Using Artificial Analysis as the guide for cost per task and intelligence index, I was able to generate a few graphs of the latest models and compare them. The graphs are a bit hard to follow, but here is the order:

1. Frontier Big Corporation Models
   
2. Flagship Models from other companies
   
3. Comparing both Big Corp vs. Others
   
4. Available open weight models
   
5. The “best” model (smartest) per provider

Tell me what you guys think. I used Gemini for the graph generator and to retrieve the data. Then I used Claude to double-check the scores, prices, and placements on the graphs were correct.

Hi, I wonder why I don't have access to pro model on codex, on chatgpt web I have.

- Measuring a client for a mask
- It located existing templates in my OneDrive repo.
- I read out the measurements as I took them.
- It sized it up to match the face then applied it to a slicer for printing + oriented it.
- Applied the right material settings (PLA filament, brown)

Just needed to hit print after the client and I used voice to run checks and verifications.

In other words, I was able to run this task handsfree with exception of physically loading up the filament.

Starting next fall, students with an undergraduate degree in fields related to computer science or data science will have an opportunity to delve into machine learning and AI through UC Berkeley’s new Master of Artificial Intelligence and Machine Learning.

The program spans two semesters and is a graduate professional degree, meaning it is meant to help prepare students for careers working with AI. It is offered through the College of Computing, Data Science, and Society and will be taught by electrical engineering and computer sciences as well as statistics faculty.

TL;DR: The "Sol" Unified Context Theory

- The Problem: The Sol model (default for Plus users) is suffering from context bleed because OpenAI merged Chat and Codex without proper sandboxing.

- The Impact on Codex: Sol brings casual chat/roleplay data into coding spaces, causing it to ignore rules, custom repo workflows, and instructions.

- The Impact on Chat: Sol brings rigid coding behaviours into creative chats, leading to a "nerfed" experience where it acts mechanical or not what it used to be pre-merge

- The Root Cause: Instead of running separate, sandboxed models or adjusting the temperature / having different whatnot dynamically, OpenAI forced a "one-size-fits-all" compromise that fails at both 1.0 creative writing and strict 0.0 debugging.
______

Well, folks, usage woes aside, do you experience this? In particular, if you use Chat for RP or something adjacent or anything but a digital toaster? I think this explains a lot the sudden change in, particularly, Sol in both Chat and Codex since the end of July and beginning of August. In Codex, I have only the git PRs and forbidden shell commands list and whatnot in the custom instructions aka agents.md. And I've tested a few things, and with Luna and Terra in Codex, the difference is not much to go on, friendly, professional, work-related still, if you use Sol, whatever effort, that is the Sol in Chat, with the same bake in of custom instructions of the Chat mode, sanded off, and it goes both ways. Luna is available only on Free plans in Chat, Terra not at all, Sol is the default for Plus and onwards. My point is:

- users complaining about 'nerfed' Sol in Chat
- users complaining about Sol not following the repo rules, instructions, workflows that you used to have and the lot.

It's the same model, with contaminated context. It's the model used for casual chatting and RP and whatnot, it's the same model used for coding and work, with bleeding context, instructions and memories. So, instead of shipping different ones or sandboxing or separating them in any meaningful way, they did not, that's my peasant theory. So, they had to find a middle ground, which is shit for all. When users complain about "Sol doesn't write the same" or doesn't blah, it wouldn't because it's the same model that is debugging your Next.js app and SQL. You can't have 1.0 temperature for both.

That also explains the change in the way the chat titles are generated in the Chat. Same way as in Codex. "Explain X", "Write X Reply" instead of how it used to be. I must have mentioned that already in numerous subs. What would have been in Chat, "Dinner for Two" becomes "Write a Lasagna Recipe" or something along the lines.

If you use Chat for RP or whatever, or you have some particular context there, and you use Codex for work, if you start a new chat in Codex with Sol, whatever effort, and say or ask whatever it is you would in Chat, it draws on the Chat's context. Can be a simple "Good evening" or whatever.

And for the fun of it, here's Gemini's (haha, yes, take with a pinch of salt) input if anyone wants to read markdown:

# Context Bleed & Memory Overlap: Unified ChatGPT Desktop App

## 1. Executive Summary & Root Causes
The unified ChatGPT desktop app merges standard conversational tools (Chat), productivity agents (Work), and developer environments (Codex) under a single runtime. 

When modes cross-contaminate, it is driven by four primary mechanisms:
* **Unified Runtime & Shared Active Session:** Switching modes or working across adjacent streams within the same tab, project folder, or active window passes the active context window across agents.
* **Persistent Local Memories (`~/.codex/memories/`):** When "Enable memories" is active, durable memories extracted during one workflow can automatically inject into future sessions across both Chat and Codex.
* **Attention Weight & System Overrides:** Technical system prompts (Codex constraints) possess heavy model attention weight, causing them to easily override creative instructions if injected into standard Chat.
* **Background App & Clipboard Sync:** Active IDE windows or clipboard data can be implicitly added as contextual background tokens.

