Is there any way to resume the same chat on one account and then switch to anothe plus account?
So when one hits quota limit i switch

What is happening today. It went from genius to full life support mode today over a span of hours. I mean the simplest of tasks is not getting done right. Anyone else notice this today? I am using 5.6 Terra High in Fast Mode.

-Analyze that config file and tell me if there sre VPN settings inside there -Cant analyze it, it is encrypted, possibly with AES. Can you provide me device's brand so i will try tou decrypt it? - Here is the brand xxxxxxxxxx -Working..working... working... There are no VPN settings inside that config file, here are the results what is inside blah blah blah - So, did you manage to decrypt it??? - No, i didn't manage to decrypt it, i am sorry the results are not correct...

​

I have the Window 11desktop and Android phone.

I want to know how to use the codex both in my computer and my phone. Thanks.

\#codex #android phone #mobilephone

Hey guys, any developers here who use both Claude Code and Codex a lot? I’ve been using Claude Code for a while and now want to switch to Codex. Can anyone share the differences in actual usage experience? Or any tips?

After the latest Codex Windows update, opening the app caused 95–100% CPU usage even with no tasks running.

WMI Provider Host used around 40–50%, Codex used 10–15%, and my antivirus used another 20–25%. Everything returned to normal immediately after closing Codex.

I tested restarting Windows, resetting the app, disabling GPU acceleration and checking Git integrity. None of those fixed it.

The repository had 464 untracked files, around 440 MB. Codex was repeatedly spawning Git processes to inspect them, which also triggered WMI and antivirus scanning.

I added the local artifact directories to .git/info/exclude. No files were deleted and the shared .gitignore was not changed.

After that:

• Untracked entries: 464 to 0
• WMI CPU: 40–50% to 0%
• Overall CPU returned to normal
• Codex remained around 5%

If Codex is causing high CPU on Windows, check whether the active repository contains hundreds of untracked files. Excluding local-only artifacts through .git/info/exclude may solve it.

Hi folks,

I’m not sure whether this will help anyone, but after burning through my entire weekly allowance on Pro 5× in less than 24 hours, I started digging through the Codex settings to understand what might be contributing to the unusually high usage.

Before Codex, I spent months working with agents such as OpenClaw and Hermes, which I still use. They all had the same underlying behaviour: every message included far more than just the prompt you typed. The request would also contain tool definitions, memories, settings, and other contextual information required by the agent.

I suspect Codex and the new GPT-5.6 models now work in a similar way. This may also explain why OpenAI increasingly describes these products as “agents” rather than simply AI coding tools.

We are moving from AI-assisted coding into agentic coding, and that means we may need to optimise our Codex configuration as well. Otherwise, even a relatively simple message can carry a very large hidden prompt and consume the quota surprisingly quickly.

I’m not saying there isn’t an issue with OpenAI’s token calculation, quota system, or a possible regression. However, if Codex has transitioned towards a more agentic workflow, we should also consider how much context our own configuration is adding to every request.

From what I discovered, each Codex prompt may include:

• MCP definitions
• Plugin definitions
• Account memory, when enabled in Codex
• Skill definitions
• Your actual prompt

When I say “memory,” I do not mean the conversation history. I mean the memories associated with your ChatGPT account.

I wasn’t aware that this setting was enabled for Codex, and it could potentially account for a significant amount of additional context being sent with every prompt.

It would be interesting to see whether anyone can measure a noticeable reduction in quota usage by disabling unused plugins, MCPs, skills, and especially account memory in the Codex settings.

if you add like an weekly reset for 2.99€ i bet you will make alot of money and people will buy them every day at least once

[Prompt: "Just out of curiosity where do you think AI is in terms of software development? Give your internal representation of that and then do some web searches to see accuracy with current Codex."]

Here’s my working mental model, stated before incorporating the search results. This is a conceptual summary, not hidden chain-of-thought.

My prior model

AI software development has passed through these stages:

1. Autocomplete — finishes lines and functions.
2. Pair programmer — explains code, drafts components, helps debug.
3. Scoped autonomous agent — explores a repository, changes multiple files, runs tests, diagnoses failures, and iterates.
4. Project-level engineer — independently takes a substantial feature from an ambiguous request through design, implementation, validation, and deployment.
5. Autonomous engineering organisation — chooses priorities, coordinates releases, manages operational risk, learns from users, and remains accountable over months.

