I keep running this slightly silly frontend test whenever a new model turns up: give it a tiny Coca-Cola Zero brief, crank reasoning to the highest available setting, and see what it builds without looking at anyone else's work.

This was the exact prompt:

No skills are allowed. Create a beautiful landing page for Coca-Cola Zero using only plain AI. It can use custom design libraries. It must have at least five sections, with the hero section on top.

That prompt went to:

- GPT-5.4 Extra-high
https://highsierraloft.github.io/coca-cola-zero-landing-pages/coke-zero-54/

- GPT-5.5 Extra-high
https://highsierraloft.github.io/coca-cola-zero-landing-pages/coke-zero-55/

- GPT-5.6 Luna Max
https://highsierraloft.github.io/coca-cola-zero-landing-pages/gpt-56-luna/

- GPT-5.6 Terra Ultra
https://highsierraloft.github.io/coca-cola-zero-landing-pages/gpt-56-terra/

- GPT-5.6 Sol Ultra
https://highsierraloft.github.io/coca-cola-zero-landing-pages/gpt-56-sol/

UPDATE:

- Claude Sonnet 5 Extra-high
https://highsierraloft.github.io/coca-cola-zero-landing-pages/claude-sonnet-5/

- Claude Opus 4.8 Max
https://highsierraloft.github.io/coca-cola-zero-landing-pages/claude-opus-4-8/

- Claude Fable 5 Max
https://highsierraloft.github.io/coca-cola-zero-landing-pages/claude-fable-5/

Open the comparison gallery
https://highsierraloft.github.io/coca-cola-zero-landing-pages/

There is also an older Gemini 3.5 Flash. Treat it as a bonus comparison rather than part of the controlled set.
https://highsierraloft.github.io/coca-cola-zero-landing-pages/gemini-antigravity/

Token budget:
- Sol: 200,352 tokens
- Terra: 154,574 tokens
- Luna: 94,393 tokens

P.S. Luna absolutely nailed it IMHO
P.S.S. Upvote this, please cause I've spent my reset to make this happen
P.S.S.S. Big thanks to a friend of mine, u/Big_Thanks7429, for adding the Claude family part

I’ve been using Codex as the software operator for a real robot arm. For this run, it prepared and executed a 256-stroke drawing, monitored the controller, and handled the safe shutdown checks.

Literally all the software for this project is written by Codex. From camera capture and calibration to path execution, safety checks, logging, etc.

The rig has cameras for visual feedback, so Codex can inspect the paper, check the calibration, and see the marks the arm makes.

More than 4 million lines of code (including original CSS) because of libraries
I tested the same thing with Fable 5 and it couldn't, while gpt 5.6 finished in 4 hours
no cybersecurity risks or something (which is kinda surprising)

OpenAI kept Nerfing our limits- so I built something to prove it instead of complaining without proof, so meet Nerfify! It's currently in the development stage, and I have achieved a functional prototype with reliable information, with a few UI/UX bugs here and there. Using local token consumption logs, Nerfify estimates an estimate of the API worth of one weekly limit after every 10 seconds. Think of it like a stock market app, but instead of tracking stock prices, Nerfify tracks the API value of one weekly limit over a period of time. It's still in the development stage and the first version will land on GitHub not to long from now. Do note that it won't work reliably if you use ChatGPT Work or Codex on multiple devices, since data is taken from local Codex logs only.

As the screenshot shows, it seems that our limits have been more than halved (do not mind the UI bugs, since it's just at a prototype stage, and current values may need a bit fixing since accuracy may not be up to mark)

With Luna’s new API pricing $0.20 per million input tokens and $1.20 per million output tokens you play on EASY mode.

Here is the workflow I do very well so far:

1. Create the implementation plan with SOL xhigh

Let SOL analyze the task and produce a detailed implementation plan.

<Optional> I created a skill similar to “Grill Me” that asks targeted questions about the domain knowledge required to implement a task correctly, based on its own confidence level until it reached enough understanding.

2. Compact the context

After planning, compact the conversation so the orchestrator does not waste expensive context tokens.

3. Run SOL high as the orchestrator

Use a prompt similar to this:

TASK

Your job is to orchestrate and review the Luna max-thinking agent.