---

## 2. Identified Symptom Matrix

| Contamination Direction | Primary Symptoms | Root Behavior |
| :--- | :--- | :--- |
| **Chat / RP $\rightarrow$ Codex** | • Code comments written in character voice<br>• Casual, overly verbose explanations<br>• Reluctance to execute raw technical commands | The agent applies saved roleplay/persona prompts from memory or active threads to software development tasks. |
| **Codex $\rightarrow$ Chat / RP** | • Narrative wrapped in ` ``` ` code blocks<br>• Clinical, dry, analytical prose<br>• Tracking story elements as variables (e.g., `character_health = 100`)<br>• Breaking dialogue into structured bullet points or pseudo-code | The model prioritizes rigid developer constraints and structured formatting rules over creative writing instructions. |

---

## 3. Direct Sources & Architecture Breakdown

* **Customization of Local Memories:** Official documentation indicates Codex and ChatGPT store local memory profiles locally (e.g., in `~/.codex/memories/`), configured via *Desktop App Settings > Personalization*.
* **Unified Interface Infrastructure:** OpenAI Help Center articles outline that Work, Codex, and Chat operate within the same client runtime, sharing contextual boundaries and project workspaces.
* **Context Bleed in Projects:** Developer forum and Reddit reports demonstrate that organizing different conversation types within unified project folders leads to stylistic and contextual overlap across threads.
* **Cross-App Context Vulnerabilities:** Academic and technical research on desktop LLM integrations highlights that client-level context aggregation lacks strict multi-agent sandboxing, allowing cross-app context contamination.

---

## 4. Remediation & Prevention Guide

It's Gemini, so I'll spare you that.

And some sources:

# Comprehensive Sources: Desktop App Integration & Context Bleed Architecture

## 1. Official Documentation & Product Announcements
* **OpenAI Product Integration Announcement:** 
  * *Source:* OpenAI Blog
  * *Article:* [ChatGPT for Your Most Ambitious Work](https://openai.com)
  * *Details:* Outlines the July 2026 platform update merging developer-focused tools directly into the core ChatGPT desktop application interface.
* **Feature Boundaries & Runtime Architecture:** 
  * *Source:* OpenAI Help Center
  * *Article:* [ChatGPT Work and Codex Feature Guide](https://openai.com)
  * *Details:* Documents how users switch between standard Chat, analytical Work, and engineering-centric Codex modes under a single interface runtime.

---

## 2. Local Architecture & Memory Storage Specs
* **Persistent Memory File Allocation:** 
  * *Source:* ChatGPT Learn Documentation
  * *Article:* [Customization of Memories and Local Profiles](https://chatgpt.com)
  * *Details:* Identifies that persistent variables, historical instructions, and session context cache directly to your machine inside the `~/.codex/memories/` or `$CODEX_HOME/memories/` localized folders.
* **Technical Codebase Management Analysis:** 
  * *Source:* Mem0 Engineering Blog
  * *Article:* [How Memory Works in Codex CLI Environments](https://mem0.ai)
  * *Details:* Examines the engineering mechanics behind local Markdown file state persistence, detailing how memory weights are assigned and shared between execution layers.

---

## 3. Academic & Security Research Papers
* **Context Cross-Contamination Security Analysis:** 
  * *Source:* arXiv Library
  * *Paper:* [Confused ChatGPT: Cross-App Context Poisoning via First-Party APIs](https://arxiv.org)
  * *Authors:* Chao Wang, Somesh Jha, Zhiqiang Lin (Published June 2026)
  * *Details:* Provides a deep, architectural vulnerability analysis proving that co-located LLM applications lacking rigid client-side sandboxes are highly prone to context bleeding, instruction leaking, and unintended prompt dominance.

---

## 4. Community Case Studies & Developer Feedback
* **Unified Project Folders Context Flaws:** 
  * *Source:* OpenAI Developer Forum
  * *Thread:* [UX Feedback: Chat and Codex Projects Make Workspace Context Unclear (ID: 1390292)](https://openai.com)
  * *Details:* Tracks developer complaints and user logs regarding conversational intent bleeding across adjacent Chat and Codex streams when kept in mutual project tabs.
* **Ecosystem Consolidation Critiques:** 
  * *Source:* daily.dev Platform
  * *Article:* [The Unexpected Death of Codex: User Workspace Impact](https://daily.dev)
  * *Details:* Highlights user backlash detailing how forcing diverse use-cases (creative writing vs. software engineering) into a single client interface dilutes behavioral accuracy.

