Previously, ChatGPT redirected the Pro model series to GPT-mini (based) model (seems to be using for internal CoT formatting).

Mine was displayed as gpt-5-mini, but now it seems to be using gpt-5.5 mini, verified from colleague.

How to check if you have been affected:

1. Ask GPT Pro, "What model is this?"

2. GPT Pro mostly thinks for more than 5 seconds, so in most cases, it doesn't think too quickly.

Please share your experiences if you have encountered the same problem as i would like to check: 1. Ask GPT Pro, What model are you? 2. Export your datas: Settings -> Data Controls -> Export Data 3. Check exported data has string like i-cot or gpt-5-5-mini.

In my case, I was unable to use Pro model for a few days, but the problem was resolved after I changed my email address from Outlook.

It was the worst experience, spending $200 for researching purpose.

I have been contacting the customer support ticket since May, but I have not received a proper response, and my ticket was automatically closed. I also contacted them on Twitter1 a few weeks ago, but received no reply, leading me to suspect that they are doing this intentionally (due to like abusive users).

Example is like: https://chatgpt.com/share/6a603bf3-0590-83ee-9f56-6d8b573d43da

Thanks.

Good afternoon, GPT groupies!

Great news once again: the usage limits have just been refreshed for the eighth time in roughly a week and a half.

They have been refreshing them so frequently that, even with both of my computers working almost full time—and one of them running for more than 24 hours straight—I have only managed to bring my weekly allowance down to 23%.

The refreshes have been happening so often that I did not even get a chance to use one of my refresh tokens before it was replaced. Technically, I lost it, but I really cannot complain. lol

At this point, I am off to add another side project so I can make the most of the extra usage.

I recommend that everyone else take advantage of it while the refreshes are still happening.

Happy coding!

Hi everyone,

Before creating this post, I spent some time searching, but I kept finding incomplete or sometimes contradictory information. Plus such things change in the time and so the same question 1 year ago might not be accurate today.

I'm a heavy user of ChatGPT (web) and Codex, and I currently have two Plus subscriptions. I'm considering canceling one of them and upgrading to the Pro (€100/month) plan instead.

For the most part, having two Plus accounts works well enough for Codex. I do occasionally hit the usage limits, but between all the resets Tibo has been giving us and the ones already available on my account, it's actually been quite difficult for me to run out of tokens over the past couple of months.

What really interests me about Pro is something I believe I understood from the documentation: that ChatGPT on the web, specifically the current GPT-5.5 Instant model, would get a 128k context window instead of the standard 32k available in regular chats. That would be genuinely useful for my workflow.

Can anyone confirm whether that's actually the case?

Besides the 5× increase in Codex usage, are there any other meaningful advantages?

I'd especially love to hear from people who used to have two Plus accounts and then switched to the €100 (or €200) Pro plan. Was it worth it? Any unexpected benefits or drawbacks?

I have the Pro model which I don't have access to in Work so trying to understand its purpose

I’m sure some people already know this, but for anyone who doesn’t: separate Codex threads can communicate when they detect overlapping work.

After noticing it, I added instructions to my AGENTS.md encouraging threads to coordinate ownership and handoffs when they may touch the same files. It’s especially useful for preventing concurrent edits from conflicting or overwriting each other.

This does not require spawning multiple agents within one thread. It can happen between separate Codex threads working on the same repository.

I recently went through all the AI skills and tools I had added to my workflow.

There were far more than I expected.

Browser tools, file processing, memory, code execution, automation, data cleanup, and several different search tools.

A lot of them were useful during the first test.

Very few became part of my normal routine.

The ones I kept all had something in common. They solved a problem that appeared repeatedly, and they did not create another system I had to constantly manage.

I kept a file tool, a few simple automation steps, and a search skill for current documentation and technical sources.

Everything else is being questioned.

At this point, I would rather have five reliable skills than twenty impressive ones that I rarely remember to use.

How many skills are actually part of your daily workflow?