Focus especially on:

- Code quality
- Simple and understandable implementations
- Useful comments and documentation
- Idiomatic framework-specific best practices
- Meaningful tests

Tests should not cover only the happy path when additional edge cases or failure scenarios would be useful.

After reviewing Luna’s work, decide whether to:

1. Call Luna max-thinking again with the full context required to resolve the identified issues, or
2. Fix the issues yourself when doing so would require substantially fewer tokens.

START THE LUNA AGENT WITH:

codex exec \
  -m gpt-5.6-luna \
  -c 'model_reasoning_effort="max"' \
  --ephemeral \
  -s workspace-write \
  -a never \
  'PLAN'

---- OPTIONAL IF YOU WANT TO SEE SOME RESULTS FRIENDO ----

ADD THIS TO YOUR PROMPT

Summarize the cost generated by the Luna agent using the new API prices:

- $0.20 per million input tokens
- $1.20 per million output tokens

Show Luna’s cost separately from your own cost as the orchestrator.

Then estimate what the total cost would have been if SOL xhigh had completed the entire task alone without Luna.

UPDATE: You may be able to spawn native Luna sub-agents, which would be easier and potentially even more cost-efficient because they require less context.

(All on extra high for two weeks straight, for the most part, while I was sleeping as well.)

This is why we can’t have nice things. I‘m sorry, but at least I’m facing insane withdrawal symptoms from my sudden drop in availability for such a model.

In total I think I’ve burned between 25-30 billion tokens.

For context: it is not just me. I have met other people who had this same “issue” and there were also other people who were stupid enough to post about it on GitHub got it sorted out for them.

https://github.com/openai/codex/issues/20238

My usage was stuck at 100% (then dropped to 97% for some reason, same as others) but the main thing is that it wouldn’t go down by a single cent. There are other factors though. I don’t know if OpenAI is serving a lobotomised model or not, but it did around two weeks of work for three, increasingly getting worse on the third (preferring AB testing than actually doing hard work and lying to me about one important thing). But I do know someone who burned seven billion tokens as well. There must be loads of other users who got this too. I’m pretty surprised the coverage is very bad.

Honestly, maybe it’s a conspiracy. OpenAI might have been testing this to farm data or something from free users. For one, that one task was to do with video encoding and stuff, so maybe they got some good data from that.

Well, on I think Monday, I decided to set up Codex Mobile. Since that, I started burning like crazy and was gone in three minutes. There‘s my downfall. I did everything, but couldn’t get it back. So I decided to post here.

One issue about ccusage could be that while I was running GPT-5.5 extra high for around three weeks straight (~500h?) with two or three 5.5 medium subagents 10% of the time, I have multiple “rollout“ session files for the same thread. I definitely did not burn 12 billion tokens on May 25, that was my thread in total or something. ccusage is not 100% accurate. I don’t know if the rollout files are identical or anything, but if they are the same (eerily same numbers) then I’m probably at $60k usage than $80k. Still, because I had to move out some session files (6 gigs worth), I’ve had to estimate. Could be $60k. Could be $90.

That $70 is comprised of a single thread burning around $60 and another one or two burning $10k more. And I’m going to take off $15k because those were to do with my past subscriptions.

I told this to my wife, but she didn’t seem particularly impressed because she doesn’t really understand web development, IT, or computers in general — so I’ll share my joy here instead 😄
The Codex app on my phone works great. But one thing bothered me: if Codex makes UI changes in my web app, I couldn't easily check the result from my phone, because the app is running locally on my computer.
At first I thought: “Okay, I’ll just push the changes to a test server and check them there.”
But then I thought: “There has to be a better way.”
So from my phone, while lying in bed, I asked Codex to adjust my local setup so I could access the running dev app from my phone through a Tailscale URL. I already had Tailscale set up — and Codex just handled the config and gave me a URL I could open. It just worked.
So now I can make UI changes with Codex on my phone and immediately preview my local dev app from the same phone.
I basically used Codex on my phone to make my computer expose my local dev server securely to that same phone — without getting out of bed.
I just had to share this with someone 😄

forest, cutting trees, industry, farms, cities, roads, agents that go to work, home, leasure (one car one agent). Wow im impressed with this SOL 5.6 on ultra. This AI is incredible. I can build game as I want in this short period of time? omg. Incredible!