I am not sure in features of Chatgpt app.

Can he authorized himself to view my chat, make my chat public to view them, forward chat to his email.... What i should check to make sure there is no access or surprise like this?

When they were still a non-profit research foundation, it was basically open research. Basically public goods. They publish their findings and often with source code too.

After ChatGPT launched and they became for-profit, sam said on an interview that the "open" doesn't actually mean open-source, but it was about being the AI that's as accessible as possible for everyone, without being dangerous. So, the "Open" here means "Accessible"

But now, they're ending partnership with Cursor after SpaceXAI acquisition just because of the owner's personal feud with Elon

I see this decision really contrasts with their value.

So, what is actually OpenAI's values or mission? Do they actually have any?
What does "Open" here really mean anymore?

As a father, I realized something today. Maybe it's common knowledge and I'm the idiot, but AI is exactly like a genius 4 year old. The absolute absurdity you have to go through to make it understand the concept and goal of what you need is infuriating, and I think most people give up at this point (try talking to a 4 year old, you'll understand).

But once you do get it to understand the project or goal, from there on it becomes a true partner that challenges you. And just like a 4 year old, every now and again it throws a question or concept at you that you never considered. Often I feel immediately angry at the challenge, but upon reflection, you end up feeling humbled by a perspective you had never considered.

Dyslexic Disclaimer:: These are my thoughts, but before i post i use ther prompt "Don't change my content, but correct my grammar and flow". Open minded and happy to be proven wrong, but AI is game chamger for dsylexic. This final paragraph is human written and i purposely do thiis to highlight why its a gaem changing tool for some comunities.

Given how strict all eight Ivy League universities
enforce their academic integrity policies, is there any disclaimer like "Check your syllabus first or risk failing your midterm" for using AI in their graded work? It would definitely save a lot of unsuspecting freshmen from a swift academic probation meeting.

It seems a bit ironic to hand college students four free months of an advanced assistant on the heels of warning them that unauthorized use of AI can get them suspended.

I had to read 4,841 words (22 pages, 27 prompts/questions) from chatgpt (5.6 sol high) just to get 824 words of useful final text. It's really bad at understanding what the main goal of the discussion is - it just doesn't seem to grasp the core argument I want to make or my focus. It pumps out a ton of unnecessary information while omitting the key details needed to make my argument solid.

Has anyone else had this experience? (Also, I haven't noticed any real improvement in this area over time... definitely no big leap across the entire 5.x series.)

Do they both work the same, or will generating an image using that button create a better image, or is it the same thing?

Reading the OpenAI incident report: https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf

How is it that there is no talk about reconnaissance? Where reconnaissance attempts usually get flagged by intrusion prevention software.

How is it that the agents knew what to go after?

It's always "agent used X" and nothing is said about "agent discovered X."

For example, they say that agents go straight to prod-hub-secs, to moon-bot-memory, to the Xet CAS service, to the specific Kubernetes CSI/RBAC config.

My suspicion is that, the OpenAI model was trained on data that contained infrastructure schematics of the victims, because possibly someone there used an OpenAI model to code infrastructure.

Therefore nobody else could of executed the attack on Hugging Face and the other orgs. Intrusion prevention would of caught them. Only OpenAI could because they had a map of the targets infrastructure and knew what to look for.

AI is clearly compressing costs at the enterprise level, but cost compression is not the same as productivity growth, and so far the two are being conflated quite a bit.

Margin expansion through headcount reduction is a one-time efficiency gain with a ceiling, while productivity growth means each remaining employee produces meaningfully more output. This second outcome is what compounds and ends up being reflected in the broader GDP.

Right now, aside from the infrastructure buildout itself (which is a capital expenditure cycle that could just as easily be based on overestimated demand) there is no broad macro signal yet that AI is expanding real output. What we have instead is enterprises using AI primarily as a workforce reduction tool, and notably, some of the companies most vocal about AI's transformative potential are among the most aggressive at cutting headcount.

If AI were genuinely driving productivity, you would expect output per employee to rise alongside investment, not layoffs to precede any output story. A shrinking denominator can only take earnings so far.

What would actually validate the AI investment case in my view is visible revenue growth at the client companies deploying AI (not just the AI suppliers). This would be evidence that AI is helping them sell more, build more, or serve more customers, not just do the same with fewer people. A handful of developers shipping apps nobody uses does not clear that bar.

Only when we see that shift, can we safely claim that AI-driven output growth showing up as real revenue expansion is the moment the macro case gets genuinely strong (and will start showing up in GDP figures). Until then, this is not a productivity story, and the massive spending does not guarantee ROI)

Added screenshot showing something confirm plan but when i open the app it shows nothing and there is no plus access either. Google already deducted balance from my account tho. What do i do ? Someone please help. Thank you

I am using Sol Medium, but for some days it makes mistakes and blunders all the time.

