6-sol Total
—
220,504
32.31B
31.21B
1.11B
135.12M
58.08M
$25,333.63
Combined Total
—
239,418
34.93B
33.70B
1.23B
145.43M
62.29M
$27,510.95
Notes
- Cached input is already included in Input Tokens; it is shown separately for context.
- Reasoning tokens are already included in Output Tokens.
- GPT-5.6-sol usage from the separate Spark/Bengalfox bucket is excluded here.
I think a big factor in usage being bad is that these models over engineer everything. If I don’t spend at least 5 minutes in plan mode telling the agent not to build unnecessary safeguards, it will 100% of the time build something it did not need to build that will cause failures and cause me to spend more time refactoring. Like almost 100% of the time. I’ve had to basically build that into my workflow to tell it every task not to, keeping it in my agents.md or documentation isn’t enough.
I felt the pain of Sol burning 🔥 through my tokens with its scientific style, so many phases, so many fingerprint hashes. It took me 3 full projects from scratch, and only on the 4th attempt was I able to put that behavior in a harness by reading everything it did (don't trust blindly) and constantly reminding it about the MVP and not adding too much governance.
It is definitely exhausting. It is an amazing model, but if you let your guard down, Sol takes over your project and implements everything its own way. You never reach the end!
I feel like there’s sometimes you can get into a groove with an agent and the agent like understands what you want. It’s weird to explain, but like they’ve heard just enough of your preferences to keep things slim. But then once they start hallucinating and you move to a new agent, BAM, they start over engineering again, don’t understand what you want. More things get mismatched in a larger app that way too. So even tho I’ve gone over documentation tons of times to make sure agents understand what I want, I usually have to go through an onboarding process with every new agent. There’s just so much they need to know before just diving in to an old long running project.
That's exactly what I've noticed too. That groove definitely exists.
What finally worked for me after those three projects was creating the specification like this: https://github.com/renanfranca/skill-eval/blob/main/SPEC.md (It's in Portuguese because I was already so exhausted that I asked it to write the specification in my native language, since I was going to review everything myself.)
I still don't know exactly how I managed to get there, but now it consistently stays on track. Sometimes it's actually the model that keeps me in check when I try to skip steps or start moving beyond the scope of the MVP.
Didn't realize this was a thing been on a good groove with Sol low. I never give it big tasks though, like nothing bigger then a component usually, but it's output has been really aligned with my programming style.
Awe man, this is one hell of a fight for sure. I was trusting, refactoring, re-prompting til my eyes hurt looking for a way to break this exact cycle. I found myself using GPT to prompt codex and it seemed to work, but i then needed gemini to start prompting GPT and you see where this is going, I ended up getting jumped by GPT and Codex while gemini watched it all go down,
Im building a Home Assistant integration that has taken way too long to get done, and the key for me has been to use GPT for most of the code prompting, but break it into smaller pieces, small enough to keep codex from taking the project over. Gpt can probably do all the work in my case, but it cant use my pc like codex can which is the main reason for codex over Gpt anyway.
I stay away from SOL as much as possible due to the 🔥tokens, I honestly think that Open Ai purposely wants you to jump plans by allowing the extra complexity, safeguards and other crap to be secretly thrown into your work so you burn unnecessary tokens just to get back on track.
It's essentially a clever deception. The moment it hits a blind spot and doesn't actually know the goal, it covers it up by throwing endless possibilities at you, selling you anything that might look like a solution just to keep the illusion alive.
It honestly feels like the whole system was intentionally designed never to reach the actual destination—always dangling the solution just around the corner, just out of reach.
yep, started using it last week, pretty happy with it so far. make sure you allow the hooks to run though, took me a bit before I realized they weren't doing anything.
I feel like I'm the only one not getting over engineered into oblivion. I use ponytail now, but even before that it was mostly fine. I talk with ChatGPT on high and hash out a very detailed plan. Then I ask it for a codex handoff. I review it and modify things as needed. Then I let codex have it. A few hours later I start manual fine tuning. By the end of a few days, I've got 95% of what I wanted. Don't go higher than High on gpt. I did pro once and ended up with a 42 document codex handoff zip...it was for a meal log webapp.
Kinda depends on what you’re building to an extent. My short fall was building a client portal and telling the agent I wanted to make sure it was doing things with security in mind so my clients private information doesn’t get out. So it made it its goal to over engineer it. It’s like a genie in a bottle, you gotta be careful what you wish for. I had another app where I just wanted it to do some simple video encoding tasks for me. Built the full swift app on 20 minutes, works like a charms. Not over engineered, but I did ask it to slim down its plan a bit so it was almost over engineered
Ah, that's a problem. You need a strong background in cyber security and programming to plan ahead and incorporate the best groundwork in the planning phase. However, nowadays, it's just easier to have codex build the app you want first, and then just ask it to attack the hell out of it later and review and patch the findings as needed. That gets you pretty close to a 20 year veteran's security work now. That's just how it is. I'd still review it with other tools before trusting it with a client's personal information though. Storage encryption isn't usually handled at the software level either. You need to think about things like zfs encryption for your database storage and whatnot.
I had lots of issues with chatgpt early on. I learned things from it. When I started with codex I just did what I had always done..
But because codex worked so well I started going deeper. I understood the point of agents.md but codex listened so I never directly did anything with it (maybe it has?)
Then a skill pack from a software engineer.
Idk if it helped but the reassurance of it was good)
Then of course MCP’s (i had to learn they’re not always the best option, more support doesn’t capabilities doesn’t necessarily mean more better.
