Claude Memory vs ChatGPT Memory: What Carries Over

Claude memory and ChatGPT memory both carry useful context into later conversations, but neither makes the two apps share one continuing record. Compare the facts each saves, the project sources a new chat can access and the controls available in your own account.

For work you keep doing in both assistants, fenbs gives them the same explicit project record to consult. Their native memories can remain useful for personal recall while approved decisions and progress stay in a place people can review.

Which option fits the job you need done?

This is a comparison of continuity, not a ranking of writing quality or model intelligence. A strong answer in one long chat tells you little about what a separate chat will receive. The general Claude vs ChatGPT comparison covers the broader product choice.

Decide what success means before testing. For a solo user it may be fewer reminders about response style. For a project it may be recognizing the latest approval. For a team it may be showing every assistant the same current decision. Those are related needs, but they require different evidence.

What do the assistants save about you?

Anthropic’s Claude memory guide↗ (opens in a new tab) describes individual topics saved during conversations. You can ask Claude to remember a fact and inspect, edit or delete the resulting topic under Settings, then Memory. Check your current account controls before relying on instructions from an older screenshot.

OpenAI’s Memories documentation↗ (opens in a new tab) describes carrying useful context from earlier work into future chats and places ChatGPT memory controls under Settings, then Personalization. It distinguishes ChatGPT memory from local Codex memory, so do not use a coding-tool setting as evidence about the chat app.

For either assistant, test a preference you can recognize without ambiguity. “Use a short paragraph before a list” is easier to assess than “write like me.” Inspect the available memory record and then try a new chat with a small writing task that depends on that preference.

A preference is not an approval. “I usually like short drafts” does not mean a specific client accepted the current draft. When you need to know who chose something, why they chose it and what remains open, write that as a decision rather than hoping a personal memory captures the distinction.

How do project boundaries change the comparison?

Claude’s current memory guide says a project uses its own memory alongside account memory, without pulling memory from other projects. Project memories can also be referenced outside projects when memory is enabled. To keep a project’s memory fully separate, it points to “Use account memory” in the project’s Memory controls.

That means “it is in a project” is not a sufficient description of the boundary. Check the actual setting, then use a harmless sample if you need to verify where a remembered detail is available. Never test separation with confidential material that should not cross the boundary.

OpenAI’s Projects and chats guide↗ (opens in a new tab) says a ChatGPT project gives its chats shared uploaded files, project instructions and connected sources. Start the new chat from the intended project to use that context. Check the project and workspace controls in your account instead of assuming every project is a sealed memory store.

For both products, a reference file and an inferred memory do different jobs. A file can preserve the exact approved wording. A remembered preference can make future requests less repetitive. An organized project should give you a clear answer to “Which source should win if these disagree?”

Keep the master version of an approved requirement easy to identify. If a draft says one thing and a remembered preference suggests another, ask the assistant to surface the conflict. The decision should come from the person responsible for the work, not from whichever detail the model happened to use.

Which controls matter when a remembered fact is wrong?

Review the source before changing the answer. Is the unwanted detail a saved memory, a project instruction, an uploaded document or something you said earlier in the current chat? Correcting one source will not necessarily correct a separate copy.

In Claude, inspect the relevant topic and use the available edit or delete control. In ChatGPT, start with its Personalization memory controls and check the options your account exposes. Then create a fresh conversation and test the corrected detail without including it in the test question.

Do not treat deleting a chat as a universal removal command. OpenAI’s ChatGPT Work cloud security guide↗ (opens in a new tab) explicitly separates conversations, project files and saved memories, and notes that deleting a conversation does not necessarily delete an existing memory. Review each location involved in your workflow.

Treat correction as maintenance, not a contest about which assistant admits mistakes more gracefully. A useful control lets you find the current fact, replace it and verify the next session. Keep a short log of your test result if you are comparing the products for ongoing work.

Can you move memory from one app to the other?

Anthropic’s memory import and export guide↗ (opens in a new tab) provides a deliberate transfer flow. You export or ask the other service for the relevant information, review it, and import the text into Claude. Inspect the resulting entries after the transfer.

Treat that as a copy taken at a point in time. If you later change a preference or finish a task in one app, the copied text does not become a live shared record. Decide which source you will maintain before repeating the transfer or keeping several versions.

Our Claude import memory guide owns that setup in detail. For a broader move, use moving from ChatGPT to Claude; for ChatGPT’s native behavior, see how ChatGPT memory works. These are separate jobs from comparing the two recall experiences.

Suppose you use ChatGPT for research and Claude for drafting a handbook. Moving a general writing preference can help the draft sound familiar. It does not tell the drafting session which research findings a person approved, whether a section is finished or whether publication is waiting on review.

That changing work state needs a record you update as the project moves. fenbs keeps it available through the supported assistant connections, without claiming to synchronize the vendors’ private memory stores.

How should you test both before choosing?

1. Choose a harmless sample project and three facts: a response preference, a project requirement and an unfinished next step.
2. Save each fact in the appropriate native place and record where you put it. Keep the wording the same in both apps.
3. Start a new chat in the intended project and ask what it needs to know before continuing. Do not reveal the answers in the question.
4. Change one requirement, inspect the saved source and test again in another fresh conversation.
5. Score whether the answer is accurate, whether you can identify its source and how easily you can correct a mistake.

Choose Claude’s native setup if its topic controls and project workflow fit the work you keep inside Claude. Choose ChatGPT’s native setup if its available memory controls and project sources fit the work you keep there. Your own task and correction test are more useful than a blanket claim that one assistant remembers better.

If you keep both, add one more test: can a new session in either app name the same approved decision and unfinished task? That is where a shared work record earns its place. You should be able to inspect the answer against the record rather than judging it by how convincing the reply sounds.

How to do this with fenbs

1. Create a free account and add one active project on your kanban board.
2. Add the current tasks and AI context notes. Record approved choices in Decisions and rules with the person who made them, leaving unanswered questions open.
3. Connect Claude and ChatGPT using their integration guides. Each supported connection uses MCP, a standard way for an AI app to use an outside tool, with browser sign-in.
4. Ask each assistant to read the project context before working. Before it stops, ask it to save task progress and Where we left off, including the next step and anything waiting on a person.
5. Open a fresh conversation in the other app and verify that it can continue from the same record. Correct stale project facts before expanding the setup.

fenbs is not the right fit if your whole need is a personal preference inside one chat app. It keeps the work rather than a biography, and it does not import Claude or ChatGPT chats, train a model or turn native recall into unlimited memory.

Keep personal recall where it helps and give shared work an inspectable home. The AI memory topic hub covers more specific memory questions when your comparison reveals a particular setup or troubleshooting need.