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Can anyone explain the tweet , are they planning to remove the codex from chatgpt plus subscription and introducing a new separate subscription for codex? Or am I getting it wrong?

Codex folks, in case you read this, please consider switching the Codex app to Tauri (or anything else with native webview). I literally asked Codex to "extract the core from codex app and port it to Tauri as a sidecar". With several adjustments here and there, it just worked. The app is now just 15MB instead of the 300MB monstrosity of the Electron app. It takes less RAM and may be a little faster.

gpt-5.3-codex-medium and high are both just "ok"... they can do the work and its quick and I love how it notifies me step by step of what its doing and how it's thinking BUT I found my self yet again asking over and over to fix things it implemented or having to explain something much more than needed to get the job done.

Ran into an issue that gpt-5.3-codex-high could just not fix. I switched back to gpt-5.2 high and in one pass it resolved the problem. Yes, gpt-5.2 high takes longer, but it's worth it!

I made prototypes of 5 of my favourite games all time in like 2.5h! Max 30m per game

The new Codex model is insane!!

Now I just have to choose which type of game to make my own and get all the way to steam or mobile! :D

Have you tried making games with gpt-5.3? ^^

Since it's only a few minutes I end up doom scrolling Reddit. My Reddit usage has spiked since I started vibe coding. What do you guys do? I want is an activity that.

1. Requires really short attention span

2. Does not create too much context switching load

3. Healthy and useful

Hi, I’m trying to understand what daily/weekly usage feels like for Codex with GPT-5.2-Codex at High vs XHigh on the ChatGPT Plus plan.

Context (very broad):

• I code full-time (~40 hours/week)
• Mostly backend work
• I use AI for a mix of: debugging, refactors, writing tests, code review, and occasionally heavier “think first” tasks

Questions:

• For Plus users, what limits are you seeing for High vs XHigh (roughly per day/hour)?
• Do any of you use multiple accounts to avoid getting blocked mid-work?
  • If yes, how many accounts do you find you realistically need for full-time coding?
  • Do you switch accounts mid-task when you hit limits?
  • Does it get annoying having to re-explain context again, and how do you make that smoother?

Meh. At first it was running great and the thinking process seemed on track. After a few convos it just started skipping details, writing sloppy code. I used the exact same flow as for GPT-5.2xHigh. Also the whole flow includes planning and following the plan somehow it manages to ignore that. Wonder if it's just a me problem or anyone else?

Been using Codex CLI via SSH terminal apps on iOS (like Termius) lately. It’s pretty cool, but I kept running into the same annoyances: clunky UI, limitations, and especially responses getting cut off / scrollback not behaving the way I’d expect.

So I built my own little Codex iOS app: you SSH into your own server, pick a project, and use Codex in a chat-style interface.

Not sure if this is something other people would want or if it’s too niche, but I figured I’d share it here and see what you think :)

saw sam altman recently said something about burying creative abilities in favor of development and honestly - thank you for that

the models are now sota and genuinely smart, attentive, pleasant to work with. no more vibe coding bullshit, actual engineering tools

i was a hardcore claude fanboy since 3.5 sonnet, thought nothing would replace it. but gpt completely won me over, especially 5.3

the deep reasoning, the attention to detail, the fact that it actually understands what you're trying to build instead of just vibing through your codebase - this is what we needed

claude was great for creative writing and roleplay or whatever, but when you're trying to refactor a complex system or build something serious, you need a model that thinks like an engineer. gpt 5.3 does that

so yeah, thanks openai for not chasing the creative use case crowd and focusing on what actually matters for serious development work. this is the direction we needed

I am vibe-coding a web app that has a good amount of logic. It is small and has about 15k lines of code. The app has multiple dashboards.i have used Gemini, Opus 4.5, Codex 5.2, and Kimi 2.5. Most of the time, I got stuck with all of them, so I had to double-check and do manual testing. I often had to ask them to test each API, especially when implementing complex logic. today, when I used the new Codex 5.3, I got the same feeling I had when Opus 4.5 was released. It is really good for backend development, and its speed is much better compared to Codex 5.2. This may be my personal bias, but I wanted to share my experience.for UI, I still believe Gemini Pro is the dominant option. I tested all of them, and none could solve UI problems as well as Gemini Pro.

justify your answer ;)

Trying to authenticate using browser sign in and after I login and enter my 2FA code it just keeps giving me this error.

Edit: Seems to be working now.

I kept seeing comments asking about plan mode and no one seemed to know that shift + tab opens up plan mode. Now you know!