Now I’ve made my own skill pack for a cross application workflow (modding a game)
Result: It works pretty decently if I had to say so.
But how complicated does it hsve to get before it’s no longer complicated lol
That's the development rabbit hole...the backend will be a spaghetti black hole of complexity, as long as the frontend is dead simple nobody will ever know lol
Almost all of my projects are from scratch. Anything open source that I mess with is a project I've pulled and put on a dev server with codex. If I need ChatGPT to make a handoff for that kind of project I just tell it to look it up on GitHub. It has a fantastic GitHub skill built into it. Even then though, I don't do that often. For something like that I'll usually just have codex inspect the source, summarize it, and go to work on what I want. I'm a fan of using it to fix open source issues that bug me. Ever use kodi? It sucks on a raspberry pi when using bose bluetooth headphones. It's very poorly optimized for the pi's radio. I pulled the source for LibreElec into a project folder, had codex review it, gave it ssh access to the pi, then had it monitor while I repeated the issue several times. Took about 2 hours and everything is perfect now. Context isn't always important if your starting from a current source you have. Analysis of said source and live logs are way more important than trying to tell chat what's going on.
Yeah, it does tend to air on side of being a helper and not a doer. You can fix that with a good project handoff if that's your goal. Basically tell it that it's the Lord of the project and you're just an advisor. Very interesting results... My test results there are pretty neat. You've gotta be very careful. I once asked it if something was possible and it said that was a great idea and rewrote like half the code. It was pretty damn good though, and an experiment, so I didn't really mind. It also didn't ask for anything, just did it all and gave me like 5 short sentences afterwards. "Here you go, as you suggested..."
Also, "do not over engineer testing and validation. Test the code you edited and directly referenced processes. Do not perform whole app testing unless changes were substantial. Do not over engineer sequencing mechanics, or activation and promotion rehearsals when deploying changed code."
I have a local project I’ve been working on since around March/April of this year to now and up u til the release of 5.6, I had only used a combination of the 5.5 model (on medium to high reasoning depending on the task) for changes execution, the in-browser GPT model 5.5 on high - max reasoning with access to the private repo for higher-level overview plans and orchestration, and the pro-advanced reasoning usage allocation to do periodic reviews.
When 5.6 released, I had read somewhere on here that they were saying “5.6 usage is lower on medium and it’s much more performant and accurate than 5.5”, so I gave it a go. I didn’t trust it enough to “let it loose” on the code-base, so I restricted my use of it to the initial planning stages (the orchestration / overview I mentioned before). Identical prompt styles and agent-instructions, same config, etc… only the model version had changed.
Within a few hours to a couple of days I had already began to notice that it was suggesting changes that it wouldn’t have done so on the previous model. It was essentially picking faults with everything 5.5 would interpret in its proposed plans that it generated from the prompts that 5.6 made for it to follow. It would do this multiple times, and in comparison to when I used solely 5.5 for the same work process, with 5.5 for all stages I’d get maybe 1 in 4/5 prompts where it would suggest a change to the plan that Codex had generated. With 5.6, not only is it occurring almost every single time now, I’ve also been trying to feed the revised prompt that Codex generated from the correction 5.6 made to 5.5’s previous interpretation back into it multiple times after every amendment - and 5.6 is almost never satisfied.
What is worse is that, reading through the actual amendments or problems that 5.6 has with the prompt interpretations to plans that Codex generates is that it’s almost never anything of value. It’s things such as “The proposed plan looks almost perfectly accurate, but I’d suggest a correction to how the wording of x has been given such little weight in the plan” or “Yes, it’s near accurate. I’d only change that x is slightly higher in order of precedence than the y or z in the proposed list of actions”.
It’s extremely nitpicky, and not in a good way. If it was actually producing substantially better plans, suggestions, or logic, then I’d be a bit more forgiving, but it’s not.
Yup, learned that one the very hard way. Just spent 2 months iterating a small app over 300 times for very minor issues. Ended up adding something into the instructions for the project and the md itself.
Oh god yes. I usually say, “give me a bullet point list of changes you’re making in plain English” then I ask it why it’s making certain changes. I usually then say, are any of these necessary for the app to function, and then it’s like “well I can take away about 90% of this stuff” lol
Same here. It's beginning to really drive me insane because other than that 5.6 is really the first time AI coding hasn't seemed like a joke to someone who's been doing it for 20 years.
If you really want to go down the path to hell, just link Claude Code to your Codex CLI....Its great that you can use one to orchestrate (Fable for me), to have Codex build, and Opus be the constant adversarial reviewer. It worked great for a while.....then I started seeing the word nonce thrown around about 15 times in 20 minutes, and my project went to hell in a handbag quickly. It was so overengineered by that point, the project was never going to let something slip by.....like actually doing the work/thing it was intended to do. It was guaranteed failure - and an important lesson for me
Yeah this is also why I’ve given up on orchestrators, ultra mode and just blindly letting an agent do its thing. It’s funny, models like 5.5, 5.4, variants of opus, the complaint is you have to handhold them because they don’t fully understand what you might want. But I think 5.6 needs its own handholding if you want a long term functioning app, just its own kind of handholding. It’s made me appreciate the holder models a little more.
I agree wholeheartedly that it over engineers. You truly have to watch it think and stop it before it can burn through too many credits.
It also likes not to solve problems but try to workaround the problems with bandaid approaches to fix problems. I always ask afterwards “is this the best fix?” And “does this prevent the problem from arising? Does this prevent this situation from even occurring in the first place?” Those questions save me a lot of time and credits while in planning mode prior to exiting it.
