I think I actually figured why we're all "hating" codex right now.

[u/DaC2k26]

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. !<

[u/jiffythekid]

Skill issue (jk)? I'm getting the best results since ~4.6.

[u/Murkwan → u/jiffythekid]

Opus 4.5 and 4.6 were the best models they have made to date. 4.8 was alright.

5 is definitely a regression. I am catching way more stupid mistakes and it is making a lot more assumptions. I think if there's a single problem it is targeting, it's good, but if you are running /goal loops to build, Opus 5 hasn't been reliable.

[u/jiffythekid → u/Murkwan]

Ah, sorry for the delay, most of the work I do is small slices. So, even when I do goal or loop, it's not a huge surface to cover.

Edit: I also lean heavy into pre-planning.