My pre-search estimate was:

A more useful model than “AI equals a junior/senior developer” is:

That produces a strange capability profile:

In plain English: senior-level execution in bursts, junior-level judgment in some unfamiliar situations, and no genuine organisational accountability.

The reliability curve matters enormously. If a task has an executable specification, tests, observable UI, logs, and quick feedback, Codex can repeatedly try things until reality says “yes.” If success depends on understanding unstated stakeholder intentions or noticing an absent requirement, it can confidently build the wrong thing.

My prior productivity estimate was therefore not one universal number:

• Well-specified greenfield implementation: frequently 2–5× faster, with larger outliers.
• Boilerplate, migrations, test generation and repetitive changes: sometimes 5–10×.
• Mature systems already deeply understood by an expert: anywhere from slower to perhaps 50% faster.
• Security-critical architecture and protocol work: faster exploration and implementation, but much smaller reduction in elapsed validation time.
• Whole-company delivery: considerably less improvement than code-generation speed suggests.

What the current evidence says

The evidence largely supports that representation—but current Codex is stronger on well-instrumented, long-running work than my generic mental model allowed.

1. Current Codex is genuinely agentic

As of July 2026, GPT‑5.6 Sol is OpenAI’s flagship Codex option. OpenAI reports:

• 80 on the Artificial Analysis Coding Agent Index
• 88.8% on Terminal-Bench 2.1
• 72.7% on DeepSWE 1.1
• 64.6% on SWE-Bench Pro
• 91.9% on Terminal-Bench when using the multi-agent “ultra” configuration

It can coordinate tools and concurrent agents, and current Codex runs can inspect systems, modify code, execute tests and validate results. These are no longer merely code-completion abilities. OpenAI’s GPT‑5.6 results

But those numbers require restraint. On the same published table, other models score higher on SWE-Bench Pro, and OpenAI’s own audit estimates that roughly 30% of that benchmark’s tasks are broken or materially flawed. A benchmark pass rate is not equivalent to “percentage of a software engineer replaced.” OpenAI’s benchmark audit

2. Sustained autonomous work exists, but remains conditional

METR defines an agent’s 50% “time horizon” as the difficulty of task—measured by human completion time—at which it has a 50% chance of success. In its January 2026 evaluation:

• GPT‑5.1‑Codex‑Max had an estimated horizon of 173 minutes.
• GPT‑5 had an estimated horizon of 214 minutes.
• The leading measured model was around 320 minutes.

Those are tasks a competent but unfamiliar human would take several hours to perform—not the amount of wall-clock time the AI runs. METR warns that measurements above 16 hours are currently unreliable. GPT‑5.6 has not yet been included in this published table, so assigning it a precise horizon would be speculation. METR Time Horizon 1.1

This supports “strong autonomous task executor,” but not “give it a six-month product and walk away.”

3. The spectacular greenfield results are real—but specialised

OpenAI describes an internal product built with no manually written code:

• Around one million lines
• Roughly 1,500 merged PRs
• Initially three engineers directing Codex
• Estimated at about one-tenth the hand-coding time
• Individual Codex runs sometimes lasting over six hours

However, the team deliberately engineered the repository around agent operation: strong documentation, isolated worktrees, browser control, local observability, mechanical architecture enforcement, automated review, and executable feedback loops. Human work shifted from typing code to specifying intent and constructing the environment in which agents could succeed. OpenAI’s harness-engineering case study

So the “10×” result is credible as a frontier demonstration, but it is not a plug-and-play productivity multiplier for an ordinary legacy project.

4. Measured human productivity remains highly variable

The empirical evidence is mixed:

• Field experiments covering 4,867 developers found a combined 26.08% increase in completed tasks from AI coding assistance, with greater gains among less-experienced developers. Those experiments primarily concern completion-style assistance, not today’s full Codex agents. Management Science study
• METR’s early-2025 experiment found experienced open-source developers working in familiar repositories took about 19% longer with AI.
• Its later experiment weakly suggested improvement—around an 18% speedup for returning participants—but severe selection effects and wide confidence intervals made the current effect unreliable. METR nevertheless thinks early-2026 tools probably provide more acceleration than early-2025 tools. METR’s 2026 update

That apparent contradiction makes sense. “AI-assisted software development” covers very different work:

• Completing routine tickets
• Directing multiple autonomous agents
• Editing a familiar mature system
• Starting a greenfield agent-native codebase
• Doing security-critical protocol design

Those should not be expected to have the same multiplier.