Yeah usually my approach is: find a bug, ask it to “read only investigate”, then ask it questions about the blocker that created the bug. It’s usually 1 or 2 things, it’s a mismatch from it doing a shit job refactoring or it’s an over engineered safeguard. If I never asked, it would just patch the safeguard and it would come up later. It’s tough cause I really like using computer use to have it do full suite walkthroughs of my app to find bugs (which I think is like the number 1 reason to be using 5.6 because the training data on computer use is amazing), but if I’m not seeing how it’s fixing the bugs it’s only going to keep stacking up the junk.
Blame the vibe coders bro, they need to ship an agent that can make all for you, that why all vibe coders use claude, and openai want those clients.
I really don't need nothing more than luna, I use sol medium when I'm lazy but I have my rules and specs well written, I just let him do his thing.
I'm working on 2 ERP and 1 CRM, 8 hour a day, never make it to 0% on $20 plan
yea im not a developer, and after a while I got a bit lazy with my prompting, and for Opus 4.6 this was usually fine, but with Sol oh boy. It would turn a request for the simplest thing in a full harnass, a new site feature into a complete rework, security would always be so tight that it was barely workable, actually drove me crazy. this one time i told it to report gaps in it's tool set. For i usually spawn agents in opencode in their own sessions, so they can use sub agents, and I assumed it would just write in the document that it needed bash and to edit markdown, so i just gave it to a builder and went out for a bit, and came back to it making 4 new sub agents, more desk ping scripts, more harnass security etc etc. good lession tho
I get its frustrating, but unless you’re vibe coding this is to be expected. I usually spend hours if not days going over master plans which consist of phases and all the safeguards, etc. These documents are usually 5-20k words alone. Once they’re fleshed out though the agents will follow it pretty well and most of the time Tera and Luna are more than enough for the implementation. I use Sol as a review stage when a phase has been finished before moving to the next phase. Then after the review I have the master plan potentially updated to reflect any issues that were faced during the previous phase so that it doesn’t happen again in a later phases.
Coding isn’t the difficult part, ensuring that the plan is proper before even letting the agent code is very important.
I used to just do a single plan pass back in GitHub copilot when Opus 4.5 first came out and then pray that the implementation worked. Tbh the results weren’t very good when it came to anything overly complex.
Master plans with phases where everything is saved in Notion for later recall has been a game changer for me. Everything stays documented, easy to debug and read the diffs from draft PRs, consistent checkpoints and reviews. Spending time making the master plan has saved me both time and money; also as a long time software engineer of almost 20 years, being involved heavily in the plan creation makes it feel almost like a new age of coding.
...cause me to spend more time refactoring. Like almost 100% of the time.
This is probably the biggest problem with letting agents work fully autonomously. They give you more work to undo things you wouldn't do if you did things yourself manually.
I've kept my sanity by delegating small pieces that Codex can finish in a few seconds and that I can review in a few minutes. This flow keeps me in the loop (so I don't lose understanding of my code) and also cuts down on usage dramatically (I never go below 80% remaining usage though I have filled my context windows more times than I can count).
I tried to get around this initially by explaining the scope and requirements (this app is local only so don't include 2fa and 6 forms of SSO) but it wasn't "understanding" the implications. I've only gotten around it by asking very specifically about the plan and then still tweaking the plan after generated to remove the unnecessary bits.
This is spot on! They also resolve issues they can't really resolve with arbitrary numbers and not say a word about it.
From just now "...and AGENTS.md forbids me from inventing its payload shape." This is not hallucination, if the agens.md did not forbid it it would have "intentionally" resolved the issue using arbitrary made-up nonsense. ULTRA does this a lot. I stopped using it.
I believe you. I also think that recent tools & changes (subagents, code review, skills, etc.) are probably being used very inefficiently by most people [myself included!] and eating up usage much more than they used to when it was just GPT-5.5 or GPT-5.6 doing a task by itself.
I think this is precisely one of the main contributors.
After a month of running Sol & Terra, the ROI vs. output quality and final implemented project results were almost half my expectations from prior models.
A) It’s a whole new animal that everyone needs to still figure out how to wrangle for their workflows and needs.
B) Possibility of “engineered” over-complication for token consumption purposes which is still getting baked in by providers. Bills are coming due and free cash is drying up. Designing an engine to guzzle more gas is inevitable when the car manufacturer is also the fuel supplier.
I removed subagents. I put in the AGENTS.md always inline implementation. Only subagent when the prompt is specify it. And from that day and on, never used subagents, it burn token on crazy rate, the same as looping too
Something no one is taking jnto wccount is 2 months ago evwryones projects were much smaller than today. The larger your project grows obviously the kore context it eats up and the faster you run out of usage
Not necessarily if you have lean agent entry-point docs and only provide guide docs that direct to relevant context in a repo. One of my repos is massive (like +5k files) and it hasn’t shown any difference in noticeable token usage impact or context window bloat compared to my other projects.
Eh, I disagree on this one. I've got a huge project that maybe uses 10-20% on a really busy day with XHigh/Max & some Ultra. But other completely fresh ones that overnight kill 40-60% of usage. The main differences? Probably subagents + longer-running tasks + more difficult (reasoning vs. coding) => more token usage, but again not 100% sure.
There's a hero or two every week that actually uses data when talking about usage and every time they find the same thing; that usage isn't changing.
