Comparison

Coldcache vs ChatGPT

Coldcache can run on GPT-5. This is not a comparison of models — it is a comparison of what happens around one when a team depends on the answer.

Three differences that matter

Memory off by default vs. on by default

CHATGPT

Cross-chat memory is on by default and invisible at the point of use. Two runs of the same prompt are not comparable, because the second one has read the first.

COLDCACHE

Fresh context window per run, zero cross-session state. If a version wants memory it declares it, and the chat header says memory: scoped so nobody is surprised.

You cannot A/B two prompts when the model half-remembers the previous nine attempts at both.

Versioned GPTs vs. edited GPTs

CHATGPT

Editing a Custom GPT rewrites it. Everyone using it gets the new one immediately, including jobs that were validated against the old one.

COLDCACHE

Publishing v6 leaves every existing chat on its pinned version. New chats resolve the stable pointer, and stable only moves when a human moves it — with an audit entry.

An upgrade is a decision with a date and an author, not a side effect of saving.

Evidence vs. output

CHATGPT

You get an answer. Sources may be linked; what the model actually read is not retained.

COLDCACHE

Every source is snapshotted with a fetch timestamp and content hash, so an answer can be re-examined against the evidence it was based on rather than the page as it exists today.

“The page said this on 14 August” is checkable, not asserted.

When to use which

Use ChatGPT

You want an assistant that knows you. Memory is the feature, not the problem, and Custom GPTs are a genuinely good way to hand a workflow to a colleague. When you are the only person who has to trust the output, accumulated context makes it better, and there is nothing to reproduce.

Use Coldcache

A second person depends on the answer, or the same answer is needed twice. Note what is not being compared: point an agent at GPT-5 and OpenAI answers the question — the model is the same. What you gain is a named, frozen configuration, per-person control over who may change it, and a run you can reproduce a year later. Ambient memory is precisely what makes that impossible.

Side by side

	ChatGPT	Coldcache
What it is	An assistant you talk to	An instrument your team runs
Cross-chat memory	On by default	Off by default, per-version opt-in
Editing a saved agent	Rewrites it for everyone	Publishes the next version
Run against an old configuration	Not possible	Any published version, any time
Diff two configurations	No	Config diff and output diff, side by side
Regression fixtures	No	Accepted output hashes, checked on every new version
Audit log of changes	No	Append-only: publishes, stable moves, deprecations
Model choice	Their models	GPT-5, Claude or Gemini — you name the exact model id
Who can change an agent	Whoever can edit the GPT	Per person, per agent: read, write or approve

Same models, same web, same ceiling on how good an answer can get. The entire difference is whether, a year from now, you can name what produced it, prove it has not changed, and run it again. That is not a smaller claim than intelligence. It is the one that survives an audit.

Compare with Perplexity

Pin an agent. Run it in a year. Get the same thing back.

Claude, ChatGPT and Gemini included — no provider account to open. Build one agent, publish v1, run it, then change the prompt and diff the two.

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