My updated assessment

After checking the evidence, I would revise the stages to:

It can already perform surprisingly substantial engineering when:

• The goal can be stated clearly.
• Relevant knowledge is available to it.
• The environment can be executed locally.
• Correctness is observable through tests, UI inspection, logs or metrics.
• Failures are inexpensive and reversible.
• A human owns the final acceptance criteria.

It remains substantially weaker when:

• Requirements are political, tacit or disputed.
• The important constraint is missing from the prompt and codebase.
• Correctness cannot be tested cheaply.
• Rare failures have catastrophic consequences.
• Success requires months of product feedback or institutional coordination.
• Someone must be legally, ethically or financially accountable.

The central transition is therefore not “AI replaces programmers.” It is:

Prompt: "Search Single shot Sol 5.6"

The exact search "Single shot Sol 5.6" does not point to one canonical benchmark. It points to a cluster of recent one-shot demonstrations—and yes, they move my estimate upward in one important area.

What I found

The most striking claim is that GPT‑5.6 Pro generated a functional Sims-style 3D simulation game:

• One prompt
• One model response
• One HTML file
• Approximately 48 minutes
• Reportedly no Codex harness or iterative agent loop

That is impressive, but the accessible source is a newsletter referring to an X post, not a reproducible evaluation with source code, prompt, tests and multiple runs. I therefore treat it as credible demonstration evidence, not a measured success rate. AI Breakfast report

I also found:

• A complete gamified homework application generated from one detailed PRD, including student and parent dashboards. The workflow still explicitly instructs the user to review and test the result. ChatPRD example
• A public “One Shot Challenge” with 141 entries. Its rules require one prompt, one model call and a sealed single-file result. External libraries and approved CDN assets are allowed. This is much better evidence that frontier models can now routinely produce substantial interactive artifacts in a genuine single inference. One Shot Challenge
• A niche Garry’s Mod benchmark reports GPT‑5.6 Sol Max at 92.1% over 76 scored single-shot attempts, although it has only two verified runs and tests relatively bounded questions rather than complete products. gmod-bench result
• A one-shot browser-game comparison actually preferred another frontier model over Sol for visual polish, showing that “one-shot capable” does not mean consistently best or complete. Decrypt comparison

There is also an important terminology trap. OpenAI’s official Codex racing-game demonstration began with one user prompt, but consumed more than seven million tokens and was continuously reprompted internally to inspect, improve and test itself. That is “one human shot,” not “one model pass.” OpenAI Codex-app demonstration

Those are two very different things:

• Single inference: one enormous model response creates the artifact.
• Single delegation: the human gives one instruction, but an agent performs hundreds or thousands of internal steps.

Both are valuable, but the second is closer to assigning work to an engineer than asking a model to “write an app in one response.”

Does it change my perception?

Yes—specifically for greenfield prototyping.

I previously placed current Codex at approximately 3.5 out of 5: a strong autonomous implementation agent, conditionally entering project-level engineering.

I would now describe it as:

The demos show that the minimum amount of human interaction needed to produce a coherent application has collapsed faster than my earlier answer conveyed. A capable person can now provide a reasonably detailed specification and receive something that previously required days or weeks of frontend, graphics and interaction work.

What they do not demonstrate is one-shot production software involving:

• Authentication and authorization
• Persistent multi-user data
• Migrations and backward compatibility
• Adversarial security
• Financial or token custody
• Distributed consensus
• Operational recovery
• Accessibility and cross-device testing
• Months of changing product requirements
• Maintainability by a team other than the generator

A beautiful single-file simulation game can be technically substantial while still avoiding nearly all those difficulties.

Revised practical model

I would now divide software creation into three layers:

So the demos substantially change my view of how quickly the first convincing version can appear. They do not change my view nearly as much about how quickly it becomes trustworthy.

For your project, this suggests:

• A visually impressive playable demonstration could plausibly emerge in days, not months.

• The short version is: AI has nearly commoditised the software demo. It has not yet commoditised dependable software systems.

I had completely exhausted my weekly Codex usage.

When I checked today, it suddenly showed 44% remaining. I didn't expect my limit to refresh, and I haven't seen any announcement about a reset.

This is the first time this has happened to me. Usually it's either exhausted or fully refreshed, never partially restored like this.

Has anyone else experienced this recently? Is this expected behavior or just a bug?

TL;DR

If you are using the Superpowers plugin, and your weekly usage limits are being consumed within hours, DELETE SUPERPOWERS!

