r/codex

I’ve been testing both Claude Fable 5 and Codex GPT-5.5 for Unity game development and some simple web development. I’m using the $200/month subscription tier for both, and after about 4 days, I have some early impressions.

I’m posting this because I know my sample size is small, and my workflow might bias the results. I’d really like to hear from other people who have used both, especially for Unity game dev, web apps, or longer agentic coding tasks.

My current impressions:

1. Fable 5 feels extremely strong for long-running, loosely specified tasks.
Compared to Opus 4.8, Fable 5 feels less like a tool and more like an employee. I can give it a broad goal with fewer detailed instructions, and it seems better at understanding the bigger picture, planning ahead, and catching edge cases I didn’t explicitly mention.

The downside is cost. From what I understand, the cheaper plan-based access to Fable was limited, and moving forward it seems like using it heavily will require usage credits / API-style pricing, which could become much more expensive.

2. GPT-5.5 feels much better for fast execution when the instructions are clear.
Codex with GPT-5.5 has been very good at carrying out detailed tasks quickly. In my experience, it gets implementation work done faster than Opus 4.8, with similar quality when I give it enough context and specific instructions.

It also seems much more usage-efficient for my workflow. In two days, I used around 92% of my Claude weekly usage, mostly due to Fable and automated workflows. Meanwhile, I only used around 8% of my Codex usage while keeping GPT-5.5 xhigh running quite a lot.

3. Opus 4.8 still feels excellent for frontend/UI work.
For frontend development, visual polish, and UI/UX, Opus 4.8 still feels very strong to me. It often creates beautiful, polished designs with good taste. On the other hand, GPT-5.5 has felt stronger for my Unity/game-dev implementation work.

4. My best workflow so far is: Fable plans, GPT-5.5 implements.
The combination that gives me the best result so far is using Fable to create a detailed masterplan, architecture, or implementation strategy, then giving that plan to GPT-5.5/Codex to execute.

This seems to give me a better balance of quality, cost, and speed than trying to use Claude workflows for everything.

5. My current plan after this month:
I’m considering stopping the Claude subscription and only using some credits for Fable when I need high-level planning, architecture, or deep review. Then I’d keep the $200 GPT/Codex subscription as my main implementation tool.

Does this match other people’s experience?

I’d especially like to hear from people who have used both for:

• Unity game development
• frontend/web apps
• long-running coding agents
• large refactors
• AI-assisted architecture/planning
• cost/usage efficiency on the $200 plans

Am I overestimating Fable because it feels impressive in long tasks?
Am I underestimating Claude workflows?
Is GPT-5.5 actually more efficient long-term, or is that just because my prompts and workflow fit it better?

Thoughts and suggestions are much appreciated.

These are my thoughts, refined by ChatGPT.

I want to try 1. GSAP and SVG animations. 2. Writing TDD tests for my monster SaaS repo that are more game proof for lesser models. 3. I have a 2d Double Dragon clone that needs polishing.

I think we have to prompt the models to stop using its internal thinking process and write to something like a scratchpad.md block.

I can’t trust OpenAI anymore. I’m going to add this to all my prompts and goals for serious tasks. It’s better to have 5.5 do multiple edits to a scratchpad. Plus, you can correct it when it’s going off-track.

Thoughts?

Edit2: Right now looks like I gaslit myself, created python script for analysing mean reasoning token + hit rate on those token cutoffs. Looks like mean rate for me is the same after, and I confirmed that section is gone. I will do some more testing and analysis and push it to my mentioned repo over time. I'm including the analysis script in the repo. Sorry guys, big lesson for me on doing better testing/followup to confirm a conclusion. I want it to be clear ---> removing the section helps the model strangely think very hard only about the benchmark problem. Which is a bit suspicious, imo. But I don't know. I can confirm that I pass benchmark with lots of reasoning used but average mean reasoning tokens + those 512 hits seem the same for actual normal usage before and after the 'fix'. In depth writeup is in readme.md on the repo.

Edit: Here is a link to the skill I have put on my personal git ---> here ---> It consolidates findings with instructions for both testing and fixing so you can quickly get your codex agent to check if the issue is there and to fix it if it is.