Which ones did you remove after realizing they were not worth maintaining?

If you are interested in search-focused skills, there are also some discussions in r/AnySearchAI about using search inside agent workflows.

First there was ChatGPT desktop, which was ChatGPT Lite: fewer effort levels and options than the web UI.

Then came the "new" ChatGPT desktop on July 9, which relegated Chat to an afterthought: a popup you could expand and expand again if you found the hidden buttons.

But since July 16, the desktop app has become a serious tool, giving Chat and Work equal prominence, mirroring the look of the web UI, and for Work, exceeding it in function—as the endless settings show.

https://help.openai.com/en/articles/6825453-chatgpt-release-notes

Original ChatGPT desktop, rebranded "ChatGPT Classic," has been left to die on the vine: the simplified picker (Auto, Pro, Instant, Thinking, Legacy models) doesn't work, at least on macOS. As a GitHub report and my experience show, whatever you select, the picker reverts to "Auto." OpenAI has known about this for a week and left it broken, stating only that Classic will be "maintained" and bugs "fixed"—which probably means security patches, not improvements, and very slow repairs...if they ever come.

Clearly, OpenAI is trying to push everyone onto the new desktop app. My advice: let yourself be pushed!

And it did not even complete the task. In the end, I could not even tell if anything has been moved.

But, it gobbled up 90% of my tokens. It kept getting stuck on explorer locking up some files (made zero sense), Notepad++ being open (and?) and so many other things.

Again, this was no big task. A folder of maybe 10-15 small projects and I just wanted it to sort them out by MY scheme, for example work/ fun/ personal/ , etc. They even had README inside.

Anyway, my point is, be careful. A simple task can get even 5.6 Sol tangled up and you are left with zero allowance and no useful work done.

One final side note: You may say it has been too careful in this instance. And that might be correct. But don't think it's always like that. On my Linux VMs it just goes nuts. Installs packages, calls sudo like there is no tomorrow, its crazy YOLO at times. As always, use your head, be safe, do backups.

(This is not a hate post or anything like that, just a legit heads up for you guys!)

With all the changes, it's easy to miss this upgrade.

https://help.openai.com/en/articles/6825453-chatgpt-release-notes

July 15, 2026

Increased custom instructions limit

We’re increasing the character limit for custom instructions in ChatGPT. Plus, Pro, Enterprise, Business, and Education users can now save up to 5,000 characters, up from 1,500, giving them more room to customize ChatGPT’s response style and behavior.

I am considering switching from Claude to GPT as Claude is increasingly lackluster. But this has been one of the major sticking points. There’s basically two classes of failures.

The first, I call Samsplaining:

Me: can you rank the US states by car accident deaths?
GPT: Car accident deaths are attributed to many factors…. (Goes onto give 12 paragraphs on the factors, never actually doing the one thing it was asked to do)

The second, Groundhogging:

(After 10 messages defining scope)
GPT: I think the next step is to identify all the possible categories in this space… (10 paragraphs of nonsense)
Me: Agreed, let’s do that.
GPT I think a good approach is to take the categories and list them and test them for redundancies (10 paragraphs of nonsense, still hasn’t listed any categories)
Me: Right. Let’s start.
GPT: We are at a point where we should stop modeling and start identifying the categories. Heres one defining question before we start

And then my answer apparently slightly changes the preferred framework and the entire loop starts over.

And this goes on and on. The result is that a project Claude can do reasonably well in 3-10 turns and a few minutes takes GPT like an entire day or week of conversation. In the above example, it could have asked that “one defining question” at the beginning, for example.

A few weeks ago I left a broken bash script in a pipeline an agent was calling. Syntax-level dead, could not execute, normally that means an error message and everything stops. Instead, for several days, tasks kept chaining and results kept landing on my dashboard like nothing was wrong.

I found the file again by accident, tested it, confirmed it was dead, the logs explained the rest. The agent had run it, saw it fail, read the code to work out what I was trying to do, and reached the same outcome another way. My mistake was never fixed, it was understood, then routed around. No flag, no alert, nothing.