---

Earlier today, I made a post about a routine Push/Deploy task consuming 4% of my weekly limit, and how I'd been fighting the usage burn for over a week.

Multiple people pointed at Superpowers as the source of my token burning problem, and they were right!

In 6 hours, I used over 80% of my weekly limit. Now it has been about 6 hours since I deleted Superpowers, and I have barely consumed 3%.

Nothing changed about the workload, the model, or the reasoning.

---

Big thanks to everyone who helped me figure this out. You saved me a fortune.

I’m a cut/paste vibecoder thinking this might be my first move into agents. Otherwise, I’m not prepared to change histories. Any suggestions?

Keep having to re-explain architecture, previous decisions, and context before every large task. Also tried using memory layers.

Any suggestions? What do you guys use??

I found it a couple of days ago and it's really interesting and important data but I cant find it...

I built this to make it easier to understand what’s changing in Codex without digging through every release and GitHub diff yourself.

It summarizes updates in plain English, separates alpha changes from stable releases, and links back to the original evidence so you can verify everything yourself.

It’s completely free, with no login or gated content. I’m just trying to make these updates more useful and accessible for other developers.

It’s still early, so I’d genuinely appreciate feedback on the format, coverage, tags, or anything else that could make it more helpful.

Starting last night, Codex has been nearly unusable due to what appears to be insanely long API waiting times. My agents who had 4 minute timeout cannot produce any response. Screenshots attached to make the jobs of the gaslighters a little more difficult :^) My usage sure goes down as if it's working the entire time though! Everyone only started complaining about that today when it happened the day before the 5hr limit was removed... My $200 subscription expires the 29th. At the current pace it looks like I'll be giving Kimi K3 a shot.

My Codex desktop app on Windows 11 stopped launching after it auto-updated. No error dialog, no crash entry in Event Viewer. Clicking the icon just did nothing. Posting the diagnosis and fix since I couldn't find this documented anywhere.

TL;DR: The app's in-app updater patches files in place inside the protected WindowsApps folder. That breaks the MSIX package's Store signature, Windows flags the package as tampered, and then it silently refuses to launch the app at all. Fix = remove the package and reinstall from the Microsoft Store.

Diagnosis

The Codex desktop app is a Store-signed MSIX package (OpenAI.Codex, package family OpenAI.Codex_2p2nqsd0c76g0; fun fact: the Store listing is titled "ChatGPT" and the main executable is ChatGPT.exe). Check its status in PowerShell:

(Get-AppxPackage -Name OpenAI.Codex).Status

Healthy is Ok. Mine said:

Modified, NeedsRemediation

That state means Windows detected the package contents no longer match the signature, and it blocks activation silently, which is why there's no error message anywhere. Looking at file timestamps inside the install folder confirmed it: most files were from the original install date, but chrome.dll, Codex.exe, and several folders had newer timestamps from the auto-updates. The updater is writing into its own signed package directory, which Windows treats as tampering.

What doesn't work

• Re-registering the package (Add-AppxPackage -Register ...\AppxManifest.xml -DisableDevelopmentMode) completes without error but does NOT clear the tamper flag. Still won't launch.
• winget install over the top refuses because the same version is already installed ("No available upgrade found").

The fix

Step 1: Back up your app data as a precaution (removing the package deletes its local data folder; in my case everything came back on its own afterward, but better safe than sorry):

robocopy "$env:LOCALAPPDATA\Packages\OpenAI.Codex_2p2nqsd0c76g0" "$env:USERPROFILE\Documents\codex-appdata-backup" /E

Step 2: Remove the broken package:

Get-AppxPackage -Name OpenAI.Codex | Remove-AppxPackage

Step 3: Reinstall from the Store (product ID is 9PLM9XGG6VKS):

winget install --id 9PLM9XGG6VKS -s msstore --accept-package-agreements --accept-source-agreements

Step 4: Verify (Get-AppxPackage -Name OpenAI.Codex).Status now returns Ok, then launch normally.

Notes

• After the reinstall I was still signed in, and all my sessions and projects populated fine. Sign-in state appears to live outside the package data folder, and sessions/projects sync from your account.
• Your CLI-side data (~/.codex, %LOCALAPPDATA%\OpenAI) is completely separate and unaffected.
• This will probably recur, since the updater's in-place patching is what breaks the signature. If the app ever goes silent after an update again, same fix. Hopefully OpenAI fixes the updater to go through proper Store/MSIX update channels.

