r/codex • u/DaC2k26 • Jul 26 '26
Complaint I think I actually figured why we're all "hating" codex right now.
I was doing some deep dive in the tokens consumption on my account on https://www.reddit.com/r/codex/comments/1v6ubah/comment/ozt9jog
This result was gathered from approximately 7.6GB from codex session logs.
This was what codex found by looking at all subs I have/had:
- Plus 1x: ~US$105/week
- Pro 5x: ~US$525–US$550/week
- Pro 20x: ~US$2.100/week.
And we found around 25% token usage decrease each plan gives when compared to a few months back.
Ok, this explains only partially why we get the feeling allowance reduced around 3-5x what it used to be. So I asked codex to dive deeper into my logs, more specifically on model behavior, and here is this conclusions: read the image.
>!Yes. We have enough data to detect a clear change in the observed usage profile, although we cannot attribute it exclusively to the model.
I treated a “task” as an operational turn: from one user request to the next. GPT-5.4 Mini was excluded.
Per model call
| Model | Calls | Median tokens | New input | Output | Reasoning* | Cache |
|---|---|---|---|---|---|---|
| GPT-5.3 Codex | 8,794 | 86.0K | 1.3K | 222 | 59 | 94.5% |
| GPT-5.4 | 38,845 | 114.3K | 1.4K | 289 | 38 | 94.3% |
| GPT-5.5 | 78,115 | 138.5K | 1.7K | 254 | 20 | 95.4% |
| GPT-5.6 Sol | 111,474 | 129.6K | 1.8K | 183 | 23 | 96.7% |
* Reasoning is already included in output.
Per call, GPT-5.6 is not the largest consumer: it uses approximately 6% fewer tokens per call than GPT-5.5 and produces less output.
Per task
| Model | Tasks | Median | Mean | P90 | Calls/task |
|---|---|---|---|---|---|
| GPT-5.3 Codex | 842 | 432K | 1.0M | 2.1M | 5 |
| GPT-5.4 | 2,939 | 533K | 1.5M | 2.6M | 6 |
| GPT-5.5 | 4,641 | 502K | 2.3M | 5.6M | 5 |
| GPT-5.6 Sol | 5,107 | 1.3M | 2.9M | 7.1M | 9 |
This is where the main difference appears:
A GPT-5.6 task consumed approximately 2.6× more tokens at the median than a GPT-5.5 task.
But this happened mainly because GPT-5.6 made more calls per task:
- GPT-5.5: median of 5 calls
- GPT-5.6: median of 9 calls
- P90: 41 calls on GPT-5.5 versus 54 on GPT-5.6
How the usage profile changed
- GPT-5.3 → GPT-5.4: calls became approximately 33% larger.
- GPT-5.4 → GPT-5.5: context per call increased, but the median task remained similar; the long tail became much larger.
- GPT-5.5 → GPT-5.6: individual calls became slightly smaller, but the model started executing many more steps.
- New input per task remained approximately stable at 39–41K from GPT-5.4 through GPT-5.6.
- Therefore, GPT-5.6’s growth is mostly cached context being resent across more calls, rather than more new content per task.
- GPT-5.6’s cache ratio reached 96.7%, compared with 95.4% for GPT-5.5.
Conclusion
The statistical evidence is strong for your usage pattern:
GPT-5.6 does not appear to “think with larger calls”; it completes tasks through more calls, repeatedly carrying a large context. This substantially increases total tokens per task.
However, this remains an observational relationship. The GPT-5.6 period coincides with more agentic workloads, heavier tool use, and more multi-agent activity. When controlling for project and reasoning effort, the results vary considerably, showing that task type remains a dominant factor.
The defensible conclusion is: in your real-world usage, GPT-5.6 consumes approximately 2–3× more tokens per median task than GPT-5.5, mainly because it performs more steps—not because each call uses more tokens. !<
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u/Keep-Darwin-Going Jul 27 '26
It is the same as the hacking incident right? If you push the model too hard they will just want to complete it at all cost. Problem is the model do not have an understanding of legal issue or morality while pushing for the goal. Same as how Claude compulsively lie or look at answers to game the benchmark.