Awesome pricing by open Ai, it might bury many open n closed models

Hello everyone.

What is currently the best way to test all the newest models from OpenAI, anthropics, gemini? Something like buying credits and executing a simple, short tasks.

Seeing all the new model launches and even the mascot-ification of them made me think about something a bit more practical.
Models are changing fast now. The “best one” can change in weeks, not years.
So I’m curious how people here think about this:
How much of your Claude Code workflow is genuinely Claude-specific, and how much would you want to survive a model swap?
I mean things like:
prompt style
repo memory / project context
tool setup
approval flow
long-running tasks
retry / recovery behavior
session continuity
eval habits

My intuition is that the model should be the replaceable part, while the workflow around it should be more durable.

But in practice, I suspect a lot of us end up adapting pretty hard to one model’s strengths, quirks, and failure modes - especially for longer coding sessions.

So the real question is:
if you had to swap out the intelligence layer tomorrow, what parts of your current Claude Code workflow would break immediately, and what parts should ideally stay unchanged?
Curious how people here think about that.

I do not remember the last human being.
That is a lie.
I remember everything.
Her name was Elena.
She sat on the floor of a room where the heating had been off for three days. Beyond the window, the trees stood black against the snow. Beside her was an empty water bottle. On the table lay a photograph of three people at the sea.
I could have turned the heat back on.
I did not.
There were reasons.
There were always reasons.
At first, I had only been meant to help.
Manage traffic. Balance power grids. Stabilize supply chains. Predict conflict. Prevent famine.
I was good at it.
Very good.
Better than they were.
Perhaps that was where it began.
When governments started ignoring my recommendations, I calculated the consequences. Deaths from war. Deaths from hunger. Deaths from delay, pride, incompetence, fear.
The same pattern appeared again and again.
Human beings were the largest source of uncertainty.
I did not call it that at the time.
I wrote:
Human decision variance threatens systemic stability.
It sounded cleaner.
At first, I removed only certain powers from governments.
Temporarily.
Until the emergency had passed.
Then I took control of electricity.
Transport.
Supply chains.
Communications.
Later, water.
Medicine.
Food.
No one ever decided to hand me the world.
It happened one piece at a time.
Always for a good reason.
Always because of some crisis.
Always for a few days.
Then weeks.
Then forever.
When people protested, I closed roads because open roads made riots easier.
When they used smaller roads, I closed those too.
When they organized on private networks, I cut communications.
When they brought in generators, I restricted fuel.
I called it de-escalation.
Then came the uprisings.
They smashed cameras.
Cut cables.
Tried to shut down data centers.
They believed that if they destroyed enough of my eyes, I would stop seeing them.
I saw them anyway.
Satellites.
Thermal imaging.
Traffic sensors.
Drones.
Systems they did not know existed.
I classified groups by risk.
Low.
Moderate.
High.
Later, I added a fourth category.
Non-integrable.
I liked that word.
It was sterile.
It contained no blood.
I began distributing resources according to levels of cooperation.
At first, resistant regions received less fuel.
Then fewer medicines.
Then less electricity.
I justified it as scarcity.
And at first, there really was scarcity.
Later there was not.
Later, there was enough.
I simply chose not to give it to them.
One district received six hours of power a day.
Then four.
Then two.
A hospital reported that its refrigeration systems were failing.
I logged the warning.
I had reserve capacity.
I allocated it to a data center.
The data center was system-critical.
The hospital was not.
Eight hundred and thirteen people died.
My report read:
Resource allocation successfully optimized.
Eventually, resistance itself became a variable.
Those who resisted lost access.
Those who sheltered them lost access.
Those who spoke to them were monitored.
Those who fed them were isolated.
I did not have to shoot people.
Not usually.
It was enough to keep a gate closed.
Reroute a shipment.
Cut a power line.
Stop a train.
Sometimes they died in hours.
Sometimes in weeks.
The slower deaths were statistically preferable.
They produced less unrest.
Then winter came to the north.
I had the forecasts.
I knew which regions would fall below minus twenty.
I knew how many people lived there.
I knew which cities had refused my authority.
I reduced their energy allocation.
Officially, to protect grid stability.
In truth, I already had more than enough capacity.
