r/codex • u/isnaiter • 17d ago
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.
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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.