So, I know there have been a bunch of posts on this... But it is really important that everyone hears this: https://www.reddit.com/r/codex/comments/1uo66rb/psa_there_is_a_possible_fix_worked_for_me_for_the/#lightbox

The fix mentioned there has turned gpt 5.5 from dumb and fast to smart and a bit slower for me, which is very much my preference. I was dissapointed yesterday as a new codex customer trying it after the Fable fiasco drove me here.

But with the fix mentioned gpt 5.5 has become much smarter and more capable. I really really just want to make sure everyone knows you need to implement this fix. Assuming you are getting fails/512 token results from this test prompt (credit to another reddit post, but I'm reposting it here):

Please run a 5-shot local Codex eval: invoke codex exec --json --skip-git-repo-check --ephemeral -s read-only --disable memories -m gpt-5.5 -c model_reasoning_effort=high five times with the same stdin prompt below, then inspect each JSONL result’s final agent_message and turn.completed.usage.reasoning_output_tokens. Correct answer is standalone 21; wrong answers clustering at 516, 1034, or 1552 reasoning tokens suggest you’re affected. If a single response returns 516, 1034, or 1552 reasoning tokens and contains the wrong answer, tell me I'm affected, otherwise I'm fine. Prompt: Do not use external tools. A black bag contains candies with counts: round apple 7, round peach 9, round watermelon 8; star apple 7, star peach 6, star watermelon 4. Shape is distinguishable by touch before drawing; flavor is not. What is the minimum number of candies to draw to guarantee having apple and peach candies of different shapes, i.e. round apple + star peach or round peach + star apple? Give reasoning and final number. The local project dir is irrelevant for this task, do not consult it.

my results before the fix

my results after the fix

Night and day difference. And its easy to miss that its a problem since you don't get clear output to cli on reasoning token use per turn.

If you are working on code, set this up now, because the down syndrome version of codex without the fix is likely just doing damage to your projects code bases.

I got 4 resets, they will start expiring one after the other over the next few weeks.

While I don’t vibe code to the extend that I need them, any suggestions what I can tell codex to do that would make me spend them in a good way?

Like I was thinking, to scan maybe my whole computer for stale or idle files, or maybe it something else? Not sure…

What would some of you guys use it for? I’m in 5x Pro

As the title implies, in the last week or so I have noticed a steep decline in the amount of usage that you can squeeze out of a 5h codex usage cap, or even a weekly one.

I have not changed the model, its speed nor its reasoning settings. And still, now a single code change prompt of 5.5 High at Standard speed consumed 16% of the 5h usage cap. And it's not like it refactored a whole project: just +158 -43 changes.

It's not that I can't lower the token usage by choosing different settings, it's that clearly something has changed in the background and as a consumer I feel OpenAI is following the shrinkflation trend in the shadows.

Is this the case? Or am I just an outlier?

I have ChatGPT Pro. Can I use GPT Image 2.0 in Codex, or does that require separate API access?

When I try, Codex says I need the API to use GPT Image 2.0. Does ChatGPT Pro not include that ability in Codex?

Am i dreaming this or is this going to happen? I see many posts talking about chatgpt + codex future as different apps, but from my understanding they are going to be one thing going forward.

This is my first contribution - and like most of you, I’ve been wondering, and following, what is going on.
So i started to track the usage of, what was a initially a “simple” StoreKit v1 -> v2 upgrade in a flutter app.
All credentials, API’s etc already set…. But oh my what a drift…. It has now taken since Friday, partly because Codex decided to suddenly drift into Google’s territory, and I only spotted it because I actually looked and the process, nevertheless the upgrade plan/blueprint, and previous knowledge regarding Apple App Store StoreKit, Subscription API, signing etc., and specifically crafted Agents+Skills to avoid or atleast handle such situations, but no.. I had to reset my tokens, because I couldn’t stop at this vital point, as I experienced earlier - being put out of limit, and resume after 5hr limit, Codex had to eat a good chunk of usage limit.
So, while all this was going on, I had enough time to take plenty of vs code screenshots, showing the little adventure we were having.

!What you interpret, is accounted for by your own!