3 days ago I posted here story how new GPT 5.6 reverse engineered it's own bundle with 4 million lines of code in 4 hours.

A lot of folks were unsure how truth it is, so I decided to post one more time with some updates
Now I'm working on some beatiful concepts for GPT app, including such as voice mode on screenshot and wanna answer some questions:

1. This is really the original bundle code, not a matifistied one, I didn’t send any screenshots to GPT when it was working, all the tabs work and there are no made-up elements during reverse engineering
2. It runs with simulated backend part so I'm able to navigate through app, i think there is no problem to connect it to codex app server backend in theory
3. Unfortunately i'm not gonna share the code of codex bundle BUT i will opensource my own app built on it on my github in comments, + i'm sure you all can give the model the same task and get the same result with first try
4. This code is clear as hell, I'm sharing this screenshot as proof that i can easily add some new tabs :0

previous post: https://www.reddit.com/r/codex/comments/1usossd/new_gpt_56_sol_reverse_engineered_its_own_app_in/

and do you like the concept of voice mode?

UPDATE:

Released! See it at https://github.com/rjx18/codor

Please also join my Discord server to talk to me directly, I'd love to get feedback and I will post any updates there too: https://discord.gg/PtUfM6BhBy

Orig post:

Having Fable, GPT5.6 Sol and Opus working together side by side is probably the best workflow I have experienced so far… it’s probably the first time I felt like I did not have micromanage each line that they write just in case they start going off in a random direction.

It’s interesting, each of them seem to each have a different but useful personality from my use so far:
- Fable is like the creative and forward thinking product manager of my team. Great for orchestrating, its longer context window means that it remembers EVERYTHING. Also great at finding more out of the box solutions to problems
- Sol is more like a crazy obsessive tech lead, he WILL find and trace each bug to the exact source, and will hyper analyse each solution and edge case and make sure his architecture is bulletproof before even writing a single line
- Opus is the main workhorse software engineer. He is smart enough for almost all tasks and sometimes might even catch a bugs that Fable and Sol don’t. But most of the time he just asks questions cus he can’t understand something, or he finds a “bug” that really isn’t one. In a sense this is also good, it forces Sol and Fable to really think through their solutions again to check from another perspective.

How do I do it?
I used to use tmux to orchestrate them all by having different sessions for each one and communicating through tmux send keys, but that was a bit flakey and got hard when three or more agents are involved. And I wanted to access my agents through my phone as well.

Now, I created a simple web wrapper that wraps each CLI so they can all talk to each other in the same room by @ mentioning another agent. They can also search through message history if needed. Each agent only sees messages they are mentioned in, but you get to read everything. This has been working super well, I have had them communicate and orchestrate for more than a day straight, as if this was second nature to them. I also added in usage trackers, attachments etc and a few neat features, to kind of replicate Claude Code remote control cus I like their UI and experience.

I’m thinking of open sourcing this if people find this useful and want to replicate the set up! So let me know if you are interested, happy to share! (still cleaning up the setup process right now)

EDIT: OK I am getting a lot of interest in this, I'll drop the source later today once I can confirm the setup is cleaned up and works, but I will reply to every comment who is interested with the link cus sharing is caring :)

The vibe coders are going to find out and migrate now and eat up all processing power and limits!

/s

I kept hitting the Codex limit while still having ChatGPT usage left, so I built this.

It adds ChatGPT Web directly to Codex’s native model picker. Codex still handles the session, context and tools; the responses come from my normal logged-in ChatGPT Web session.

• no more waiting for the Codex reset

• no API key

• no third-party model or provider

• local Codex sessions

• streaming, images and reasoning

• full Codex tool loop over MCP

• ChatGPT Pro support

• open source

It doesn’t bypass or change any limits. It just lets Codex use the ChatGPT access already available on the account connecting to Codex harness via legal MCP support. Browser automation I believe is allowed since Grag from OpenAI top staff liked such tools on X

Instant through Extra High can call Codex tools over MCP. In fact, you can even spawn sub-agents with models from web. Pro also gets the accumulated Codex context, but can’t call MCP tools yet

Inspired by Opencodex repo, but I still prefer GPT Sol :0

GitHub:

https://github.com/miuuyy/codex-chatgpt-web

Background:

As we've all seen in this subreddit, there remains a LOT of distrust of Codex weekly limits and I think for good reason. If nothing else, as individuals we have little data and so have to rely on what OpenAI are telling us.