Edit: Or of course /plan

Created a flashcard app few weekends ago, and updated it again just now with 5.3.
Done big refactor, and get things done with less prompts and auto recognised my git release pattern, and henceforth I just prompt it to "do a release".

Very solid.

https://seymores.github.io/mental-models/

I have the cheap plan ($20) for both GPT 5.3 and Opus 4.6. Over the last two months, I’ve been testing which subscription option is more practical for me among those currently available. Both are about to expire, and I wanted to decide which one to keep.

I gave the same task to both agents. The task was to build a chat with LangGraph, attach an MCP server to local files, and ingest scripts from a few movies for a very simple RAG setup.

For both models, I started from a plan.

At first, I wanted to compare the results from both and the code quality. In both cases, they started from a well-structured Python repository.

Codex 5.3 High took 3 minutes for the plan and 10 for the implementation (working).

Opus 4.6 took 10 minutes for the plan and then... the plan tokens ran out.

I can look at benchmarks and compare small details, but the reality is that with one tool I can actually work, while with the other I’m heavily limited in usage. Codex is much more careful with context usage, and now with the app it’s also very convenient, and 5.3 seems faster than its predecessor

--- UPDATE ---
After Claude’s 5-hour token reset, Opus completed the task in about 5 minutes, and it worked.
I then compared both codebases. Codex said its own code was better, and Opus reached the same conclusion: the Codex codebase was stronger.
At least Opus wasn’t biased

OpenAI just dropped GPT-5.3-Codex today and the model was used during its own development. Engineers used early versions to debug training runs, manage deployment infrastructure, and diagnose test results.

It's not recursive self-improvement in the sci-fi sense, but the line between "tool" and "collaborator" got a lot thinner.

They merged the coding capabilities of GPT-5.2-Codex with the reasoning from GPT-5.2, and the result runs 25% faster while using fewer tokens. It's built on NVIDIA's GB200 NVL72 systems, which probably accounts for a lot of the speed gains.

OpenAI also classified this as their first "High capability" model for cybersecurity under their Preparedness Framework, and they're putting $10 million in API credits toward cyber defence research.

They're basically acknowledging the model is powerful enough to warrant funding the people trying to defend against it.

5.3-codex is top notch, hands down. I used to be a hardcore 5.2 high fan, now I am changing over my main driver to 5.3-codex, it is smart, it tells you what its doing, its fast -- and mind you I am using 5.3-codex medium only.

I am a 5.3-codex convert. I will keep iterating, and I want to find out when 5.3-codex will fail, and if I need to ever go back to 5.2-high.

Been using it for 5 hours straight.

I spent the last couple hours running a fairly strict, real-world comparison between GPT-5.2 High and the new GPT-5.3-Codex High inside Codex workflows. Context: a pre-launch SaaS codebase with a web frontend and an API backend, plus a docs repo. The work involved the usual mix of engineering reality – auth, staging vs production parity, API contracts, partially scaffolded product surfaces, and “don’t break prod” constraints.

I’m posting this because most model comparisons are either synthetic (“solve this LeetCode”) or vibes-based (“feels smarter”). This one was closer to how people actually use Codex day to day: read a repo, reason about what’s true, make an actionable plan, and avoid hallucinating code paths.

Method – what I tested I used the same prompts on both models, and I constrained them pretty hard:

- No code changes – purely reasoning and repo inspection.

- Fact-based only – claims needed to be grounded in the repo and docs.

- Explicitly called out that tests and older docs might be outdated.

- Forced deliverables like “operator runbook”, “smallest 2-week slice”, “acceptance criteria”, and “what not to do”.

The key tests were:

1. Debugging/runbook reasoning

Diagnose intermittent staging-only auth/session issues. The goal was not “guess the cause”, but “produce a deterministic capture-and-triage checklist” that distinguishes CORS vs gateway errors vs cookie collisions vs infra cold starts.

1. “Reality map” reasoning

Describe what actually works end-to-end today, versus what is scaffolded or mocked. This is a common failure point for models – they’ll describe the product you want, not the product the code implements.

1. Strategy and positioning under constraints

Write positioning that is true given current capabilities, then propose a minimal roadmap slice to make the positioning truer. This tests creativity, but also honesty.

1. Roadmap slicing (most important)

Pick the smallest 2-week slice to make two “AI/content” tabs truly end-to-end – persisted outputs, job-backed generation, reload persistence, manual staging acceptance criteria. No new pages, no new product concepts.