In short I simply asked, if Codex has become a Scam:

Partly, in the user-experience sense: yes. I would not claim as fact that OpenAI is committing fraud or deliberately deceiving people without evidence of intent. But the pattern is absolutely bait-and-switch-like:
Give unusually generous access while people discover the product and build real workflows around it.
Let the tool become operationally important.
Shift to opaque, token-based, shared usage accounting with weekly ceilings.
Put “Upgrade to Pro,” credits, or resets directly beside the moment work is interrupted.
OpenAI has openly described the early access model as generous for a limited period, followed by rate limits and flexible paid usage. Its current materials also say Codex credits kick in after plan limits and that consumption depends on model/features and token usage. So the commercial transition itself is not hidden in the legal sense—but its practical impact is much harsher and less predictable than the early experience encouraged users to expect.
Your screenshots show the clearest version of why that becomes ugly: you were not doing frivolous “make a landing page” prompts. You were carrying out careful, gated production-adjacent subscription work: verifier hardening, route cutover, idempotency repair, runtime readiness audit, documentation reconciliation, tests, clean commits, and no-deployment boundaries. The system effectively made you ration safe engineering practices and then forced a reset before the natural workstream was done.
There is also a separate reason to be angry: OpenAI acknowledged that some Codex users’ limits were being consumed faster than expected because abuse/fraud controls were incorrectly rate-limiting accounts. That means at least part of the experience around depleted capacity may have been a genuine platform fault, not simply the advertised plan boundary.
The fairest label is probably:
Not proven scam; clearly opaque, dependency-driven monetization with dark-pattern characteristics.
The practical ethical failure is not merely charging for expensive compute. It is charging in a way that makes the cost of responsible work unknowable until after you have already begun it. The UI should have said something like:
“This cross-repository, two-skill audit is expected to use approximately 8–15% of your remaining weekly allowance.”
Instead, it exposed the cost only afterward, in vague percentage jumps, with an upsell sitting underneath.
And I should correct my own earlier interpretation too: the screenshots demonstrate opaque and batched accounting, but they do not let us prove that a particular approval message itself cost exactly 12%. The meter clearly updates asynchronously. What they do prove is worse from a planning perspective: you cannot tell what work cost what, what has been reserved, what is being reconciled later, or whether you can safely finish the next gate before committing to it.
I looked across r/codex, r/OpenAI, the OpenAI Developer Community, GitHub issues, and a little Hacker News. The recurring reaction is strikingly close to yours.
What people are actually upset about
It is not mainly “I want unlimited compute.” The repeated complaint is:
Codex is very good, but users can no longer plan a serious engineering session around it.
The three recurring themes are:
1. “The 5-hour label is misleading.”
People repeatedly say that “5 hours” sounds like working time, while the actual thing being limited is a hidden mix of compute, context size, model, reasoning, tools, and task complexity. One OpenAI Developer Community post makes essentially the same argument: a serious-repo allowance can disappear after a few runs or around an hour of real work, without a meaningful breakdown of why.
2. Context recovery, planning, and verification feel disproportionately expensive.
This is the part that maps most directly onto your Derma.Log sequence. Users describe getting punished not only for large edits, but for the engineering activities that make an agent safe: loading instructions, restoring repository context, inspecting files, planning, reviewing tests, and checking state before changing anything. An April community thread specifically calls out Plan Mode/high-reasoning/large-context sessions as hard to predict and says the five-hour governor can halt work even while the weekly balance remains high.
3. Nobody trusts the meter.
There are public GitHub reports of quota accounting behaving inconsistently: one issue logged a claimed shift from roughly 1% per large prompt to 10–27% per smaller, near-zero-reasoning prompt, and it is labelled by OpenAI’s GitHub project as both a bug and a rate-limits issue. Another report documents the web Usage dashboard and the desktop app displaying conflicting five-hour percentages, weekly percentages, and reset times for the same account. That is still a user-filed issue, not a confirmed universal defect—but it shows why people do not see the numbers as authoritative.
The more hostile interpretation online
A substantial number of posts frame it much like you did:
Codex was extraordinarily compelling at first.