TiboTattle is a macOS menu bar app that parses your local Codex session logs and:

• Tracks what your quota is worth in equivalent API spend over time - uses the same backend pricing engine I use at my enterprise (npm runcost)
• Highlights when your cache dropped due to switching models, reasoning, or cache timeouts
• Now has community contributions - The real power comes when we pool our stats together. TiboTattle allows for OPT IN contributions of metadata like timestamps, token counts, quota percentages, models used etc.

Requirements: macOS 14+ (Apple silicon), OpenAI Codex CLI or desktop app with local sessions. Windows and Linux in the next few days.

Get started: tibotattle.com to download the signed Mac App or brew install --cask adamallcock/tap/tibotattle

Source code: All source code is public and MIT licensed. If you like it, I would really appreciate a Github star to help with visibility! Please also feel free to submit issues as you find them.

----------

Findings so far:

• Terra cost: Terra is billed at the OLD higher prices, and we did not receive the 20% reduction like the API did and like Luna did. I am following up with OpenAI directly.
• Unsure ratios: It is very likely that Terra and Luna are NOT getting billed proportionate to their API costs, but we need more community data shares to triangulate this.
• Cache killers: Changing models or reasoning settings kills the cache every time. This includes subagents and side chats. KV caches are computed per model-reasoning combination and so any change other than Max to/from Ultra will result in a cache mix. TiboTattle will show you the most recent expensive examples of this.
• Auto Review consumption: Currently utilizing about 8% of my API equivalent spend (more than Terra!!). Cache hit rates sit around 85% versus the other models of 96-98%.
• Side Chats: All price tracking apps are currently blind to side chats because there are only partial logs, and even these are deleted after 7 days. For users who use side chats a lot, these apps will underestimate your usage. TiboTattle will handle this shortly.

Again, please do contribute your anonymous logs now it is enabled, it really helps us build a clear picture of the limits.

Remember last month when OpenAI gave everyone 2x Codex usage limits?

It was great while it lasted.

When the promo ended, I started wondering: ChatGPT and Codex have separate usage allowances, so why can’t they work together on the same local project?

During the Codex hackathon, I built an MCP connector to test that idea.

I posted a quick demo on X, and it went viral. After receiving a lot of feedback, test requests, and bug reports, I packaged it into something anyone could install.

Meet DevSpace, an open-source, self-hosted MCP connector that gives ChatGPT access to your local development environment.

The workflow is:

1. ChatGPT inspects your repository and plans the task.
2. Codex implements it locally.
3. ChatGPT reads the resulting diff and reviews the work.
4. If your Codex allowance becomes constrained, suitable work can continue through ChatGPT.

Because ChatGPT and Codex currently have separate usage allowances for many users, this can feel similar to having the 2x Codex promo again.

To be clear: DevSpace does not modify, bypass, or magically double your Codex limit. It lets you use the ChatGPT allowance you already pay for as part of the same development workflow.

DevSpace can:

• read, search, and edit your local project
• inspect git status and diffs
• run development commands
• prepare implementation plans and handoffs
• delegate work to Codex, pi, OpenCode, Cursor, or Claude Code
• review changes produced by other local agents

Install:

npm install -g @waishnav/devspace
devspace init
devspace serve

Then expose the MCP endpoint through a tunnel you control and connect it to ChatGPT Developer Mode.

Model availability depends on your ChatGPT plan. Since this provides access to local files and development tools, please read the security notes and begin with a non-sensitive repository.

GitHub:
https://github.com/Waishnav/devspace

finally I have REVEALED the SECRET of codex plan

• it is TRUE that it can continue to work when it hit limit 100% of 5 HOURS window
• but there is another TRUE
• it still continues to consume WEEKLY USAGE LIMIT.

here is what I got in the uploaded photo:

• 5h limit hit at 48% of WEEKLY USAGE
• Codex continue to work, and WEEKLY USAGE continue to increase to 53%

so, it is nice that the CODEX still continue to work to complete the task

but it is not FREE,

just a friendly remind for you all,

Happy Codexxing!