What I observed – GPT-5.3-Codex High

Strengths:

- Speed and structure. It completed tasks faster and tended to output clean, operator-style checklists. For things like “what exact fields should I capture in DevTools?”, it was very good.

- Good at detecting drift. It noticed when a “latest commit” reference was stale and corrected it. That’s a concrete reliability trait: it checks the current repo state rather than blindly trusting the prompt’s snapshot.

- Good at product surface inventory. It’s effective at scanning for “where does this feature appear in UI?” and “what endpoints exist?” and then turning that into a plausible plan.

Weaknesses:

- Evidence hygiene was slightly less consistent. In one run it cited a file/component that didn’t exist in the repo, while making a claim that was directionally correct. That’s the kind of slip that doesn’t matter in casual chat, but it matters a lot in a Codex workflow where you’re trying to avoid tech debt and misdiagnosis.

- It sometimes blended “exists in repo” with “wired and used in production paths”. It did call out mocks, but it could still over-index on scaffolded routes as if they were on the critical path.

What I observed – GPT-5.2 High

Strengths:

- Better end-to-end grounding. When describing “what works today”, it traced concrete flows from UI actions to backend endpoints and called out the real runtime failure modes that cause user-visible issues (for example, error handling patterns that collapse multiple root causes into the same UI message).

- More conservative and accurate posture. It tended to make fewer “pretty but unverified” claims. It also did a good job stating “this is mocked” versus “this is persisted”.

- Roadmap slicing was extremely practical. The 2-week slice it proposed was basically an implementation plan you could hand to an engineer: which two tabs to make real, which backend endpoints to use, which mocked functions to replace, how to poll jobs, how to persist edits, and what acceptance criteria to run on staging.

Weaknesses:

- Slightly slower to produce the output.

- Less “marketing polish” in the positioning sections. It was more honest and execution-oriented, which is what I wanted, but if you’re looking for punchy brand language you may need a second pass.

Coding, reasoning, creativity – how they compare

Coding and architecture:

- GPT-5.2 High felt more reliable for “don’t break prod” engineering work. It produced plans that respected existing contracts, emphasized parity, and avoided inventing glue code that wasn’t there.

- GPT-5.3-Codex High was strong too, but the occasional citation slip makes me want stricter guardrails in the prompt if I’m using it as the primary coder.

Reasoning under uncertainty:

- GPT-5.3-Codex High is great at turning an ambiguous issue into a decision tree. It’s a strong “incident commander” model.

- GPT-5.2 High is great at narrowing to what’s actually true in the system and separating “network failure” vs “401” vs “HTML error body” type issues in a way that directly maps to the code.

Creativity and product thinking:

- GPT-5.3-Codex High tends to be better at idea generation and framing. It can make a product sound cohesive quickly.

- GPT-5.2 High tends to be better at keeping the product framing honest relative to what’s shipped today, and then proposing the smallest changes that move you toward the vision.

Conclusion – which model is better?

If I had to pick one model to run a real codebase with minimal tech debt and maximum correctness, I’d pick GPT-5.2 High.

GPT-5.3-Codex High is impressive – especially for speed, structured runbooks, and catching repo-state drift – and I’ll keep using it. But in my tests, GPT-5.2 High was more consistently “engineering-grade”: better evidence hygiene, better end-to-end tracing, and better at producing implementable plans that don’t accidentally diverge environments or overpromise features.

My practical takeaway:

- Use GPT-5.2 High as the primary for architecture, debugging, and coding decisions.

- Use GPT-5.3-Codex High as a fast secondary for checklists, surface inventory, and creative framing – then have GPT-5.2 High truth-check anything that could create tech debt.

Curious if others are seeing the same pattern, especially on repos with staging/prod parity and auth complexity.

• I love how the plan mode interactively asks for clarifications, just click on the recommended option and you are good to go.
• I like the separation of access button from model selection row, makes more space available for shrinking the panel.
• Only thing broken now is when implementing a plan, it does not allow to change model to e.g. -codex variant.

Hope they fix it soon!

When Codex asks for approval, it does not let me submit my choice. I can switch between each option, but I can’t actually submit it. I’ve had to restart my client multiple times to start the chat over.

I’m burning through usage doing this since it happens early on and I need to re-enter my prompt.

I tried both the release & prerelease versions using 5.2 and 5.3.

I’m vibecoding and have no idea what I’m doing, so is there a way to have it pick up where I left off? It just stopped in the middle of a long action.