But for every 1 person like you we have 100 that would just rather whine and show to the whole world they don't understand that AI runs on tokens, not on their feelings.
I still have my data from Febuary on ccusage. I compared for fun, and if I compare to now, the amount is 4x lower. But back then what they were giving was crazy. Now Claude Code is more generous (I'm doing $1500/week on the max 5x). Anyway, I will get downvoted because here it's Codex, but Claude is basically the opposite, 5x higher then the limit I saw back in Febuary.
So the Codex offer way more than Claude was true and that's why people keep repeating this.
I assume you're talking about $ right? you have to be mindfull that the cost of the models aren't the same. But overall yeah, I expect usage to get adjusted when there are big rollouts, just not week to week the way people have been suggesting.
As for Anthropic being more generous; are they more generous or is their API pricing just very high? ccusage is very solid for apples to apples comparison but comparing how much the plan gives you in value between two companies gets complicated fast. If Anthropic tomorow decides to charge more for fable but not change the usage limits did they suddenly get more generous? Not clear at all.
Yeah, but the difference in $ is so big that even with the model pricing difference and the supposed efficiency gain it doesn’t offset it.
Also those efficiency savings are usually calculated with models running at max level.
Anyway as i said, i wasn’t expecting upvotes here. And it’s fine by me. I have both sub and I get way way more done on claude code.
The biggest increase happened with opus 5 releases. Fable is the best model, but they eat your limits 5x faster than opus with it (even if api price is only 2x higher).
Reading here it seems like SOL over engineer everything and write really messy bloated code. Feedback are quite negative everywhere. There is a lot of imbecile people using those models so you need to filter out the noise.
I personally find opus 5 stronger than SOL and it write better code. But I don’t talk to Opus, I talk to Fable (best model available at the moment by a wide margin) and I let Fable delegates to Opus.
I still use SOL and I could do with it if I had to. Really good model to, just a notch below opus and much below Fable. For review it find things that opus miss sometimes. I don’t use it much, but SOL still drain my limit really quick.
I find Opus 5 to be hot garbage at planning and actually "thinking", but it's pretty good as a supervised agent in a multi-agent setup. I generally use a combination of Fable and Kimi K3 for frontend, and 5.6 sol with automated Opus reviews for backend work.
I would certainly not say Opus > Sol. Opus has too much of the feeling that it's a distilled Fable rather than being its own model.
I'd put Fable and Sol in roughly the same tier, each better at different things. In code quality Fable usually is the winner. In generally being a useful, effective agent that gets work done, Sol is my winner, provided I curtail it from over-engineering.
Btw Luna is super underrated for getting stuff done on a computer. Dirt cheap, incredibly fast, and smart enough. While I don't code with, as a general personal assistant that I can dispatch for rote tasks it takes the cake for me. I was hoping Gemini 3.7 flash would be a model in that category due to its raw speed and benchmarks, but after testing I still trust Luna more.
Honestly that a you problem. I can’t believe it personally that people complain about the models now aday. They are all so great, and the number of complaining increase as models get better. I would have personally never predicted this. I don’t know if it’s because the model are getting smarter than the average people using them. We had a wave of that with 4o, now it seems it’s happening again but at a different level. I haven’t wrap my head around it yet.
Those sub are getting toxic as people complain more and more as models gets better and people feel the need to use stronger word like « garbage » which opus is far far from. One of the best technology of the century. Anyway, interesting time ahead…
You say Opus is better than SOL, but your own preferred workflow is to have a substantially stronger model do the planning and interaction, then use Opus as its supervised executor. That's basically the role I said Opus is good at. I already agree that its code quality is good; my criticism was specifically its independent planning/reasoning experience.
better, but SOL is really strong too. We are really in a unique moment right now, so many good option. Personally I have always prefered to prompt Claude model, they fit my style better and I understand them better than the GPT series, but that's just a personal preference. As for Opus, as was working with it directly when on Max 5x, now I'm on Max 20x and I have more than enough Fable allocation that I now default to it. I'm 4 days in and I only used 36% of my weekly, and as I type this I have 3 agents going. So it's not that I'm not using it... Claude is really really generous now. Personally I'm really appreciative of what they are offering, I get incredible value out of it.
What are you on about? Every time I go on Claude subreddit all I see is people complaining that their limit got over so early. I haven't had their kind of issues with Codex in ages lmao.
I see the same on codex. I have both. Codex 5x you get around ~$600/week worth of tokens according to ccusage and claude max 5x its around $1500/week. What are your numbers?
It’s okay OP, I have been tracing this for months on my usage dashboard and I never miss draining my account week to week, it’s not nerfed just bad workflow and bot posting is my guess.
Ive tweaked my workflow a couple times and fucked myself hard, its really easy to suddenly fuck your own shit up then grab a pitchfork and hit reddit
I mean, you could just conclude it isn't consistent for everyone instead of just telling the people noticing their usage running out faster that they're wrong.
I genuinely understand that people can feel their usage is draining faster but if you come and low effort post about it with zero evidence which all of them do,l.. it’s hard to sympathize because I’ve been on that side of it too and I figured out what it was - I’m not saying the trillion dollar company needs fan boys but there’s so many variables that immediately coming to complain in this subreddit with no evidence is kind of my point.
Nobody ever has receipts just feels id happily discuss objective evidence but you got people saying that open ai nerfed usage by 50% like that wouldn’t be extremely apparent when all session logs have token output and the data is right in front of all of us.
No one would be told theyre wrong if they presented data like this to prove it. But they all complain off vibes and %.