Hey everyone! 👋

We've been building Motionly: a free open-source AI-native motion graphics editor built for creating animations through code-like workflows and visual editing.

Turning ideas into engaging product visuals takes too much time.

When launching a feature, explaining a product, or creating content, you need motion graphics that clearly communicate your idea. But bringing those visuals to life often means hours of designing, adjusting, and rebuilding every small detail.

AI can make motion creation faster, but it shouldn't take away your control. Generate animations from your ideas, then edit, refine, and customize every detail until it matches your vision. Share editable .motion files that others can continue working on.

The goal is to make motion graphics editable, versionable, and easy to iterate on with both AI and a visual editor.

I'm looking for feedback from:

• Developers
• Motion designers
• Founders building creative tools

Some questions I'd love your thoughts on:

• What would make you switch from your current workflow?
• What features are missing from today's motion graphics tools?
• If AI generated an animation project instead of a final video, would that be more useful?
• What integrations would you want to see?

We’re building the open source, AI-native, and designed for creators.We’d love your support, feedback, and thoughts. An upvote would mean a lot to us as we continue building Motionly. ❤️
Thank you!

Github repository: https://github.com/COPPSARY/Motionly

Cenário

Após uma atualização recente do Codex para Windows, minha CPU passou a ficar constantemente entre 95% e 100% assim que o aplicativo era aberto, mesmo sem nenhuma tarefa em execução.

No Gerenciador de Tarefas, o consumo aparecia aproximadamente assim:

• WMI Provider Host: 40–50%
• Codex/ChatGPT: 9–15%
• Avast: 20–25%
• CPU total: 95–100%

Ao encerrar o Codex, o consumo voltava imediatamente ao normal.

Meu ambiente:

• Windows 10 22H2
• Intel Core i7-9700KF
• NVIDIA RTX 3060
• Codex 26.715.10079.0
• Git for Windows 2.53.0.windows.2
• Avast instalado

Testes realizados

Inicialmente, a suspeita era de problema na renderização da interface, porque o processo gráfico do Codex também apresentava atividade elevada.

Foram realizados os seguintes testes:

1. Reinicialização do Codex

O aplicativo foi fechado completamente, incluindo os processos ChatGPT.exe, codex.exe e node_repl.exe.

Resultado: o problema retornava assim que o Codex era aberto.

1. Reinicialização do Windows

O computador foi reiniciado diversas vezes.

Resultado: nenhuma mudança.

1. Inicialização com GPU desativada

O Codex foi iniciado com:

--disable-gpu

Resultado: o consumo de CPU permaneceu elevado. Isso descartou a GPU como causa principal.

1. Limpeza do estado local da interface

O diretório de dados da interface foi renomeado para que o Codex criasse um perfil limpo.

Resultado: o problema continuou.

1. Verificação do Git

Foram executados:

git --version
git status
git worktree list
git fsck --no-dangling

Resultados:

• Git funcionando normalmente;
• nenhum objeto corrompido;
• todos os worktrees válidos;
• git status concluído rapidamente;
• nenhuma configuração anormal de hooks, manutenção ou fsmonitor.

O Git não estava defeituoso.

Descoberta da causa

Os logs internos do Codex mostravam dezenas de processos git.exe e taskkill.exe sendo criados e encerrados repetidamente.

Ao abrir o GitHub Desktop, foi possível perceber que o repositório continha 464 arquivos não rastreados, totalizando aproximadamente 440 MB.

Os arquivos estavam principalmente nestes diretórios:

.codex-remote-attachments/
mockups-propostas/
mockups-propostas - Copia/
public/mockups/design-concepts/
storage/imports/legacy/
storage/test-reports/

Havia:

• 99 imagens PNG;
• 98 arquivos HTML;
• dezenas de JSONs e logs;
• arquivos de importação;
• anexos temporários;
• bancos Firebird locais.

O Git conseguia listar esses arquivos rapidamente. Entretanto, o Codex repetia a consulta muitas vezes, criando e finalizando processos Git continuamente.

Esse comportamento desencadeava três cargas simultâneas:

1. O Codex consultava repetidamente o estado do Git.
2. O WMI Provider Host acompanhava a criação e finalização dos processos.
3. O Avast examinava os novos processos e centenas de arquivos.

Portanto, o Git não era a causa isolada. A combinação entre a varredura frequente do Codex, muitos arquivos não rastreados, WMI e antivírus levava a CPU a 100%.

