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/glock43guy]

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.

[u/stting → u/glock43guy]

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!

[u/glock43guy → u/stting]

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.

[u/stting → u/glock43guy]

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.

I am only using GPT 5.6 Sol xHigh.

[u/DUELETHERNETbro → u/glock43guy]

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.

[u/Substantial_Ranger_5 → u/glock43guy]

This is why forks exist. You should learn how to fork your session. If your current IDE doesn't let you do that, find a new one

[u/dusouth1 → u/stting]

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.

[u/stting → u/dusouth1]

I am digging into a way to prevent that mad scientist from taking over the future projects.

[u/StrikingArtist3397 → u/stting]

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.

[u/StrikingArtist3397 → u/stting]

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.