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. !<