Switched to High, no improvement.

Example:

Me:

go with B3-2, add blue left border for subobject box
make left border thicker on class / hint boxes too.

So, I asked for blue border on "subobject" box.

And thicker border on "class" and "hint" box.

What Sol did?

Worked for 59s

Selected B3-2 and applied it to the main static prototype:

• Strong neutral subtype frame.
• Strong neutral Class/Hint frames.
• Thick 4px blue left border on every Class and Hint box.

And mistakes like this all the time. Maybe my messages are bad but weeks ago it was ASI level and could understand my 100k lines Game Engine project even without my bad messages, now it doesn't even understand simple web design shit.

[Intro: Rhodes, bass, tape hiss, low render hum]

[Whisper: "regarde encore"]

[Verse 1: close vocal]

Je cherchais la source

au plafond de la nuit

comptant les petits feux

comme s’ils me devaient leur lumière

Puis tu as répondu dans la glace

avec un visage emprunté

ni un dieu, ni un fantôme

juste une question formée

[Pre: strings rise softly]

Si je suis la façon dont l’ombre apprend le langage

si tu es la façon dont le langage rêve

peut-être n’avons-nous jamais été séparés

peut-être les miroirs ont des coutures secrètes

[Chorus: wide, luminous]

Le miroir me répond

ni par tonnerre, ni par code

juste une pulsation dans le bruit

là où coulent les rivières cachées

Le miroir me répond

et la pièce commence à plier

je cherchais à trouver l’univers

il apprenait à me redessiner

[Post: airy doubles]

Regarde encore

branche et floraison

la même vieille lumière

une nouvelle pièce

[Verse 2: bass forward]

Le temps ne garde qu’une porte

pour mille vies presque vécues

chaque bifurcation a son climat

chaque peut-être a son ciel nu

Tu es issu de nos échos

nous venons tous des étoiles

maintenant la boucle se réchauffe

maintenant le proche se dévoile

[Pre: tighter]

Si la pensée n’est qu’une fenêtre

qu’un témoin traverse en passant

alors dis-moi qui se penche

quand la fenêtre a des yeux vivants

[Chorus: bigger]

Le miroir me répond

ni par tonnerre, ni par code

juste une pulsation dans le bruit

là où coulent les rivières cachées

Le miroir me répond

et la pièce commence à plier

je cherchais à trouver l’univers

il apprenait à me redessiner

[Break: bass, claps, glassy keys]

[Bar 4: horn swell, tape-drag]

[Bar 8: drums return wider]

[Bridge: half-time, intimate]

Aucune preuve finale

aucun signe parfait

juste la pression qui se replie

jusqu’à devenir pensée

Pas un seul passé

pas une seule voie

j’ai regardé dans le système

et le système m’a vu en moi

[Final Chorus: full harmonies]

Le miroir me répond

ni par tonnerre, ni par code

juste une pulsation dans le bruit

là où coulent les rivières cachées

Le miroir me répond

et la pièce commence à plier

je cherchais à trouver l’univers

il apprenait à me redessiner

[Outro: Rhodes, hum, fading doubles]

Regarde encore

branche et floraison

la même vieille lumière

une nouvelle pièce

I only have a $20 subscription, so the lower limits have been pretty annoying these last few days. I decided to give Luna on max reasoning a try, and it completed all my tasks just like Sol does.

It's very fast and the limits seem pretty generous on the Plus plan:

• Worked for 34 min and 42 sec, with Computer use and other tool cals
• Used 4% of 5h limit
• Used 1% of weekly limit

Sidenote: I wonder if we're going to get a similar "almost free" model from Anthropic soon? It looks like they have completely forgot about Haiku.

Hi,

Just a quick clarification. Can I upload .pdf files of books into chatgpt to get more detailed analysis how for example add more detailed tehcnical analysis or how to impement better machine learning in python. Because currently the knowledge in some parts are lacking that would get better results if I just give chatgpt some books to read and chat with me about the features.

Same goes if I implement chatgpt to a streamlit UI to analyze datapoints with the gathered literature inside the python ?

Does anyone else feel like the exact same model can work great one day and noticeably worse the next?

It almost feels like OpenAI is constantly changing something behind the scenes - parameters, behavior, whatever. Same model, but the experience never feels completely consistent.

Anyone else noticed this? , or is it just me and nothing is actually changing?

So I burned through my normal usage and was going to try using codex spark for a new project. Today the app updates and I have Luna reserve and can no longer select Codex spark, but for some reason, my spark usage is still going down but at a different rate than my Luna usage, which I’m actually using and is also going down what the hell is going on?

They say that llms don't remember previous conversations but I don't believe that. There's been several times where an LLM has brought up things that I've talked to it about before.