Switching AI models without starting over

New AI models are arriving every few weeks now. In a single stretch of 2026, OpenAI shipped GPT-5.6, Anthropic released Claude Sonnet 5, xAI launched Grok 4.5, and a wave of strong open models — GLM-5.2, DeepSeek V4, Qwen 3.6, Kimi K3 — landed alongside them. If you use AI for real work, the honest advice from every corner of the industry is the same: stop trying to crown one permanent winner. Test the new release on your own tasks, and use whichever model fits the job in front of you.

Good advice. But it hides a practical problem nobody mentions: every time you switch, you leave a conversation behind.

You've spent an afternoon with one model working something out — the context is built, the decisions are made, the thread knows what you're doing. Then a new model drops, you want to try it, and you're staring at a blank chat. Do you re-explain the whole thing from scratch? Copy-paste a wall of old messages and hope it sorts them out? Most people just don't bother switching, which means they never actually test the "best fit" everyone tells them to find.

That gap — between wanting to move and actually moving — is the whole reason this is annoying. Here's how to close it.

Why you can't just copy-paste the conversation

The obvious move is to copy your old chat and paste it into the new model. It rarely works well, and it's worth understanding why.

A conversation isn't just its content — it's a shape. Dozens of turns, corrections, tangents, ideas you raised and then dropped. When you paste all of that into a fresh model, you're not handing it your context — you're handing it your transcript, and asking it to reconstruct what mattered on its own. It might. It might also latch onto something you abandoned twenty messages ago, or miss the one decision that actually counts, because nothing in the raw dump tells it what to weigh.

The longer your original conversation, the worse this gets. The same length that made your first chat drift is now a wall the new model has to read through before it can help you.

The move that actually works: a clean handoff

Instead of the raw transcript, you want a handoff — your context rewritten as a short, structured starting point that any model can pick up cold. What you're working on, what's been decided, where things stand, what's next. You paste that as the first message in the new chat, and the new model starts already oriented, instead of digging through your history.

This is what Uncook does. You take a share link from your existing conversation — in ChatGPT, Claude, Gemini, whatever you were using — paste it in, and get a handoff you can drop into the new model. No account, no extension, nothing to install. It works from any model to any model, in the language of your conversation.

So when GPT-5.6 or Grok 4.5 or the next thing lands, trying it doesn't mean starting over. It means pasting one clean message and picking up where you left off.

Three moves the model wave makes useful

Switching is the obvious one, but the same "many models" reality creates two more:

Comparing the same task across models. A lot of people now run the same prompt through two or three models to see which answer they prefer. When you're done, you've got the good parts scattered across three chats. Merge pulls them into one — and it works across different models and even different subjects, so comparing GPT-5.6 against Claude against Grok on the same problem ends with one combined result instead of three tabs.

Sharing what you found. You tested the new model, you formed an opinion, and now someone on your team asks "so, is it worth switching?" They don't want your whole transcript. A brief turns the conversation into a short, readable summary a person can actually read — the verdict, not the raw thread.

The honest part

Two things worth saying plainly, because this space moves fast and overpromising would be easy.

First, we're not ranking the models. Which one is "best" changes every few weeks, depends entirely on your task, and anyone claiming a permanent winner is selling something. Uncook doesn't care which model you use or move to — it just makes the moving painless. The point isn't which model; it's that you can actually try the new one without paying a tax in re-explaining.

Second, a handoff carries what's in the conversation, not what lives outside it. Files you uploaded, images, a model's private memory of you, custom instructions — those you'll re-establish in the new tool. What the handoff carries is the hard part: the state of your work and the decisions. Treat a switch as a fresh, well-briefed start, not a perfect clone of the old chat. And the new model won't respond identically to the same context — that difference is usually the whole reason you're switching.

The takeaway

The industry's advice — don't marry one model, use the best fit — only works if switching is cheap. Right now, for most people, it isn't: the re-explaining tax is high enough that they stay put. A clean handoff removes that tax. Test the new model. Keep the good conversation. Move on when something better shows up. That's the workflow the model wave actually calls for.

Related: How to move a ChatGPT conversation over to Claude · Merge several ChatGPT conversations into one