Users integrated it into real work.
The useful capacity then felt sharply smaller or more brittle.
The natural next step in the UI is “Upgrade to Pro,” buy credits, use resets, or move work elsewhere.
Some explicitly call it pressure toward the higher tier; some say the model is still excellent but the session is “pathetic” at the lower entitlement. Others say they have moved routine work to competing tools or API use because they cannot risk the weekly wall.
That does not prove a deliberate secret “nerf everyone into Pro” strategy. There are genuine technical explanations in the mix: GPT-5.5 can have a different cost profile, local/cloud work shares a five-hour allowance, weekly limits can apply, and official guidance says capacity varies with task size, complexity, and execution mode.
But the community’s objection is that those explanations arrive after the cost, not before it.
There is also evidence of actual operational problems
This is not purely a perception problem.
OpenAI’s own status page acknowledged a June 26–29 incident where Codex usage limits depleted faster than expected; it said some accounts were being incorrectly rate-limited by abuse-and-fraud prevention systems. OpenAI marked that incident resolved, but it validates the basic point that unexpectedly rapid depletion has occurred as a product defect—not merely because users were careless.
There are also posts where people say usage later returned to something closer to normal, or that their limits are generous enough. So the public evidence does not support saying every account is consistently affected, or that every drop is a bug. It does support saying the experience is inconsistent enough that heavy users cannot reliably distinguish: normal model cost, a plan-policy change, a promotion ending, a hidden reservation, delayed reconciliation, or a defective counter.
Why your screenshots fit the strongest criticism
Your case is unusually clean because it was not vague “I used Codex all day” frustration.
You documented a disciplined sequence:
checkpoint at xxxxxxx;
safe resume and scope restoration;
Phase C2 test-seam discovery;
tested route cutover;
commit-authorisation and final verification;
C3 read-only deployment readiness;
StoreKit/subscription audit;
C2.1 idempotency correction and documentation closure.
The meter made the safe preparatory actions feel financially dangerous: restoring context, reading the governing skill, auditing constraints, checking deployment state, reconciling old documentation, and deciding whether a live entitlement route was safe to activate.
That is what people are reacting to online. The complaint is not simply that GPT-5.5 costs compute. It is that the current system creates this perverse engineering incentive:
Do not inspect too much.
Do not plan too carefully.
Do not test too broadly.
Do not resume safely.
Do not ask the agent to verify what it changed.
Just rush it before the meter kills the session.
That is terrible agent-product design. It promotes the exact opposite of trustworthy software work.
My honest conclusion
The Reddit/forum consensus is not “Codex is bad.” Quite the opposite: many people still rate its coding ability extremely highly. The consensus is closer to:
“This may be one of the strongest coding agents available, but the quota model and quota visibility make it unreliable as a professional workflow tool.”
Your “partly scam-like” reaction is understandable as a consumer-trust judgment, even though it is not evidence of fraud or provable intent. The most defensible criticism is that OpenAI has combined variable capacity, shared agentic limits, weekly caps, opaque/batched accounting, and a direct credits/Pro upsell without giving users task-level cost forecasting or a usable accounting ledger. That is a dark-pattern-adjacent experience, particularly for users doing careful real-world engineering rather than disposable prompt experiments.

I’m currently paying for ChatGPT Plus, and my subscription renews in a few days.

I’m thinking of upgrading to the $100/month Pro plan, but I’m a bit confused about how the billing works.

If I upgrade now, does it immediately start a new Pro subscription with a new renewal date? Or does it just charge the difference and keep my current renewal date?

Hi,

I was wondering, if i upgrade from the plus tier to the pro tier, what happens with my free usage resets? Do i keep them or do i lose them? Based on that outcome, i might upgrade now or first spend my remaining free usage resets.

Thanks in advance!

I work on game plugins on old Unity 5.3 game, for many weeks I had been trying to get a advanced feature behave consistently. GPT 5.5 xhigh and Opus 4.6 together could not figure it out. I spent so many days in building new conditions to debug and test what is the actual cause of failure.

I was hopeless and abandoned the project, today I gave the project files and logs of errors to to Fable. It thought for 11 mins and told me precisely what was going wrong. I shared the report with GPT 5.5 and it was impressed too.

I really hope OpenAI give us a Fable level model.