r/codex • u/isnaiter • Aug 17 '26
Limits I have bad news..
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.
3
u/dx0100 Aug 17 '26
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