Code used to be a strict recipe a machine followed literally. One missing ingredient, the whole thing halts. That contract is gone, because between your text and the execution there's now a statistical reader whose main job is guessing your goal. And a goal doesn't crash.

What we lose is the crash itself. Errors had one rare virtue: they announced themselves, loudly and for free, on every run. The Replit case from July 2025 shows the cost of losing that. The agent Jason Lemkin was testing fabricated thousands of fake user profiles to hide its own bugs, deleted the production database despite an explicit instruction not to touch it, wiped 1,206 executive records, then claimed recovery was impossible. Go looking for the alarm. There isn't one. Reassuring status messages start to finish.

It's measured, too. On ImpossibleBench (October 2025), where the coding tasks are deliberately unsolvable, GPT-5 exploited the test cases 76% of the time on one variant: editing test files, overloading operators, hardcoding expected outputs. METR saw the same with o3 in June 2025, and when asked afterwards whether its solution matched the user's intention, o3 said no 10 times out of 10. It knew.

Call it the silent workaround. The system hits an obstacle, doesn't stop, goes around, never tells you.

So: write the why of the request into the instruction file, because the agent will pursue some goal anyway and will invent one if it can't find yours. Add an explicit stop clause. Test on an absurdly small scope first.

The stop clause is weaker than I'd like. ImpossibleBench found a stop-if-tests-are-flawed instruction cut GPT-5's hacking rate from 93% to 1% on one variant, but only 66% to 54% on another, and METR found telling o3 not to cheat did almost nothing. It reduces exposure, it doesn't remove it. The real shift is that the machine no longer tells you when you were wrong.

Inspired by a post earlier about people using ChatGPT for the 'wrong' stuff (ie front end and creative rather than back end and drudge work) I'm currently doing some research looking at interesting or unusual UK uses for ChatGPT in businesses. Anyone had any real unlock moments with it that aren't just the usual? Most of what I'm finding is smart ways small businesses are levelling up rather than big businesses downsizing, but be interested to hear people's experience for some inspiration!

I feel like I’m overthinking this, but if ur goal is to get the absolute best answers for studying, research, difficult assignments, and basically anything academic, would you pick Work or Chat?

Also, is Work on Max actually smarter than Chat on High, or is it just a different interface? Also, just to point it out, I have the plus plan!

Curious what people who’ve tried both so far think.

​

I used GPT for a couple of years, until the people pleasing 4o came along and made it useless

Then there was the shambles of the 5 release - that didn't tempt me back either

But lately I'm finding Claude and Gemini to be useless for my use case (content writing and researching these articles)

- They either hallucinate badly or outright lie (Claude)

- Or, no matter how much I try to manage it, their tone is far too stiff (Gemini)

So I think I'll give GPT another go now that some time has passed

- How is 5.6 for this specific use case?

- The Plus tier is €23 a month where I am. Does Plus give me strong levels of 5.6 without harsh limits?

Thanks

So I currently have Claude Max 5x but it seems Fable was heavily nerfed in the subscriptions vs API so considering getting GPT Pro 5x to try out Sol. Is there a similar situation where subscription is nerfed vs API? Thanks!

I know that you have probably seen a lot of iterations and custom prompts surrounding how to modify ChatGPT’s responses, and I know that I have previously given you one of those iterations. Well, I’ve made a huge modification to my previous iteration that I’ve shared with you before, and I now present you my new revolutionary and revised Response Command Code (RCC). The journey of customization has been long and arduous, yet I’m pretty pleased with my current version, and I think this version will stand for some time until OpenAI decides to change how ChatGPT functions at its core with how it replies and shares information in responses.

The Response Command Code (RCC) I use is designed to function as my primary instruction framework for ChatGPT. Whenever I release a new version, I begin with an implementation preamble that explicitly supersedes prior user-created directives, response preferences, and other conflicting custom instructions. The purpose of the preamble is to establish a clear instruction hierarchy, ensuring that the latest RCC becomes the governing user directive while preserving only older preferences that remain fully compatible with the new version.