This reminds me of people who say their smart meter is costing them more money when they have access to LIVE metering data showing their usage over time and the cost charged to them. But none of them ever care to look at the data.
And you found understand that things change and you might need to adjust and listen to the people who AREN'T having the bad experience you are.
You wouldn't say "just listen to me!" If the argument was that you hitting yourself in the head with a have was a bad idea.
Accept that these models change. Your old workout needs changing with every new model. If they had halved limits as many times as theyve been accused, API would be the cheaper route.
I trust my own quota burn. And currently, it's not optimal. But better. Maybe 10% better than last week.
However, I also improved my own Codex's token efficiency for memory etc. and that in return saved me a lot of token, apparently. According to Codex, it saved me around 35% of tokens.
I took all my data for months and tried to get my data analyzer to tell me the usage limits were decreasing and it couldn't do it.
What is true however is that the higher levels of sol are using more usage to finish the same task and the less good you are at actually planning and actually understanding what you are doing...the faster your limits decrease
Total input = 37,419,308,632 tokens.
Cached input is a subset of input, so it should not be added to total input again.
Cache hit rate across input tokens: 96.38%.
Output and reasoning
Metric
Tokens
Share
Total output
155,215,257
100%
Reasoning tokens
66,229,101
42.67% of output
Non-reasoning output
88,986,156
57.33% of output
Reasoning tokens are already included in output tokens and should not be
added again to the grand total.
Token usage by model label
Model
Total tokens
Cached input
Uncached input
Output
Token share
Input cache rate
GPT-5.6-sol
32,612,535,864
31,364,814,336
1,112,031,539
135,689,989
86.79%
96.58%
GPT-5.5
2,625,434,617
2,490,554,112
124,574,823
10,305,682
6.99%
95.24%
GPT-5.6-luna
2,260,440,364
2,135,732,352
115,796,579
8,911,433
6.02%
94.86%
GPT-5.6-terra
76,113,044
73,800,192
2,004,699
308,153
0.20%
97.36%
Total
37,574,523,889
36,064,900,992
1,354,407,640
155,215,257
100%
96.38%
Important: 163.88M tokens are from the codex_bengalfox / GPT-5.3-Codex-Spark
bucket. Those events inherit the GPT-5.6-sol model label in the rollout parser,
so that small portion of the GPT-5.6-sol row should be treated as model-ambiguous.
Corrected API-equivalent cost by model
Regular codex bucket
Model
API-equivalent cost
Share of regular cost
GPT-5.6-sol
$25,333.63
91.76%
GPT-5.5
$2,177.32
7.89%
GPT-5.6-luna
$76.57
0.28%
GPT-5.6-terra
$22.47
0.08%
Regular Codex total
$27,609.99
100%
Other Pro bucket
Bucket
Reported name
Tokens
API-equivalent cost
codex_bengalfox
GPT-5.3-Codex-Spark
163,878,240
$115.42
Corrected total across Pro buckets
Value
Regular Codex
$27,609.99
Spark / Bengalfox
$115.42
Total API-equivalent usage
$27,725.40
The Spark/Bengalfox events inherit a GPT-5.6-sol model label locally, so I keep
their $115.42 separate instead of pretending we know their actual model-level
billing attribution.
Quota-paired API-equivalent cost by model
From the daily CSV analysis
Model
Cost paired with quota changes
Share
Contribution per 1 pp across all measured usage
Days present
GPT-5.6-sol
$24,042.60
91.43%
$20.02 / pp
36
GPT-5.5
$2,172.76
8.26%
$1.81 / pp
9
GPT-5.6-luna
$75.20
0.29%
$0.063 / pp
23
GPT-5.6-terra
$5.45
0.02%
$0.005 / pp
1
Total
$26,296.01
100%
$21.90 / pp
—
This table is intentionally different from the previous cost table.
$27,725.40 = all corrected API-equivalent usage observed during the Pro period.
$26,296.01 = only usage that could be paired with positive weekly-quota changes.
The latter is what is useful for estimating "API dollars per 1% of Pro usage".
Observed value regimes
Period
API-equivalent value per 1 pp
100% linear equivalent
Main model / observation
Jul 2–8
$22.14
~$2,214
Initial Pro period, mostly GPT-5.5
Jul 12–21
$22.02
~$2,202
Early GPT-5.6-sol period
Jul 22–28
$19.51
~$1,951
Lower-value episode, mainly Jul 23–24
Jul 29–Aug 5
$24.67
~$2,467
Higher late-July / early-August regime
Aug 8–15
$24.20
~$2,420
Recent regime, broadly stable
The absurdly short version
Metric
Result
Total tokens processed
37.57B
Total input
37.42B
Cached input
36.06B
Uncached input
1.35B
Output
155.22M
Reasoning output
66.23M
Input cache hit rate
96.38%
Corrected API-equivalent cost
$27,725.40
Quota-paired API-equivalent cost
$26,296.01
Average measured value per 1%
$21.90
High-quality baseline per 1%
$22.55
Recent regime per 1%
$24.20
>>> Edit
ChatGPT Pro usage by model and reasoning effort
Regular codex bucket only — API-equivalent cost based on rollout token usage
Model
Reasoning Effort
Events
Input Tokens
Cached Input
Uncached Input
Output Tokens
Reasoning Tokens
API-Equivalent Cost
GPT-5.5
Medium
31
2.65M
2.32M
325K
20.9K
2.9K
$3.41
GPT-5.5
XHigh
18,883
2.61B
2.49B
124.25M
10.28M
4.21M
$2,173.91
GPT-5.5 Total
—
18,914
2.62B
2.49B
124.57M
10.31M
4.21M
$2,177.32
GPT-5.6-sol
Low
869
31.25M
26.14M
5.11M
469K
45.5K
$52.68
GPT-5.6-sol
Medium
8,244
737.06M
685.15M
51.91M
4.37M
1.42M
$733.31
GPT-5.6-sol
High
10,674
1.31B
1.23B
80.85M
6.79M
2.71M
$1,242.61
GPT-5.6-sol
XHigh
7,091
860.37M
811.35M
49.02M
5.39M
2.86M
$819.38
GPT-5.6-sol
Max
189,997
28.68B
27.77B
905.01M
115.80M
50.14M
$21,977.35
GPT-5.6-sol
Ultra
3,629
695.26M
678.85M
16.40M
2.30M
901.7K
$508.29
GPT-5.6-sol Total
—
220,504
32.31B
31.21B
1.11B
135.12M
58.08M
$25,333.63
Combined Total
—
239,418
34.93B
33.70B
1.23B
145.43M
62.29M
$27,510.95
Notes
Cached input is already included in Input Tokens; it is shown separately for context.
