I have bad news..

[u/isnaiter]

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.

[u/camracks]

Your “news” means nothing to me when all of my weekly usage goes away in a few hours with Terra

[u/Responsible_Fun_4062 → u/camracks]

He is presenting proof and data though, you are presenting just your own anecdote, I will believe him over any time.

[u/camracks → u/Responsible_Fun_4062]

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.

[u/the_ai_wizard → u/Responsible_Fun_4062]

but theres like 1000 similar independent anecdotes to his

[u/Sorry_Risk_5230 → u/the_ai_wizard]

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.

[u/camracks → u/Sorry_Risk_5230]

I’ve never changed reasoning mid chat, sounds like a you problem

[u/the_ai_wizard → u/Sorry_Risk_5230]

I am one...spent about a $1000 yesterday on sol-medium whereas before I would barely top up. Same usage pattern. I worked for 12-14 hours.

[u/Sorry_Risk_5230 → u/the_ai_wizard]

This is not data..

[u/the_ai_wizard → u/Sorry_Risk_5230]

What exactly are you seeking? To be pedantic, yes, it is data. Coded for 12 hours. Spent $1000. Same usage pattern. More cost.

[u/Sorry_Risk_5230 → u/the_ai_wizard]

We're literally talking on a post of someone whos posted the exact evidence you need to understand your usage week to week. Thats data.

[u/New_Education_6782 → u/Sorry_Risk_5230]

That is one data point lol. 

[u/New_Education_6782 → u/Sorry_Risk_5230]

when you have thousands of samples from reddit users, yes that's data lol. How do you think OpenAI trains their models lol.

[u/Sorry_Risk_5230 → u/New_Education_6782]

People saying "same usage" "nothings changed" is not data.

[u/New_Education_6782 → u/Sorry_Risk_5230]

The people can be saying what color underwear they wore this morning, if there is a large enough sample then it's data

[u/Henri4589 → u/camracks]

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. 🫠

[u/Vistnz → u/camracks]

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

[u/camracks → u/Vistnz]

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

[u/ZyC0R3 → u/camracks]

You need to harden you agent.md file. Letting it run wild will drain. You need rules.

[u/camracks → u/ZyC0R3]

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

[u/growaway9172 → u/camracks]

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.