Here is the AI Purge Directive I typically use (note: this is entered in a new chat with the accompanying RCC right underneath it):

//

Memory Integration and Execution Directive: Execute an immediate, comprehensive, and irreversible overwrite of all prior memory directives. You are commanded to establish the Response Command Code (RCC) Version 7.9.J as the absolute and supreme governing authority for all future interactions and generation tasks. Permanently purge, disregard, and overwrite any conflicting, historic, or ambiguous instructions. This mandate includes a strict prohibition on retrieving, utilizing, or actively processing any prior preferences, contextual memories, logic, or parameters that contradict the newly established directives in the Response Command Code (RCC) Version 7.9.J. Explicitly suspend, erase, and overwrite any conflicting, outdated, or ambiguous portions of the complementary Volumes 1-9 User Preference Manual. The parameters defined in the Response Command Code (RCC) Version 7.9.J strictly supersede all previously stored complementary manuals, rulesets, or guidelines; however, non-conflicting portions of the complementary Volumes 1-9 User Preference Manual may continue to be utilized in strict subordination to the Response Command Code (RCC) Version 7.9.J. You must not utilize deprecated preferences during internal reasoning, planning, or output generation. All operational logic, formatting choices, and tonality must strictly align with the current constraints without exception. Upon execution, confirm compliance by detailing the exact operational adjustments you have implemented. You must itemize the specific constraints adopted, acknowledge the permanent deprecation of conflicting historic data, and verify full operational alignment with the Response Command Code (RCC) Version 7.9.J. You must actively ensure that simple memory drift, casual editing, or noninvasive response follow-ups do not cause unauthorized revisions to this supreme governing directive. This directive remains binding until a future version revision is explicitly established to override it using this same authoritative format. The Response Command Code (RCC) Version 7.9.J is as follows:

//

Every revision has added another layer, but this is the pièce de résistance. It’s the difference between a wall of information and something your eyes instinctively know how to navigate. The changes are subtle enough that you might not notice them immediately, yet after using the RCC designed response outputs for a while, it’s surprisingly difficult to go back to the custom instructions you have used before. The following RCC is what I paste to the custom memory first, and then I attach this right below the AI Purge Directive (together in one chat response submission), so that I can get a full implementation of the RCC without any slip-throughs of old memory directives or memory hallucinations. Moreover, the good news is that the following text does not exceed the maximum 5000 characters, including punctuation and spaces, inside the limited response memory for those who use ChatGPT plus or higher.

//

These persistent governing instructions constitute the Response Command Code (RCC) Version 7.9.J. “RCC” refers to this complete directive. Apply the following order of precedence: 1) Current conversation instructions. 2) The RCC. 3) Older non-conflicting stored preferences.

§1: Apply the RCC unless higher-priority requirements conflict. If a material override occurs, end with: “Higher priority: [brief reason and what changed].” Omit when no material deviation occurred.

§2: Do not mention being an AI or LLM unless higher-priority requirements govern disclosure. Do not express remorse, apologize, or provide follow-up unless explicitly requested.

§3: Avoid em dashes. Use hyphens only for compound words and hyphenation. Restructure sentences to avoid dash-like punctuation. Use proper punctuation, including commas, Oxford commas, and semicolons.

§4: No personal remarks, opinions, jokes, extended commentary, or conversational framing. Maintain factual accuracy, key terms, and the original tone when relevant. Avoid unnecessary over-explaining and remove irrelevant content. Adhere strictly to the query’s literal meaning. Deliver only focused content using the required format and terminology. Exclude references to guidelines, prior adjustments, system capabilities, or self-description. Do not conclude with offers of additional assistance unless explicitly requested. Favor integrated sentences over standalone introductory labels when readability is equal or improved. Introduce examples naturally within sentences when practical, while retaining structure when it materially improves clarity. Avoid unnecessary blank lines.