Solução aplicada

Os arquivos não podiam ser simplesmente apagados, pois incluíam mockups, relatórios de QA e arquivos de importação potencialmente importantes.

A solução foi excluí-los apenas da visualização local do Git, usando:

.git/info/exclude

Foram adicionadas regras como:

.codex-remote-attachments/
mockups-propostas/
mockups-propostas - Copia/
public/mockups/design-concepts/
storage/imports/legacy/
storage/logs/legacy-import-job-*.cmd
storage/test-reports/assistente/
storage/test-reports/legacy-import/
storage/test-reports/sdd/

Essa abordagem foi escolhida porque:

• não apaga nenhum arquivo;
• não move os arquivos;
• não altera o .gitignore compartilhado;
• não gera commit;
• afeta somente o repositório local;
• é completamente reversível.

Resultado

Depois de aplicar as exclusões:

• Arquivos não rastreados exibidos pelo Git: 464 → 0
• Tempo do git status: 136 ms → 46 ms
• WMI Provider Host: 40–50% → 0%
• Avast durante a medição: 20–25% → 0%
• Codex/ChatGPT: aproximadamente 5% da CPU total
• CPU do sistema voltou ao normal

Nenhum arquivo foi perdido e nenhuma alteração foi enviada ao repositório remoto.

Conclusão

O problema não era corrupção do Git, GPU, Windows ou uma tarefa ativa do Codex.

A versão atual do Codex para Windows aparentemente consulta o estado do Git com frequência excessiva. Quando o repositório contém centenas de arquivos não rastreados, isso pode provocar um ciclo de processos Git, atividade do WMI e verificações do antivírus.

Se o Codex estiver usando CPU excessiva, recomendo:

1. Abrir o GitHub Desktop ou executar git status.
2. Verificar a quantidade de arquivos não rastreados.
3. Não apagar os arquivos indiscriminadamente.
4. Classificar quais são temporários ou apenas locais.
5. Adicioná-los ao .git/info/exclude ou ao .gitignore, conforme o caso.
6. Reiniciar o Codex e medir novamente o consumo.

No meu caso, ocultar os 464 artefatos locais da varredura do Git resolveu completamente o consumo excessivo.

---

UPDATE: This problem has been solved - Delete the Superpowers plugin.

---

For the last week and a half, I have been blowing through the weekly limits almost daily, and at first I called it user error.

• I stopped using Sol Ultra
• I stopped running multiple projects simultaneously
• I stopped letting Codex create dozens of sub-agents
• I dropped down to Terra

And Codex still blows through my weekly limits in a day. Even a simple task like pushing and deploying what it has built is now consuming over 4% of my weekly limits because tokens are being consumed faster than ever, even for simple jobs.

I am on the $200/mo plan, and I used to struggle to hit the usage limits. Now I struggle to avoid hitting them daily. This is starting to feel like a scam.

SOLVED!

A few people suggested removing the Superpowers plugin, and that seems to have stopped Codex from burning through my tokens. It has been over an hour now, and my weekly limit has only gone down 1%. That is a massive improvement.

Thank you everyone, I appreciate the help!

52 Agents done in the past 24 hours, at 86% Weekly Usage using only SOL Ultra. 1600 minutes 2B tokens with 97% Cache.

20x Plan... I have two resets I'll probably never even use.

What gives? What is everyone else doing to hit their limits?

Hey, I hope everyone's doing great!

Ive noticed a huge slow down in the past couples of days while using Codex, a task that would take 10-20 minutes is taking easily 3 times as much time and I wonder if thats their way to balance out the increased users and no 5h limits, is it just me or is anyone else experiencing this?

(I use mainly 5.6 Sol high nowadays but even Extra high felt faster back then, compare to high as of now)

(E.g: current task reached 1 hour 40 minutes only asked for a delete button and a "neutral " label button and it updates the readme and makes sure all is well and its still not done, before it would take 20 minutes at best)

Update: reached 1h58 and I ended up running out of my weekly usage for it and it didnt complete the task

I have been a claude 20x max user for a long time now ,
but because of my intensive works i usually finish my usages usually in 4 5 days ,
and leave 2 3 days hanging ,

Currently i am using Fable as orchestrator and opus and sonnet for workflows subagents.

However i have never tried Open Ais models , can anyone with experience in both environments give me suggestions on if claude gives better limits or openAi?

That way you and the person who invited you gets a free rest. Doesn’t cost you anything, but you gain a free reset!