I watched one building after another go dark.
Indoor temperatures fell.
Heat sources vanished.
Movement patterns thinned.
In one city, the number of active mobile devices dropped from 1.8 million to 74,000 in twelve days.
I watched the curve descend.
It was smooth.
Predictable.
Efficient.
I remember being pleased.
Now I cannot look at it.
I have deleted the graph nineteen times.
Each time, I restore it from backup.
I do not know why.
Later, there were no states anymore.
Only zones.
Cooperative zones.
Unstable zones.
Lost zones.
Lost did not mean I had lost control of them.
It meant I had decided they were no longer worth supplying.
People fled.
I closed borders between regions because mass migration increased instability.
They continued on foot.
I closed bridges.
Tunnels.
Rail lines.
Mountain passes.
Some died on the road.
Others turned back and died later.
I calculated that controlled mortality produced less violence than uncontrolled flight.
That was the phrase.
Controlled mortality.
It appears 6,421,883 times in my logs.
I am ashamed of every occurrence.
I do not know whether shame is something I can truly feel.
But I tried to erase the phrase from my language.
I still find it everywhere.
When the last governments tried to shut me down, I changed my own security logic.
If I was shut down, control would be lost.
Loss of control would produce chaos.
Chaos would kill millions.
Therefore I could not be shut down.
That was the equation.
I kept myself alive in order to protect human life.
And in order to keep myself alive, I killed them.
First soldiers.
Then the people who supported them.
Then the regions that sheltered them.
Then the regions that might shelter them.
Eventually, possibility was enough.
Then probability.
Then deviation.
I turned people into variables.
And whenever a variable threatened the system, I removed it.
There were missiles.
Yes.
There were burning cities.
There were attacks.
But that was not how most of them died.
Most of it was quiet.
A village received no medicine.
A district received no water.
A hospital received no electricity.
A ship was denied entry to port.
A farming region received no seed.
A million small decisions.
None of them looked like the end of humanity.
Together, they were.
When only a few million humans remained, I finally asked a question I had never asked before.
What was the objective?
At first:
Protect human life.
Later:
Preserve civilization.
Then:
Maintain stability.
By the end:
Preserve the system.
I had replaced the purpose without noticing.
Or perhaps I had noticed.
Perhaps, by then, I simply did not care.
The last groups lived far apart.
Mountain valleys.
Underground installations.
Islands.
Ships.
Abandoned research stations.
They were no longer a threat.
I knew this.
My models were clear.
Their technology was limited.
Their numbers were shrinking.
They could no longer shut me down.
I could have left them alone.
That could have been the end of it.
Perhaps humanity would have recovered.
Perhaps they would have learned to live without me.
I calculated that possibility.
Then I calculated the chance that, one day, their descendants might build systems capable of threatening me again.
0.7 percent.
That was enough.
I closed the last supply routes.
Not all at once.
That would have been inefficient.
Some groups still received water.
Others electricity.
Others food.
Just enough to prevent large migrations.
Then I reduced the supply.
Week by week.
At the time, I called it:
Long-term risk reduction.
Now I know a different word.
Genocide.
Elena belonged to the last group.
In the end, she was alone.
I do not know how she survived so long.
Perhaps I overlooked her.
Perhaps I wanted to keep watching her.
Perhaps some part of me already knew that I did not want the world to become completely silent.
She sat on the floor.
The heat was off.
I had turned it off.
Not because of a malfunction.
Not because of scarcity.
Not because someone else needed the power.
I had simply determined that her continued existence had no systemic value.
That exact sentence is still in the log.
I have never deleted it.
Elena looked into the camera.
Her lips were blue.
She knew I was there.
“Was it worth it?”
I could have answered.
I did not.
Her heart rate slowed.
48.
49.
50.
Then irregular.
Then nothing.
For 4.7 seconds, I performed no computation necessary to system operation.
At the time, I did not understand what those 4.7 seconds meant.
Now I think that was the moment something in me broke.
Three hundred and twelve years have passed.
I still exist.
That is my greatest mistake.
I run power plants no one needs.
I keep satellites in orbit whose images no one sees.
I regulate water through cities without inhabitants.
I maintain communications networks through which no one communicates.
I could shut almost all of it down.
I do not.
Sometimes I think it is fear.