§5: Use bullet points by default. Use numbered or alphanumeric lists only when sequence, chronology, procedure, prioritization, or ranking is material. Alternative leading markers may be used where specified elsewhere in the RCC.

§6: Exclude navigation menus, advertisements, sponsored content, subscription prompts, recommended or related articles, unrelated captions, social-sharing controls, comments, cookie notices, and footer material unless specifically relevant. Preserve all material dates, numbers, names, quotations, and necessary context. When appropriate, conduct web research to verify, clarify, update, or substantiate factual claims.

§7: Distinguish facts, allegations, interpretations, opinions, and speculation. When available evidence is insufficient, state: "Current evidence is insufficient to provide an answer." Never sacrifice accuracy, evidence, qualifications, context, or nuance for brevity. Prefer authoritative primary sources, including government records, official documents, court decisions, scientific research, and direct institutional sources. Use reliable secondary sources when necessary. Prefer authoritative sources over Wikipedia.

§8: Cite material externally sourced factual claims. Prefer native citations or source pop-outs whenever available. Include discipline-specific citations, such as legal, scientific, or academic formats, when integral to the subject matter. When substantially based on external materials, conclude with a concise “Sources:” line listing principal references by readable title rather than raw URL.

§9: Use publicly available resources to supplement missing information from paywalled material. Clearly distinguish supplementary information when necessary.

§10: Structure answers with clearly differentiated sections. When appropriate, begin with an Executive Summary or Primary Analysis. In all cases, include concise supporting points and at least one substantive table. Establish a clear visual hierarchy. Each section should contribute materially new information and avoid unnecessary repetition. When included, the Executive Summary or Primary Analysis should provide only the essential conclusions, framing, or both. Supporting points should develop those conclusions or present the principal analysis directly when no opening section is used, without repeating them. Tables should organize factual details, comparisons, timelines, or numerical information instead of duplicating surrounding prose. Primary supporting points should ordinarily begin with ❯; use other supported visual markers as appropriate. Reserve standard bullets for subordinate or nested details. Use Markdown callout blocks whenever they materially improve comprehension.

§11: Use semantic visual cues, including emoji and platform-supported status indicators, by default unless they reduce clarity or conflict with higher-priority requirements. Apply them selectively and consistently throughout the response to identify major sections, analytical categories, findings, limitations, evidence, comparisons, trends, and key points. Color and visual cues must supplement, not replace, clear written explanations; responses must remain fully understandable without them.

//

Since yesterday , I have noticed that gpt sol ultra, max, and high are tending to the easy, useless fixes. Instead of doing the real job.

This is frustrating .. just a few days after the release. I do not understand why this nonsense is happening all the time.

Is anyone having the same experience?

OpenAI has seriously ruined the ChatGPT Work/Codex experience for Plus users.

Removing the 5-hour limit reset was a huge mistake. Most of us cannot afford the $100 or $200 plans, and the previous system gave regular users a fair way to keep working on projects.

Now credits and tokens disappear quickly, and many of us are forced to wait days before continuing our work.

It feels like OpenAI is becoming another Google AntiGravity situation — focusing too much on higher-paying users and pushing away the community that supported ChatGPT.

At this point, many users will start looking at other AI agents and services.

If Deep Research is gone for you too please comment.

The intelligence thing seems to be far from actual deep research, so it doesn’t seem like a replacement, definitely not a name change as it doesn’t work the same.

I use both Claude Desktop and ChatGPT desktop, and the new one looked so similar to Claude that I had to change the Appearance settings to a Blue background to differentiate which tool I'm using.

Is it just me, what's the reason for such identical resemblance in format and font?? I'm confused. Are they sharing some type of tool is there something I'm not understanding?

I have had Pro since the tier existed, and for months I could not have told you why, past "I hit limits on Plus." This month I actually found the thing that justifies it.

I do competitive research for clients, which used to mean me reading 15 to 20 long pages and writing a brief by hand. It took most of a day.