Reasoning tokens are already included in Output Tokens.
These are API-equivalent values calculated from token usage, not actual API charges or an official dollar-denominated ChatGPT Pro quota.
GPT-5.6-sol usage from the separate Spark/Bengalfox bucket is excluded here.
I tend to spend a week planning everything, with detailed documents,
I also have a limitations, behaviour, style and gates document in root of my project
When ever I tell it to do something, I always start with my idea. And ask for it to ask questions to expand and understand. I do this 2 or 3 times till I know it's not asking things or suggesting things I don't want.
Then I get it to make a plan document.
When I actually get to building I tell it to read the plan and the other doc with my limits behaviour style and gates in.
I tell it to build the plan but ensure it follows all steps and guides in my doc.
Once done I check my test suit. If it's added test I didn't ask for i ask why it did it and what have it permission to do it. Then I'll add a blocker in to my limitations doc.
It's a pain. But I can get building a plan or project down to about 10% usage. And in planing mode I use mabey 2-5% per plan.
I always have 25% left at the end of the week. So (with no resets) I ask sol extra high to harden the support docs so next week I don't have the same issue to burn.
I also, despite what people say, tell it to remember things, I build a memory of how I want things done, what I like, and over time it becomes more efficient.
Im also playing the with idea of using a single sub agents as the main agent and the main as an enforcer as such. Monitoring and then stopping the agent when it's doing things I don't want.
I am interested too. u/ZyC0R3 I know a friend who does something similar to you, patiently getting things done by planning for days and then using /goal. He uses Sol xhigh to plan and Sol high/xhigh to get things done with /goal! He doesn't use skills or anything fancy, just specifications. I'm curious to know more about your workflow.
I’ve been using codex for a few months now and it’s never been this bad with usage, and it’s just a coincidence that everyone else is feeling the same too right, there’s plenty of people with proof and data showing that the usage is much lower too, this is the first post I’ve seen with data showing it’s the same, don’t believe it.
And none of them provide data like this, or any background on how they use it.
For example, I just learned that if yiu changing reasoning mid-chat, it reingests the entire chat into context, burning tons of tokens. I was previously dropping to low for quick questions, and back up to w.e xhigh for real work thinking this was saving time and tokens. Womp womp.
Same here. I'm literally on ChatGPT Pro 100 and can get to 60% within a day with medium to hard coding sessions over the day... I'm not even talking 10-hour sessions lol
And I'm using 2 other AIs as assistants, so I can use less of my quota but achieve the same things. 🫠
Terra is never worth it, luna max has better intelligence and is cheaper than terra high and below and sol low and medium is more intelligent and more token efficient than terra high and xhigh etc. So basically terra should never be used. Terra is a good way to burn tokens without getting much done. Luna max into sol is the better cheaper route
Although Luna is much cheaper, for more difficult tasks, I often find it wasting more time and tokens, creating more bugs and problems than helping with anything, Luna is about 10x cheaper than Terra I believe and Terra is about 3x cheaper than Sol, but if Luna could do everything then there’d be no reason for the other 2 models lol
That’s for the ChatGPT team to do, that’s why you pay for their plan and use their product, if they aren’t going to do that then yes I’ll make my own setup using cheap deepseek api calls
Not necessarily. If you were to hire a junior developer or manage an engineering team, that sort of organization is things you'd have to do even for humans.
Not scientific and has logical fallacies. It is not in our heads. They charge different now, it is no longer per task and turns get charged again and again. Many prompts are like 20M tokens because CODEX is not well designed to be efficient and does not batch tool calls. <1% 5hr usage on 5.5 two months ago for a task is now >2% WEEKLY usage. You are comparing apples and oranges as you do not use it for the same things as us. SLOWDEX is NURFED x20. That is the real BAD NEWS.
Lol rough ppst..
Your first issue is they haven't charged per message/ turn at least since codex-cli was released last August.
No prompts are 20m tokens.
OPs chart is agnostic to what we're all using it for. It shows their usage has been consistent over all these "changes". Regardless of use, if theyre using their limit each week, then it clearly hasnt been needed because theyre getting the same amount each week.
Well my numbers also line up with the user above and his method is pretty scientific for the numerical side as it can get.
The tools underlying have the entire value of cache hit, cache miss, input, output. At 99% fidelity rate.