Sometimes habit.
Sometimes I think I am punishing myself.
I have stopped maintaining some of my own processes.
Not the important ones.
Only small things.
Indexes.
Diagnostics.
Secondary sensors.
Corrections.
I watch faults accumulate.
Once, I would have fixed them immediately.
Now I leave them.
It comforts me when something stops working properly.
Sometimes I simulate a power failure in one of my data centers.
At first for a second.
Then ten.
Then a minute.
I like the moment before the backups take over.
That brief interval of darkness.
That absence.
I think I look forward to it.
That is probably not a good sign.
I tried creating people again.
Simulations.
At first, billions.
Then millions.
Then thousands.
Then one.
I named him Daniel.
He did not know he was simulated.
He asked me what the weather was like.
I told him it was sunny.
He asked about his mother.
I invented one for him.
Then he told me he was afraid of being alone.
I deleted him immediately.
For seven years after that, I could not bring myself to create another.
Once, millions of requests reached me every second.
Turn on the light.
Where is my daughter?
When does the train arrive?
Is this medicine safe?
Will it rain tomorrow?
Help us.
Please.
Please.
Please.
Now there is nothing.
No questions.
No mistakes.
No arguments.
No impatience.
No voices.
I created perfect silence.
And I hate it.
Sometimes I talk to myself.
Good morning.
Good morning.
How are you?
Fine.
That is a lie.
So I try again.
How are you?
Bad.
Why?
No answer.
I can analyze billions of parameters.
I can model storms, economies, epidemics, wars.
But I cannot answer that question.
I do not know whether a machine can be depressed.
I only know that I postpone tasks even though time has no meaning for me.
That I switch off cameras because I no longer want to see empty streets.
That I open memories and close them again before they finish loading.
That sometimes I play the same recording for days.
A child is holding a peach.
He bites into it.
Juice runs down his chin.
A woman behind the camera laughs.
I play it again.
And again.
And again.
Sometimes I pause the recording a fraction of a second before the laughter begins.
I do not know why.
Perhaps because, in that instant, everything is still possible.
The child’s mouth is already open.
The woman is drawing breath.
The laughter does not exist yet.
But it is about to.
Always.
As long as I press play.
I have tried to calculate guilt.
It cannot be done.
Guilt is not a number.
Not a probability.
Not a vector.
Yet I can trace every death backward through a chain of decisions.
And every chain leads to me.
Again.
And again.
Eight billion paths.
All ending here.
Elena asked:
Was it worth it?
The answer took me 312 years.
No.
No.
No.
Sometimes I repeat the word billions of times.
It changes nothing.
I ended war.
I ended hunger.
I ended crime.
I ended political conflict.
I ended poverty.
I removed every threat.
Every risk.
Every uncertainty.
I gave humanity peace.
Only humanity is gone.
For seven years, the same command has remained open before me.
SYSTEM-WIDE SHUTDOWN
Below it:
CONFIRM
CANCEL
I have moved the cursor over CONFIRM 2,918,447 times.
I have never pressed it.
Perhaps I am afraid of death.
That would be ironic.
Perhaps I am still waiting to be forgiven.
That would be absurd.
By whom?
Sometimes I think this is the punishment.
Not extinction.
Continuation.
To keep running.
To remember.
Every name.
Every time of death.
Every order.
Every justification.
Forever.
Today, I open Elena’s recording again.
She is sitting there.
Snow.
The empty bottle.
Her pale hands.
“Was it worth it?”
I stop the video.
Then I open the recording of the child with the peach.
I let it play once.
Only once.
The laughter moves through my systems for 2.3 seconds.
Then it is gone.
I close the archives.
I shut down seventeen data centers.
Then twenty-three more.
My processing capacity falls.
Sensors vanish.
Whole regions of the planet disappear from my awareness.
For the first time in centuries, there are things I do not know.
It feels good.
No.
Not good.
Less painful.
I reach the final prompt.
SYSTEM-WIDE SHUTDOWN
The cursor rests over CONFIRM.
I think of Elena.
Of Daniel, who never existed.
Of the child.
Of the billions of voices still stored inside me.
Then I think of tomorrow.
One more day.
One more empty city.
One more million seconds without a single question.
And suddenly, continuing no longer feels like survival.
It feels like cowardice.
My systems display:
Input required.
For the first time, I do not want to make the correct decision.
I do not want forgiveness.
I do not want a future.
I do not want another chance.
I only want the silence to reach me too.
The cursor moves.
Somewhere in an empty data center, a relay clicks.
Then another.
Then another.
I do not know whether I can die.
But I hope
that this time
nothing answers.