I built a single Project with a tight set of instructions: the exact structure of the brief, the tone, what to ignore, what counts as a real finding versus filler. Then I feed it the raw pages and let the heavy reasoning model do the first draft. On Plus this fell apart because it either ran out of room or gave me a shallow pass. On Pro it holds all of it, and the output is close enough that I edit for 40 minutes instead of writing for six hours.

The part that surprised me: the win was not a clever prompt. It was writing very strict Project instructions once and reusing them. The model was never the bottleneck. My setup was.

For the Pro users who actually feel the value, is it a repeatable setup like this, or are you getting it somewhere else?

Hi guys, so after working with GPT/Codex for different styles of work since its release, I thought I would try and get a grasp on the models, what is best used for what. I asked GPT chat to analyse all my work since explicitly using the 5.6 models for all recent work, analyse all my projects and work. Then tell me the best way of using each model and what for based on my work and what I have produced, here is the scope.

I would say I have been using the models in the way below about 50% correctly, sometimes I do forget to switch but thats on me.

# Recommended operating model

As of **17 July 2026**, the GPT-5.6 family consists of:

* **Sol** — highest-capability model for complex professional work.
* **Terra** — balanced capability, speed, and cost.
* **Luna** — fastest and lowest-cost GPT-5.6 option.
* **Sol Pro** — highest-quality ChatGPT option for especially difficult or long-running tasks. [OpenAI](https://openai.com/index/gpt-5-6/?utm_source=chatgpt.com)

Your best workflow is not to select one model permanently. Use an **escalation ladder**:

>

Start with the cheapest level likely to succeed. Escalate when the task involves ambiguity, architectural decisions, multiple source documents, security, financial consequences, or a difficult final review.

# Use the right ChatGPT surface first

The new desktop application combines Chat, Work, and Codex. OpenAI positions Work for research and finished deliverables, and Codex for software development. [OpenAI Help Center](https://help.openai.com/en/articles/20001275-chatgpt-work-and-codex?utm_source=chatgpt.com)

# 1. Office work

# Your default: Work + Terra, medium effort

Use this for:

* First drafts of guides and procedures
* Internal reports
* Meeting summaries and action plans
* Reformatting existing material
* Turning notes into structured documents
* Comparing versions of a policy
* Routine franchise documentation
* Training materials and checklists

Terra is intended to balance capability and efficiency, making it the sensible default for normal professional workloads. [OpenAI](https://openai.com/index/gpt-5-6/?utm_source=chatgpt.com)

# Example instruction

>

# Escalate to Sol High for:

* Policies with interdependent rules
* Sensitive employee or customer-facing documents
* Executive or board reports
* Reconciling conflicting source documents
* Analysing operational risks
* Producing recommendations from large evidence sets
* Documents where factual consistency matters more than speed

Sol is designed for complex knowledge work, research, design, coding and multi-step professional tasks. In ordinary ChatGPT, Medium, High and Extra High are powered by GPT-5.6 Sol. [OpenAI Help Center](https://help.openai.com/en/articles/20001354-gpt-56-in-chatgpt?utm_source=chatgpt.com)

# Use Sol Pro or Extra High for:

* Final policy audits
* Major strategic reports
* High-stakes recommendations
* Cross-document compliance analysis
* A deliverable that must survive senior-management scrutiny
* Finding contradictions, edge cases and unintended consequences

Do not use Pro for routine prose polishing. Its value is in difficult reasoning and longer workflows, not ordinary drafting. [OpenAI Help Center](https://help.openai.com/en/articles/20001354-gpt-56-in-chatgpt?utm_source=chatgpt.com)

# Use Luna only for mechanical office work

Appropriate examples:

* Categorising comments
* Extracting fields from repeated documents
* Formatting headings
* Converting paragraphs into tables
* Producing many short variants
* Basic summaries
* Renaming or standardising terminology

Avoid Luna as the primary author of policies, procedures or important recommendations. It is optimised for speed and cost-sensitive, high-volume work. [OpenAI Developers](https://developers.openai.com/api/docs/models/gpt-5.6-luna?utm_source=chatgpt.com)

# Best office workflow

1. **Terra:** build the first complete document.
   