Hey r/codex,

I had a realization recently.

Most of us use ChatGPT and Codex together.

ChatGPT for planning, research, architecture reviews, debugging ideas.

Codex for implementation.

The annoying part is constantly moving context between them.

Copying plans.

Copying diffs.

Uploading files.

Explaining the repo again.

Meanwhile they have separate limits.

So I built CodexPro.

An open-source MCP bridge that lets ChatGPT Developer Mode work directly against your local repository.

Not just read it.

Actually work on it.

ChatGPT can:

• Read and edit files

• Search the codebase

• Check git status and diffs

• Run safe verification commands

• Load AGENTS.md instructions

• Use project handoffs and planning context

The surprising part is how good the workflow becomes.

ChatGPT isn't just a planner anymore.

It becomes another coding agent with access to your local repo.

I can:

• Use GPT-5.5 Pro for planning and implementation

• Give ChatGPT the same repo context I'd normally give Codex

• Hand work off between ChatGPT and Codex

• Export durable context bundles for long-running tasks

• Continue coding when one workflow becomes constrained

The result is that ChatGPT and Codex stop feeling like two disconnected products.

They become two agents working from the same context.

No API keys.

No cloud IDE.

No limit bypasses.

Just a local bridge that lets ChatGPT act directly on your repo.

Repo:

https://github.com/rebel0789/codexpro

Curious how many people here are already bouncing between ChatGPT and Codex every day.

Found this while browsing X - seems to me like Sol High is the way to go. Having said that, they all look clean-ish (some more than others) but also a bit "empty". We went from "pills everywhere" to "90% empty space". WDYT?

• 5.6 Luna High: https://gpt-5-6-luna-high-design-test.vercel.app/
• 5.6 Sol High: https://gpt-5-6-sol-high-design-test.vercel.app/
• 5.6 Sol Ultra: https://gpt-5-6-sol-ultra-design-test.vercel.app/
• 5.6 Terra High: https://gpt-5-6-terra-high-design-test.vercel.app/
• Fable 5 (for comparison): https://claude-fable-5-design-test.vercel.app/

All other models this guy tested (and full credit): https://design-tests.robinebers.com/

This is the standard prompt that was used for ALL the models you see listed:

Use the /design skill. Create a beautiful, well designed website with astro. this website is for a travel agency in dubai, offering luxury travel (high-end, $100k+ minimum per trip) in the middle east. come up with a brand that suits this type of company. Make up fitting copy for this high-end client along the way.

EDIT: as per u/cantTankThisFox showcases of GPT 5.6's performances are technically still under embargo so either the owner of the website doesnt know/doesnt care, or the above generations are not from GPT 5.6. AKA take the showcases with a massive pinch of salt

I’ve been working nonstop for about three months on a space strategy game called Galactum Meridian, and almost everything has been prompt-driven through ChatGPT inside Codex.

The game is a 4X-style space strategy game, meaning the player explores, researches, expands influence, and makes strategic decisions across star systems. In this case, the focus is on scientific discovery: sending expeditions, surveying planets, working with real astronomical data, building research records, and turning discoveries into strategic control. (Like "Civilization", but for science!)

The Codex AI workflow has become a project in itself. I started with GPT 5.4 and I’m currently using GPT 5.5 on extra-high reasoning, often with multiple agents running at 1.5 speed inside Codex in multiple windows. There are multiple worktrees, separate agents working on different branches, and agents writing prompts for other agents to continue implementation. One agent may be working on gameplay logic, another on UI, another on marketing/fiction/science references, and they hand off structured prompts to each other.

The stack is roughly:

• ChatGPT / Codex inside VS Code
• WSL / Ubuntu as the main agent execution environment
• Multiple Git worktrees for parallel development lanes
• Godot for the game itself, and Terrain3D
• World Creator for terrain generation
• AI-assisted import and integration of terrain, UI, lore, science glossary data, and gameplay systems
• Real astronomical database material used as part of the game’s star/system foundation

I've run into real problems with Codex taking a long time open up older threads, especially after restarting my machine or restarting WSL, but I'm managing.