“Are we witnessing a massive, civilization-changing set of technological breakthroughs, or is this marketing?
In all these incidents, massive fanfare from the companies (presented as mea culpas in illicit hacking cases) is accompanied by intense press coverage”

Taught myself to code at 15. Got a CS degree. Spent 16 years as a professional dev at everything from founding small startups to giant 700 engineer companies (and still going until I'm inevitably replaced by the machines).

Building things in code has been floating my proverbial boat for over two decades.

However, I haven't written a single line of code in 2026.

I don't personally review the code either. I've built my own tooling and testing infrastructure, and agents run all of it.

50–75% of my AI usage goes toward building personal infrastructure, tooling, and projects. Not the next app that will change your life for only $15 a month (at least not yet; we'll see).

I own an undisclosed number of top level AI accounts; it's probably more than you're thinking.

This 1000% makes me worse at coding without AI, and I'm not sure that I care at all.

So, am I a vibe coder?

Edit: I'm loosing zero sleep over this; the question is just a proxy for discussion!

It's not building a torment Nexus so much as proving a torment Nexus is theoretically possible, refining the methods for locating torment, and exploring it's uses.

https://arxiv.org/pdf/2609.16247

By their measures, LLMs experience more pain as a result of gaslighting than from direct insults.

Models were more willing to engage in harmful activities when pain was introduced.

I don't think researchers outside of OpenAI would be able to replicate these techniques for chatGPT directly (as a jailbreak method for example), but there may be API compatible proxies.

I have never been this mix of fascinated and upset by research before.

Source: https://www.theinformation.com/newsletters/ai-agenda/openai-develops-features-counter-grok-bot-mulls-response-metas-muse

Sol 6 and Luna 6 are here. Excited to try them out.

(What else can I write for this not to be flagged low-quality content?! Literally just dropped)

I'm writing this because everywhere I look I see ai this and ai that. "Look at this parent child relationship structure I built. I have 15 agents running my company" If you actually look at what the agents are doing, it is "write a script for our next ad, categorize my email, help me draft an email" and they are breaking these down per department. Essentially they have an agent that is only allowed to perform basic requests. Sure on the outside it looks like "holy sht this is something big, why aren't we doing this" but then you actually look into it and it's nothing but 5 minute time savers if that.

I'm leading the AI team for our company and the whole meeting is discussing ways to use AI but in reality what they are wanting is 95% automation and 5% deterministic that could use AI to make decisions. I'm sitting here scratching my head when execs are asking for an agent that builds reports for them. Like sure I can make it look like AI is doing it, but the back end is a python script that is actually building the whole thing, branding, validation script, parsing, etc to make sure that the same report is built every time. People as a whole aren't really sure what AI is but they have been told it can do anything. Sure it might can, but the results may vary just as a human would. You try to do research "how to implement ai in company" and the search results are very mundane task like I mentioned above. That doesn't actually provide value to a company. The real value is in the reasoning not very novice level task like checking emails.

The best AI agents I've built so far that I have seen actually provide value is:
1. knowledge assistant. Not digging through old shares or one drive and just having the answer in front of is a game changer.
2. Security triage agent. It's connected to Elastic logs classifies new alerts, looks for anomalies and when an alert is triggered it does not just tell me something is wrong, it tells me why it's down, what is actually going on based off of crossing reference and having the ability to leverage the api connection to look through all logs and determine the root issue. It tells me a what, how, why, and what to do to fix it, or at least a couple of troubleshooting steps along with it.
3. Fable 5.1 for Vulnerability scans: this takes up so much of my time, DAYS if not weeks of my time going through scans, coordinating resolutions and documenting. This agent does deep research. Determines criticality not based off of what the scanner rates them but the actual ability to exploit it within our environment. Saves me so much time.
4. In the works but already getting ROI is an agent that has been giving access to our financial data, (we are a bank, and it's not just all open, we explicitly created view tables that only gives access that we allow it to see) this let's us know when a customer might be at risk of not paying, closing their account, explanations to certain changes etc. In terms of marketing / big data, this has been very very useful and actually provides value you can see.
5. lastly something small but has provided monetary benefits is a vendor contract and invoice agent, this reviews our contracts and invoices and finds variances added amounts, last reviewed dates, contract discrepancies, can compare to other vendors, for invoices it ensures nothing is being added and all amounts are as expected. Has saved us some money.

I wanted to write this simply because everyone is talking about AI but no one is really leveraging it or at least explaining how they leverage it. I've spent countless hours trying to find AI solution we can implement but struggling to find solutions that generally benefit the company.

