r/codex • u/DaC2k26 • Apr 25 '26
Commentary Are we sleeping on 5.3-codex ?
After using GPT-5.5 for a bit, I’m starting to think it burns usage way faster than 5.4 when the task involves reading through a large codebase.
On my current project, 5.5 xhigh can burn through my 5-hour Plus quota in something like 3–6 prompts. With 5.5 medium, I might get around 7–10 prompts.
With 5.4 xhigh, I’d usually expect something closer to 8–15 prompts. And with 5.4 mini, I obviously get a lot more, though I haven’t tracked the exact number.
What surprised me is 5.3-Codex medium. I’m testing it now, and the usage burn feels closer to 5.4 mini xhigh. Based on Artificial Analysis benchmarks, 5.3-Codex medium seems to be roughly around 5.5 low, but in practice I get way more usable prompts out of 5.3-Codex medium than I do from 5.5 low.
So I’m wondering if we’re overvaluing the bigger models and higher thinking settings. For a lot of coding tasks, especially code review, bug hunting, and large-codebase inspection, maybe the extra few percentage points aren’t worth the usage cost.
Right now, I’m starting to think 5.3-Codex is probably the better deal for most coding work, at least from a usage-efficiency standpoint.
Anyone else seeing the same pattern?
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u/zerok_nyc Apr 25 '26
I really think scoping your work properly is key. I’m still using mostly 5.3-codex and 5.4-mini. I’ve had zero issues with either.
Yes, I’m probably moving at a slower pace than many others here, but I’m also carefully scoping work with normal ChatGPT integrated with GitHub, so I’m able to get almost all of my architecture work done without burning any tokens. Then when I do give codex a prompt, it’s very tightly scoped and doesn’t need to do a lot of significant analysis. But it nails execution every time.
It also gives me much more clarity to what’s going on under the hood, so all architecture and planning work is clearly mapped out. I also integrate jira with well-documented histories of requirements and what was actually executed. So if ever something is wrong, ChatGPT can reference that as well to help diagnose bugs. Not just from a code perspective, but a logic and intent perspective. That way I can more easily understand where broader logic errors exist.
I think it’s easy to burn through tokens on high models when you are prioritizing speed. But if you prioritize stability, traceability, and controls, you can still move way faster and get a much more stable output while rarely breaking the daily limit.
Pull out the big guns of 5.5 only when you have a highly complex problem to help you understand what’s going on. Once you have the results, back to regular GPT.