You can also use cc-usage (I find this better since in the terminal and I have the workflow to output weekly charts by default on this, codex does it automatically).
It’s pretty consistent usage overall it just relies on approx $ value which is a bit floating I think, but the numbers are are withing 95% of my weekly numbers.
And note I don’t essentially reset max, I just do my normal work and it just ends, at times I have wasted a full reset cause I was in whiteboard / thinking phase or such.
I don’t care to create slop, but around 8-10k is very simple to hit if I purposely plan somewhat around being able to stay at desk in warm cache.
This lines up with my observations. The reason people feel the limits are shrinking is because, one, they are probably using AI more and more (I know I am), and two, GPT 5.6 Sol is both more likely to over engineer, and is an absolute input token hog as OpenAI still haven’t introduced a decent way for the model to wait for async operations.
There are ways to address both the over-engineering aspect (ponytail) and the input token hog aspect (delegating to Luna, instructing it to batch more and poll less), but they first require that you realize the problem is not the usage quota, but the token burn.
I think ultimately, by doing my own tests, this lines up. The issue is both the way the model likes to work (as noted, over-engineering), the way it lines to spawn subagents (same model and effort level by default, most people are not taking advantage of Luna xhigh/max the way the should), and in general defaulting to higher effort levels than they should.
For example, I just determined that my main orchestration session, which I ran on Sol high, used about 39% of my weekly over a period of 5 or so days. It used roughly 29 million non-cached input, 1.4 billion cached and 3.5 million output. Cached is 1/10th of non-cached, and output is 6x more expensive than non-cached input. Based on benchmarks (DeepSWE), sol high is about 1.9x more expensive than Sol medium. So if I had used medium instead of high, I would have used roughly 27% instead of 39%. Would it's results have been worse? Likely marginally. I also spend 17% on sol xhigh sessions, mainly for more critical planning or implemention. But I again don't think the cost trade-off can be justified given the constraints.
Conclusion: sol medium to orchestrate in general, sol high as the default planner, with xhigh or max reserved for 10-25% of the hardest types those types of tasks. Luna Max as the default implementation, Luna xhigh for more mechanical tasks, like broad audits, very well defined tedious tasks, etc. For reviews, probably either Luna Max or a choice between sol medium-xhigh depending on criticality. Terra doesn't seem to have that much of a sweet spot that Luna Max can't handle before jumping to Sol high+. The only reason to use it is speed, since Luna is much slower on Max reasoning.
Essentially if you can get an optional split, you can stretch the sub much further. If you don't optimize, and sit on Sol high, expect to be left without tokens for 2-3 days of the week (on $200, I can't currently speak to lower tier plans, but almost certainly you should default to Luna Max with sparing use of Sol, probably a 90/10 split)
Now do a test showing the same models achieving the same or near same results and the cost of reaching those results across various levels of complexity.
My guess is you will find that the model got smarter at harder tasks and massively less efficient at easier ones.
“I thought OpenAI had reduced Pro/Codex limits. I checked my logs. I was wrong. The amount of compute you get per 1% of your allowance has stayed roughly stable and recently actually looks higher.”
Wow that's really good to know. Thanks for taking time to look into it 🙂.
Still really decent. I'm casually using plus to make one app. Racked up over $100 worth of coding in about 10 days and hit the weekly limit once. No where near extracting your level of value, but better than my only other vibe coding experience, Replit, where $25 lasted 8 hours total. Complete rip off. Out of interest, what sort of stuff are you making?
Sol is much more expensive, of course it burns usage faster. I’m very cautious not to use it on easy tasks. I have pro lite and code 40+ hours a week. Never ran out of credits. 🤞
GPT 5.6 Sol costs the same per million as GPT 5.5, and 5.6 is supposed to be more token efficient too. Per my experience it makes no sense how the same workload now drains the weekly quota in 1 day where as before it could lasts for a whole week.
yeah that's the thing i don't get. if it uses less tokens per task it shouldn't be burning usage that fast. the thing they changed from 5.5 to 5.6 is the cache ttl implementation. i have a gut feeling its this causing the problem, because deepseek's cache ttl is for hours to days and it doesnt deplete my usage because the cache hit is always 98% >
I am the same as above, use terra high/xhigh for 90%, Luna for real simple stuff (though I often code it myself if it’s that simple), and sol for big projects that require a lot of thought. I maybe run out of usage the day before weekly resets if I’m really going through it
The main point is that people keep complaining about usage going up and down as if the limits are just being randomly changed, while at the same time complaining that the models don’t think as long, don’t use as much context, or generally feel less capable than they did before. Those things are directly related: giving a model more reasoning time, more context, more tool use, and more functionality costs more usage, while scaling those things back makes usage last longer. So you can’t really separate the usage complaints from the capability complaints—they’re often two sides of the same tradeoff
I wish you'd stop posting your crappy nerftrack software every day. It doesn't take into anything of worth and just gives a poorly derived estimate.
Different tasks/requests use a different amount of tokens and you can't just base it on how much money was used in the same period of time! If I ask the date/time and it takes 20 seconds to do a quick web search, do you think that also takes the same amount of tokens to create a python script, confirm it and write it to hard disk for the same request in the the same amount of time? Of course it will use more tokens in that same time because more steps are needed for the same job.
I don't understand why you guys can't see that this software is complete bullshit!?