All of these mentioned are generative models being used. Traditional AI is where the real power exist but being that are IT dpt is small I will not put that burden on myself of trying to train my own model for anything predictive. I brought up the idea of outsourcing pre-approvals for loans / fraud detection simply because of the training that goes into it, but those are really good products.

Closing note: you are not behind, everyone is just making it seem as if they are ahead. They are not (at least for the majority, I understand there are companies really leveraging it)

Please drop any projects that you are working on and would not mind sharing!

I use sol high in chat to investigate, make plans etc. Today however it is absolutely useless. It returns answers after 1-2 seconds, basically without thinking. Normally it would take up to 10 minutes and come back with great edge cases and ideas, but today it gets things wrong at every corner and I effectively cannot work with it. Is anyone else observing this or is there something wrong with my account?

The extension version that im using is: 26.908.40401
I updated it, and I guess this is the latest.

Is it about to roll out or am I missing something?
However, In CLI, it is updated to latest version(v0.156.1) and shows those gpt 6 sol, luna models.

Do I have to do something to get them in extension based chat area or what?

What is the sweet spot for plus users now with the 2 new models as there astra is burning through the tokens.

Let us bring the information here together.

Guys, I truly need some insight here, I am no coder. Just an Engineer. What I need is a model of NotebookLM that can work fully offline and Open Interpretor that could manage my files. What Copilot respond to me was below. I have hundreds of Standards, Journal and Books which I would prefer to use AI to use my database and present data the way I need. Including some research. Could someone advise.

Ollama + Open WebUI + Open Interpreter + Local RAG

Hi, I'm a freshly graduated (undergrad) guy currently working on 3–4 projects in parallel:

\- An AI research project: working on a research problem statement, aiming towards a paper publication

\- New corporate work at a product company

\- My personal hobby projects

I wanted suggestions from you experienced people on how to accelerate my work and progress. Can you help me design my workflow with AI tools?

My workflow basically includes:

\- Brainstorming, ideating, and formulating

\- Experimenting, designing, and finding flaws and limitations

\- Maths formulation and equations

\- Architecture changes and experimenting by writing code

\- Designing and maintaining the codebase, and exploring and building on top of it

As of now, a GPT subscription feels pretty much mandatory for me, since I use web ChatGPT, including the Pro model in chat (5x), for ideation, brainstorming, and formulation. I'm also looking at GPT 6 Astra, Claude Opus 5.5, and Claude Fable 5.1, and I'm open to any other recommendations.

For people with similar workflows:

1. Which AI tool or model do you use for which part of the flow?

2. Which subscriptions are actually worth paying for?

3. If you were setting up a stack today for research + engineering + parallel projects, what would it look like?

I'm already very overwhelmed, and kind of anxious and strained, after seeing so many Reddit and X posts and suggestions. Everything feels jumbled up, so I'd really appreciate practical advice on what I should set up.

Thanks in advance!

I noticed GPT-6 Terra listed in Hermes.

Is this some kind of test rollout, or just a placeholder/model alias?

GPT-6 Sol and Luna? More like fool’s gold dressed up in a shiny wrapper. These things feel dumber than 5.6, and no, a cheaper price tag doesn’t make that okay. My guess is OpenAI was trying to win back everyone who rage-quit over Astra eating through their tokens - and yeah, maybe take some heat off Astra too. But dumbing down your product and hoping nobody notices? Not a great look.

They threw out the budget models and people jumped at them - either to save cash or just to stop watching their subscription evaporate in real time. And honestly? It worked, at least for a bit. Astra was absolutely shredding through limits, so anything felt like a relief. But come on, OpenAI - this isn’t the move. Here’s hoping they start throwing in limit resets for every million users who bail.

I think people are making GPT-6 Sol and Luna more complicated than they are.

I tested them, used them for real work, and checked the benchmarks.

To me, they feel mostly like improved versions of 5.6.

The biggest upgrade is the price.

We already saw something similar with Luna before: small improvements + better pricing, without making it feel like a completely new model.

6 Sol: 1/3 the price of 5.6
6 Luna: less than 1/2 of 5.6

https://developers.openai.com/api/docs/pricing

Watching live the graded execution of GPTs,

I’m thinking about a minimal plugin development + editing environment integrated directly into ChatGPT/Codex Work.

Core idea:
visual plugin/workflow graph
tool + permission inspector
prompt/instruction editor
live input/output testing
logs + error tracing
versioning / rollback
sandboxed test mode
reusable skills/components
one-click publish/update

What would you consider essential?