How can a company just remove 8 different AI models without telling their paying customers first?
I think most people here will agree that each model had its own purpose. That was the whole point of having different models with different strengths. You could set up different AI agents for different jobs.
For me, I used GPT-4o for creative work and brainstorming. The o1 model was great for logical problems. O1-Pro helped with detailed research. GPT-4 Turbo was my go-to for writing tasks. I bet many of you had similar setups.
Anyone else notice how different models had different content filtering? As someone who codes, it was really helpful to have multiple models to double-check when one gave weird results or got blocked by filters.
Like when GPT-4o gave me something that seemed off, I could ask o1 to verify it or help debug the issue. Pretty sure everyone here knows what I mean.
Now we’re supposed to just trust one single model without being able to check its work against other models on the same platform. No way to verify if it’s making stuff up or hiding information.
We’re expected to just trust ChatGPT-5 as our only source of AI help
If you can’t see what’s really happening here with all the corporate messaging, I’m worried. OpenAI is clearly trying to make users dependent on one model that they call the ‘most advanced’ while removing the models that actually showed real creativity and problem-solving abilities.
This feels like they’re trying to control how we think and access information.
The trust issue you mentioned hits hard. When they force everyone onto one model, they’re basically saying “we know better than you what you need.” That’s garbage.
I hit this exact problem managing different AI workflows for my team. We had specific models handling different parts of our development pipeline. When providers started pulling these stunts, I knew we needed bulletproof automation that could adapt instantly.
Now I route everything through automated workflows that switch between any AI provider or model without missing a beat. One day GPT-4o handles creative tasks, next day it’s Claude if OpenAI screws up their API. The system doesn’t care - it just works.
That verification problem you mentioned? I solve it by automatically sending the same prompt to different models and comparing results. When one model gives weird output, the others catch it. No manual checking.
Best part is when providers pull surprise moves like this, I just update a few connections and everything keeps running. No scrambling, no rebuilding workflows.
You’re spot on about the dependency trap. They want you so stuck that leaving feels impossible. Automation breaks that cycle. You become provider agnostic.
This whole situation reminds me of Amazon suddenly killing half their EC2 instance types a few years back. We had production systems running on instances they decided weren’t profitable anymore.
Same pattern every time: promise flexibility to hook you, then squeeze options until you’re stuck with whatever makes them the most money.
What kills me about OpenAI is how they made multiple models this huge selling point. “Look, we give you specialized tools for every job!” Then boom - consolidation because variety costs money.
I’ve seen this too many times. Real solution isn’t finding another single provider to depend on. Build workflows that work with anything.
After my last company got burned by a similar move, I started building everything model-agnostic. Same prompt hits OpenAI, Anthropic, Google, whatever - through the same interface. Provider changes a model? Fine. Jacks up prices? Switch in five minutes.
That cross-checking thing you mentioned is crucial. I never trust one model’s output on important stuff. Always validate against at least two different sources, preferably from different companies.
The dependency game only works if you let yourself get trapped. Build flexibility into your setup now before the next provider pulls this exact move.
Been there. They killed my fine-tuned models after I’d spent weeks training them. All that time and money - gone, with zero compensation or way to migrate. Classic tech company move: start open and collaborative, then slowly lock you in. Hook you with options and flexibility, then strip everything away until you’re stuck with whatever they want to give you. The business shift is obvious - running multiple models costs too much, so they’re cramming everything into one ‘superior’ version. Except no single model nails every use case, no matter what their marketing says. Now I keep detailed notes on which providers work best for what. When OpenAI pulls their next surprise move, I can jump ship fast without losing productivity. Bottom line: never trust any AI service to stay consistent. They’re chasing profit margins, not caring about your workflow.
This is exactly why I ditched subscription AI services for anything critical. OpenAI treats paying customers like we’re beta testers - they change everything whenever they want. What pisses me off most? Zero communication. A simple email like “hey, we’re consolidating models in 30 days” would’ve let people adjust their workflows. Instead they flip a switch and expect instant adaptation. I’ve switched to local models for important stuff. Yeah, they’re weaker than GPT-4o or o1, but I control updates. Ollama lets you run solid models locally without some exec randomly nuking your workflow. That verification issue you mentioned is massive. Using multiple models to cross-check was smart. Now when ChatGPT-5 hallucinates or blocks legit requests, you’re screwed - no backup options on the platform. OpenAI obviously wants cheaper infrastructure while selling the “one model rules all” story. Reality check: specialized models crush generalist ones at specific tasks, marketing BS aside.
Been through this exact frustration with OpenAI model changes. The constant switching and removing models breaks workflows that took months to perfect.
Here’s what I learned: don’t build everything around one AI provider. It’s risky. You need flexibility to switch between services without rebuilding from scratch.
I fixed this with automated workflows that route tasks to different providers based on what works best. When one provider kills models or jacks up pricing, I redirect to another service without touching my core setup.
I send creative stuff to Claude, logic problems to different GPT versions, and coding questions to specialized models. If one service craps out, the system switches to backups automatically.
The trick is using one automation platform that connects to multiple AI services. You’re never stuck with just ChatGPT-5 or any single model. You can compare results and always have alternatives ready.
This saved me when other AI services pulled similar stunts. Instead of canceling everything and starting over, I updated a few connections and kept working.
the worst part? they marketed multiple models as this big feature, then ripped it away like we shouldn’t care. remember when they bragged about giving us choice? now it’s just “trust our shiny new model” while they kill everything that actually worked for specific tasks.
Same thing happened to me last year when they killed off GPT-3.5-turbo-instruct without warning. Had three different automation workflows that just broke overnight.
The real problem is treating AI providers like reliable infrastructure. They’re not. These companies change models, pricing, and features whenever they want because we’re basically beta testers paying for the privilege.
I learned to spread my AI tasks across multiple providers from day one. When OpenAI removes models, I still have Claude for analysis, Perplexity for research, and other services for specific tasks. Never put all your eggs in one AI basket.
What really bugs me is how they frame these changes as “improvements” when they’re clearly cost-cutting moves. Maintaining 8 different models is expensive, so they consolidate everything into one model and call it progress.
My advice: start building redundancy now. Pick 2-3 different AI services and learn how they work. When the next provider pulls this stunt (and they will), you won’t be scrambling to find alternatives.
The dependency game is real. Once you’re locked into their ecosystem, they know you won’t leave easily. Breaking that cycle takes some upfront work but saves massive headaches later.