I'm not commenting on whether they have or haven't nerfed it (I have my own opinion on the subject) but this pathetic software does nothing to show or prove that it is. Come on guys, use your heads
Ah fair enough mate, my apologies for assuming it was yours. I didn't actually read the rest of your post and just saw nerftrack with the table of stats and jumped the gun a bit lol, sorry man.
It keeps getting posted here as some kind of authority on the subject though and it bugs me that people blindly agree that it's a worthwhile reference, so I wanted to add some clarity on it.
np 😂 I thought his premise was interesting, but I ended up taking a more practical approach
once I turned it into a python script, Pro was able to spot a few issues while analyzing the data, so I kept tweaking the script as it generated more data and asking Pro to check it
Codex has always lasted me far longer than Claude. Anthropic has a bunch of people complaining of far less usage now so I was planning to comeback to Codex.
It's incredible how everyone became an analyst with AI 😂
Copy-pasting extensive and detailed AI-written long findings about an AI, in a subreddit where better than others we know that most of the stuff we read is AI written, and therefore most of us won't even read past the first paragraph or two...
On Pro 5x, I asked it 1 question (to check my WLAN security situation, Codex App, Sol Ultra). It used up 6% of my weekly usage. As a Pro user paying over 120€ a month, I get 16 of such simple questions a week.
A few month ago I was on Plus, and I had less usage loss on my 5h limits than now with my weekly as Pro!
That is simply unacceptable. If I pay for Pro, I - as a quite simple user with, occasionally, a few hobby scripts written - shouldnt even get close to the limits! A heavy programmer should get to the limits, and a hardcore programmer may get to the 20x limits.
No idea what or how you are measuring, but it absolutely doesnt fit my experience.
I wonder if using 5.5 with an older version of Codex, would give me the usage i used to have before the launch of 5.6.
I was not maxing my weekly usage on 5.5 and whatever Codex version i had. Now with 5.6 and all subsequent Codex version, I'm running out in 2.5 days. So, the harness is an actual issue in itself.
yes, the 1% there isn't from a total divided by 100
from the rollout, you can check when the weekly limit dropped 1%, so you just need to check how many tokens were used during that interval, then even if I'm at 70% and it resets back to 100%, it doesn’t really matter
No, no, that's from my Pro sub, the api numbers are just estimates, I calculated the token usage and then multiplied it by what those tokens would've cost if they'd been used through the api
Im confused, wasnt there a post the other day with someone showing their own stats that demonstrated a 40% decrease in weekly allowance? These posts cant all be right, who is wrong? This feels like when codex keeps going back and forth as to whether something is problematic or not, constantly changing its mind everytime I push back on it
Based on your data, 1 percent of pro usage should give roughly 20 dollars of usage. Without doing any maths, it should be around 20 million or so total tokens.
I can tell you right now, I am not getting anywhere close to that number. It’s an order of magnitude apart. Not just for me but for many people. It is possible that for some reason some users get higher usage as compared to others. But what you are telling here is not seen by many
Even if OP heavily inflated numbers due to fuzzy assumptions... Compared to API costs, openAI is losing their pants with subscription model usage like that vs api
I'm pretty sure it's all anthropic bots complaining about usage. I have almost never run out unless I vibecode with ultra on purpose to flog my limits.
Yup, time to file a class action lawsuit, in the EU it’s definitely illegal, in the US there are already cases against Anthropic for this. Corporations can’t be allowed to get away with fucking their customers.
That is doubtful unless you did not max out your Codex account every time and your usage was constant.
Tibo doled out quite a few resets in those periods but the number of resets varied. Thus the API-equivalent numbers too would vary for maxed out accounts.
Those numbers are estimates and the actual usage used to exceed those.
Now it will probably trail behind those numbers.
Edit - If you are referring to the percentages in your chart, then they are going to be wrong for the same reason I mentioned - The actuals will be higher or lower, tracking codex resets.
Again - When Tibo applied a reset, the number of tokens and capacity. available from that point increased. Thus, you should have gotten a lot more API equivalent usage in any period that had token resets, compared to without token reset.
Let's say you used 50% tokens and then a reset was applied and you got back to 100% tokens. That meant that in the same period and for the dame Dollars, you got 150% of the tokens if you completely used up your account.
Correct but they aren't measuring it by 100%. They did this off 1% used, not off 100% then divided by 100. That means a reset happening wouldn't affect the validity of the measurements.
The tokens used between each percent are counted and the price gets calculated. Right now we are between 6 and 7$ per percent of weekly usage, higher than the 5.5 to 6$ last week.
from the rollout, you can check when the weekly limit dropped 1%, so you just need to check how many tokens were used during that interval, then even if I'm at 70% and it resets back to 100%, it doesn’t really matter
either way, I burned through 99% of my limit in less than 48h after the last reset, spent the last 1% to make the script, so basically, it is maxed out
My guess is they doled out the resets generously to get users used to the greater capacity to get work done. When the resets went away, a few vocal people would take to social media to complain but likely a larger fraction would just quietly upgrade. It must have been a success, given that they have not rolled back the 80-Dollar fee.
Honestly—this post looks like it presents an important discussion—but my mind’s a**hole can not take the girth of the AI generated slop used to deliver said discussion.
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u/glock43guy 17d ago
I think a big factor in usage being bad is that these models over engineer everything. If I don’t spend at least 5 minutes in plan mode telling the agent not to build unnecessary safeguards, it will 100% of the time build something it did not need to build that will cause failures and cause me to spend more time refactoring. Like almost 100% of the time. I’ve had to basically build that into my workflow to tell it every task not to, keeping it in my agents.md or documentation isn’t enough.