The best model keeps changing. Your memory shouldn't start over.
OpenAI shipped GPT-5 and the switchers came. Anthropic shipped the Claude 5 family and the switchers went the other way. Next quarter someone else will take the podium, and the cycle runs again.
This is the actual shape of the AI market now: the best model for your work changes every few months. The interesting question isn't which assistant wins. It's why switching feels so expensive when the subscriptions cost about the same.
Quick answer
Chat history, built-in memory, Projects, and custom GPTs are all locked inside the assistant you built them in. None of it transfers when you switch. A knowledge base connected over MCP is the exception: the same notes work in ChatGPT, Claude, and any other MCP client, so a new model launch becomes a five-minute connection instead of starting from zero.
The real switching cost is your context
The money is not the lock-in. Twenty dollars a month is nothing against the value gap between model generations.
The lock-in is everything the assistant knows about you. Months of conversations. The memory entries it collected. The Projects you set up, the files you attached, the instructions you tuned. Switch assistants and all of it stays behind. The new model is smarter and knows nothing. So people stay with a weaker model because it remembers them. Say that out loud and it sounds like a strange kind of loyalty.
What actually moves when you switch
| You built | When you switch assistants |
|---|---|
| Chat history | Stays behind |
| Built-in memory | Stays behind, manual export at best |
| Projects and their files | Rebuilt by hand |
| Custom GPTs | Stay in ChatGPT |
| A knowledge base over MCP | Connects to the new assistant in minutes |
The pattern in that table is not an accident. Every assistant vendor benefits when your context lives inside their app. ChatGPT's memory is genuinely useful and genuinely unportable. Claude Projects are genuinely useful and genuinely unportable. That's not malice. It's just what happens when memory is a feature of the assistant instead of a thing you own.
Memory as a feature vs. memory you own
Flip the architecture and the problem disappears. Keep your knowledge in a knowledge base that speaks MCP, and the assistant becomes the replaceable part:
- Your notes live in one place, as Markdown, in folders you organized.
- ChatGPT reads and writes them. Claude reads and writes the same notes. So do Cursor, Copilot, and whatever ships next.
- Write a note from one assistant, ask another about it an hour later. One brain, every assistant.
When the next model family lands, you connect it and ask it to read your key notes. It starts from what you know, not from zero. Trying the new best model stops being a migration and becomes what it should have been all along: trying a model.
This is what Hjarni is for
Hjarni is a knowledge base with a built-in MCP server. Not a plugin, not a bridge: the app was built around it. It's listed in the ChatGPT Apps Directory and the Claude Connectors Directory, so both of today's big assistants connect in a few clicks, and MCP being an open standard covers the ones that don't exist yet.
And because the point of this whole argument is owning your knowledge, the exit door is part of the product: export everything as a ZIP of Markdown files, folder structure preserved, in an open format. Portability that stops at our door wouldn't be portability.
Give your AI a memory that survives the next launch. Connect ChatGPT or Claude in five minutes. Then follow the best model wherever it goes. Your knowledge comes with you.
Give your AI a memory. Free.
Connect Claude or ChatGPT to notes they can actually read and write.
Give your AI a memory. Free.
Common questions
FAQ
Do ChatGPT memories transfer to Claude?
Not automatically. ChatGPT's built-in memory lives inside ChatGPT, and Claude's memory lives inside Claude. You can move the contents by hand: review what ChatGPT has saved, export the facts worth keeping, and store them somewhere both assistants can read. A knowledge base connected over MCP is the durable version of that move.
Can I use the same knowledge base with ChatGPT and Claude?
Yes. MCP is an open standard, so one knowledge base works in ChatGPT, Claude, Cursor, Copilot, and any other MCP client. Write a note from one assistant and ask another about it an hour later. Hjarni ships a built-in MCP server and is listed in both the ChatGPT Apps Directory and the Claude Connectors Directory.
What happens to my Claude Projects if I switch to ChatGPT?
They stay in Claude. Project knowledge, files, and instructions don't export to another assistant, so switching means rebuilding by hand. Keeping the knowledge itself in an external knowledge base avoids the rebuild: the new assistant connects to the same notes the old one used.
How do I try a new AI model without losing my context?
Keep your context in a knowledge base the new model can search over MCP. Connect the new assistant, ask it to read your key notes, and it starts from what you know instead of from zero. Trying the new model becomes a five-minute connection instead of weeks of re-explaining.
Is it worth switching AI assistants when a new model comes out?
Often, yes: the capability gap between model generations is real. The switching cost is mostly your accumulated context, not the subscription price. Remove that cost by keeping knowledge in a store you own, and you can follow the best model freely instead of staying loyal to your chat history.