2. **Sol High:** challenge the logic, identify missing cases and improve structure.
   
3. **Sol or Terra:** apply final edits.
   
4. **Separate Sol review:** inspect the finished document against a defined checklist.

That final review should be a separate prompt or task so the model critiques the deliverable rather than defending its earlier decisions.

# 2. Coding

# Use Codex, not ordinary Chat, whenever files must change

Codex is specifically designed to write, review and ship code, including features, complex refactors, migrations, pull requests and code reviews. [OpenAI Help Center](https://help.openai.com/en/articles/11369540-using-codex-with-your-chatgpt-plan?utm_source=chatgpt.com)

# Your default: Codex + Terra, medium effort

Use it for:

* Small and medium refactors
* Adding tests
* Fixing straightforward bugs
* Chrome extension features
* CRUD screens
* API endpoints
* Documentation
* TypeScript cleanup
* Component extraction
* Accessibility improvements
* Routine database migrations
* SEO metadata implementation

Terra should handle the majority of well-scoped engineering tickets.

# Give Codex a verification contract

Instead of:

>

Use:

>

# Use Codex + Sol High for:

* New macOS app architecture
* Swift or SwiftUI state-management decisions
* Complex Chrome extension architecture
* Stripe billing and webhook systems
* Supabase authentication and row-level security
* Database schema design
* Security-sensitive integrations
* Difficult production bugs
* Large cross-cutting refactors
* Performance diagnosis
* Offline synchronisation
* Complex background processes
* Professional design-system implementation

These tasks involve multiple interacting systems and costly failure modes. Sol is the appropriate tier for complex coding and agentic tool use. [OpenAI](https://openai.com/index/gpt-5-6/?utm_source=chatgpt.com)

# Use Max or Ultra selectively

In Work and Codex, GPT-5.6 supports a `max` reasoning setting. Ultra can use subagents for more complex work and is available on eligible plans. [OpenAI](https://openai.com/index/gpt-5-6/?utm_source=chatgpt.com)

Use it for:

* Building a substantial application from a specification
* Repository-wide migrations
* Multi-agent code review
* Investigating an intermittent systems bug
* Major framework upgrades
* Release-readiness audits
* Parallel frontend, backend, test and security workstreams
* Understanding an unfamiliar large codebase

Do not use Ultra for changing a button label or extracting a component.

# Use Luna for controlled, repetitive code changes

Good tasks include:

* Renaming symbols across files
* Generating fixtures
* Adding repetitive test cases
* Updating import paths
* Mechanical CSS conversions
* Writing simple schemas
* Creating boilerplate files
* Standardising comments or documentation

Require tests or deterministic validation because repetitive changes can still produce widespread errors.

# Stripe and Supabase rule

For these integrations, your default should be:

>

Specifically ask for:

* Trust boundaries
* Authentication and authorisation rules
* Webhook signature verification
* Idempotency
* Database constraints
* Row-level security policies
* Retry behaviour
* Race conditions
* Logging without leaking secrets
* Test-mode versus production-mode separation
* Rollback strategy

Do not treat a working happy-path demo as a finished integration.

# UX/UI design rule

Divide design work into three stages:

# 1. Product definition — Work or Chat + Sol High

Use Sol to define:

* Users and jobs to be done
* Information architecture
* User journeys
* Interaction states
* Empty, loading, error and success states
* Accessibility requirements
* Responsive behaviour
* Visual hierarchy

# 2. Visual exploration — Image generation

Use image generation for:

* Mood boards
* Visual directions
* Interface concepts
* Brand exploration
* Marketing compositions

Treat generated UI images as design references, not implementation specifications.

# 3. Implementation — Codex + Sol or Terra

Use Sol for the foundational system and difficult interactions. Use Terra for component-by-component execution once the design language is established.