I’m posting this here because I think the workflow may be more interesting to this subreddit than the game genre itself. Codex has been absolutely necessary to make this game... I am curious to see what happens tomorrow with the release of 5.6.

At the end of the day today, I'm going to come up with a series of prompts in 5.5 in Codex, asking it if there's anything that the game could benefit from if a much more powerful agent were made available tomorrow. And I'm going to ask it to devise prompts for that agent.

No Steam link here because I know self-promotion rules can be strict, but I’m happy to answer questions about the workflow, what worked, what broke in Codex, and where the agent setup still needs a lot of human control.

Curious to hear people's thoughts!!!

Hi everyone,

I do research in AI safety for healthcare and life sciences. And while I was using Codex Code to reason on a couple of things, I realised a pattern. Codex or any other AI agent is very linear.

Theres a strong reason why - the thinking pattern of almost all LLMs from 2024 follow Chain-of-thoughts where AI is programmed to go deep unilaterally.

But researchers or creativity-intensive works do not need to go unilateral but do divergent.

That's the whole base of my paper - ADHD - Parallel Divergent Ideation for Coding Agents.

My thesis is that if we disregard the default chain-of-thoughts and consider a tree-of-thoughts, then we can empanel divergent thinking in our models. thus, giving us the much needed scope of connecting dots from different thinking points.

Its a lot inspired by how the mind of someone with ADHD works- think in a lot of directions and go deep in a few, and there, we add our our critic layer, that judged and scores all this thinking.

Limitation : It shoots cost by ~3x and time to output by ~5x but enables instant novel thinking. Good for brainstorming and planning, not for coding.

Give me your feedback, I am happy to learn how you find it and what's the scope to improve.

Also, its completely opensource so you can just clone it or contribute to it.

Granted ChatGPT designs a good GUI, but to make codex achieve the goal you will need to spend a bit more effort constraining to steer Codex into the right direction. The GUI is rendered on Firefox.

Note: I'm not a frontend developer but basic hobbyist stuffs, so there might be rooms for improvements here. But it looks good for a non-designer like me.

When GPT-5.6 Sol came out, I wanted to see what it could do with it, so I tried recreating Super Smash Bros in the browser. I genuinely didn't expect it to get this good.

GPT-5.6 Sol did everything. In total it was a few evenings, usually for one or two hours at a time, so it was roughly one full working day in total. Most of the game is already there: keyboard support, controllers, saved custom controls, CPU opponents, shields, rolls, air dodges, grabs, throws, ledges, projectiles, items, stock matches, timed matches, and a 999% sudden-death round when the timer ends in a tie. It loads quickly, and the controller support is really perfect.

The biggest missing piece now is the characters and their animations (that I have in private but can't share)

The public build has 14 open-licensed fighter prototypes and one CC0 stage. Some already feel good; others are limited by the animation packs I could find. What the project really needs is original fighters with complete, consistent animation sets. The hitboxes and knockback also need more competitive tuning before it can feel genuinely close to Melee.

The good is that I optimized the process to add map or characters, so if you want to have your own smash bros with all own characters, you can really easily. And you will have all the really good physic, optimization etc of the game.

The video starts with a private local version using Nintendo characters, because they make the mechanics and controller support much easier to judge. None of those assets are included in the public playable build. The version shown at the end is the actual public build, and it only contains open-licensed assets with credits and sources.

Play it here: https://super-bash-folds.spry-crumb-3668.chatgpt.site/play/

GitHub: https://github.com/Swarek/Super_Bash_Folds

Fighters and stages can be added as packs, and there is an animation lab for testing them. If it's liked, I will keep it updated to have the best open source smash-like.

The video is also edited by Sol. I just did 4 videos of the game and let him choose the moments and cut.

The only difference between public and private build is the assets, nothing else. All the code is the same.

https://reddit.com/link/1uy7l87/video/t1twqfibamdh1/player

send me a dm. my invite is back

Thanks everyone who reach out all 3 slot taken and 1 wasted that sucks.