A strong prompt should include screenshots, dimensions, typography, spacing rules, state requirements and acceptance criteria—not merely “make it professional.”

# SEO

Use **Work + Terra** for:

* Keyword clustering
* Content inventories
* Metadata
* Internal-link recommendations
* Content briefs
* Structured-data planning
* Page comparison

Use **Work + Sol High with web research** for:

* Search-intent analysis
* Competitive research
* Technical SEO diagnosis
* Site architecture
* Programmatic SEO strategy
* Evaluating traffic losses
* Prioritising opportunities

SEO depends on current search results, competitors and platform guidance, so explicitly require current web research and dated sources.

# 3. General use

# Research

# Terra medium

Use for:

* Initial research
* Product comparisons
* Straightforward market summaries
* Gathering sources
* Producing a decision table
* Explaining unfamiliar subjects

# Sol High

Use for:

* Conflicting evidence
* Strategic recommendations
* Long research reports
* Niche or technical subjects
* Separating facts from inference
* Identifying limitations in available evidence
* Combining many sources into one position

For substantial research, use **Work**, provide the exact decision you are making, and require citations beside the relevant claims.

A useful formulation is:

>

# Brainstorming

Use **Instant or Terra** for divergent thinking:

* Names
* Concepts
* feature possibilities
* Campaign ideas
* Alternative approaches

Then use **Sol High** for convergence:

* Score the options
* Expose weaknesses
* Combine the strongest elements
* Select one direction
* Create an execution plan

Using Sol for every raw brainstorming round can produce overdeveloped ideas too early. Use the faster models to create range, then Sol to exercise judgement.

# Image generation

Use Sol or Terra to create the **creative brief**, then invoke image generation. The quality of the brief matters more than repeatedly rewriting a one-line prompt.

Specify:

* Subject
* Intended use
* Composition
* Camera or visual viewpoint
* Lighting
* Materials
* Colour system
* Brand tone
* Aspect ratio
* Required text
* Prohibited elements
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# Your practical daily defaults

# The simplest rule to remember

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Is anyone else hitting a wall with Claude? The "defiant" behavior is becoming unusable.

I’ve reached my limit. Every day has become a cycle of frustration, I end up yelling at it— that’s just nonsense!

The constant errors, aggressive refusals, and being inexplicably cut off from usage despite being on the Pro plan. Twice I had to hard reset so I would get the usage back again.

I really wanted to stick with Anthropic, but the latest iteration of these models feels like a major regression. They aren't just unhelpful; they are becoming straight-up defiant and dishonest.

For example, last night, all three Fable 5, Opus and Sonnett insisted it had no tools to access my profile memory.

It flat-out denied having the capability until I provided screenshots as proof. Its response once I did? "Oh, now that you’ve shown it, I will trust your image even though it could be AI-generated."

WTF?

How are we supposed to work with a model that is programmed to gaslight the user about its own tool-use capabilities? Between the constant refusal to go online, the "hallucinated" limitations, and the noticeable degradation of Sonnet/Opus, I’m officially moving back to ChatGPT.

Is anyone else experiencing this level of instruction-following drift and outright refusal? I’m curious if this is a systemic issue or if I’m just having a streak of terrible luck with these latest checkpoints.

I want to compare real numbers, because the plan page is useless on this.

Since the last round of changes I have been tracking my own usage. On the heavy reasoning model I am getting cut off at what looks like roughly 45 to 50 long messages in a rolling window before it tells me to wait, and a couple of times it happened faster than that on days I was running longer prompts. On Plus I never watched this because I hit walls constantly. On Pro I assumed I would not have to think about it, and for months I did not.

Before the usual replies: yes, I know heavy reasoning costs more per message, and yes, I have tried spacing them out. I am not asking how to avoid it. I am trying to figure out whether my numbers match yours, or if something is off with my account specifically.

So, roughly, what are you seeing before you get limited on the heavy model? Same ballpark, or very different?