I wanted to do a simple experiment.

Instead of asking, “Which is better, ChatGPT or Claude?”, I gave both of them the same practical task:

Explain prompt caching for a beginner.

Then I compared the results.

I wasn't trying to test which AI was “smarter.” I wanted to see something much more useful in everyday work:

Which one helps me understand a technical topic, explain it clearly, and turn it into something I would actually want to publish?

And after going through the comparison, I found myself preferring the OpenAI/ChatGPT version.

Here's why.

The experiment

I started with Anthropic's official documentation about prompt caching.

The original documentation is technically detailed. It explains things like cache breakpoints, automatic and explicit caching, cache duration, pricing, and how cached prompt prefixes work. Anthropic says prompt caching allows previously processed prompt prefixes to be reused, reducing processing time and cost for repetitive prompts.

I then asked ChatGPT to explain the article for a beginner, in a short and friendly way.

After that, I asked Claude to explain the same topic and got a much more polished beginner-oriented explanation.

So now I had three things:

The original technical documentation → ChatGPT's explanation → Claude's explanation

That gave me something interesting to compare.

1. First impression: Claude was very good

I'll give Claude credit.

Its explanation was excellent at making the idea immediately understandable.

It started with a simple analogy:

Think of a barista who remembers your usual order.

That's a very good analogy.

Instead of thinking about tokens, cache prefixes, API requests and pricing, you can immediately understand the basic idea:

Don't make the system process the same information again and again if that information hasn't changed.

Claude also organized the explanation nicely:

• What it is
• Why it matters
• How it works
• What you get
• Practical notes
• Bottom line

For someone who knows nothing about prompt caching, this is a very comfortable way to learn.

So at this point, I thought:

Claude might win this one.

But then I looked more closely.

2. Where ChatGPT was better for me

The biggest difference wasn't the amount of information.

It was the way the information was framed.

ChatGPT gave me a simple mental model:

Stable information → changing information

For example:

Company policies
Product documentation
Instructions
Examples
--------------------
Customer's new question

The first part doesn't change much.

The last part changes constantly.

That immediately gave me a practical way to think about prompt caching.

Instead of simply understanding what caching is, I could start asking:

What part of my prompt is repetitive?

That is a much more useful question when you're actually building something.

3. The “Claude remembers” problem

There was one thing in Claude's explanation that I would change.

It described prompt caching as Claude essentially remembering information it has already seen.

That's a great beginner analogy.

But technically, I think it can create the wrong mental model.

Prompt caching isn't really human-like memory.

It's closer to:

The API temporarily reuses previously processed prompt content.

Anthropic's documentation describes it as reusing cached prompt prefixes, with automatic caching or explicit cache breakpoints.

That distinction may sound small.

For a beginner, it probably doesn't matter much.

But once you start working with APIs, it matters.

I prefer explanations that are simple without creating a mental model I'll later have to unlearn.

That was a point in ChatGPT's favor for me.

4. Then I looked at the practical side

This is where the difference became clearer.

Claude's explanation told me that caching is useful for:

• long system instructions
• large documents
• growing conversations
• repeated instructions
• reference material

That's good.

But ChatGPT's explanation made me think about why those things are good candidates.

The key is repetition.

If I'm sending the same 50,000-token document with 100 different questions, I don't want the system to repeatedly process that same 50,000-token block from scratch.

That's the real opportunity.

The question becomes:

How much of my input stays the same between requests?

The more that stays the same, the more interesting caching becomes.

Anthropic's documentation makes the same fundamental point: prompt caching is particularly useful for large context, many examples, repetitive tasks, and long multi-turn conversations.

5. Pricing: this is where the simplified explanations can become misleading

This was probably the biggest thing I noticed.

It's easy to say:

“Prompt caching makes things cheaper.”

That's true in the right situation, but it isn't the complete story.

There is a cost to creating the cache.

Anthropic's current documentation says 5-minute cache writes cost 1.25× the base input-token price, while cache reads are 0.1× the base input price. A 1-hour cache has a higher write multiplier.

So I wouldn't explain caching simply as:

“Caching = cheaper.”

I'd explain it as:

“Caching has an upfront cost, but repeated use of the cached content can be much cheaper.”

That's a much better practical explanation.

And that's another reason I preferred the ChatGPT approach: it encouraged me to think about the trade-off, rather than just the benefit.

6. What about the writing itself?

This is where my personal preference became obvious.

Claude's writing felt very polished.

Almost too polished.

It read like a carefully prepared educational article.

That's not necessarily bad.

In fact, if I wanted a clean technical explainer for a documentation website, I'd probably be very happy with it.

But when I asked myself:

“Which one sounds more like something I would actually write?”

I preferred ChatGPT.

The ChatGPT version felt more conversational.

It explained the idea, gave me an example, then moved on.

It didn't feel like it was trying quite as hard to sound like an article.

For me, that's important.

When I'm writing, I don't just want correct information.

I want something that sounds like a person thinking through a problem.

7. My step-by-step scoring

After comparing both versions, this is how I'd score them for this particular task:

So this wasn't a case of:

ChatGPT good, Claude bad.

Far from it.

Claude actually did a very good job.

But the winner for my writing workflow was ChatGPT.

8. What I learned from the experiment

The interesting part wasn't really prompt caching.

It was the comparison itself.

I realized that when I'm evaluating AI writing tools, I shouldn't just ask:

“Which answer is better?”

That's too vague.

I should ask:

Better for what?

If I'm looking for a highly structured beginner tutorial, Claude performed extremely well.

If I'm looking for technical documentation, the official source still wins.

But if I'm trying to understand something, think about it, and then turn my understanding into natural-sounding writing, I preferred ChatGPT.

That's a different test.

And for me, it's a more useful one.

My winner: ChatGPT

So, after this particular comparison, I prefer the OpenAI/ChatGPT approach for writing.

Not because Claude is bad.

Actually, Claude gave me a very strong answer.

I prefer ChatGPT because it helped me move through the process more naturally:

Understand → simplify → question the details → form an opinion → write.

That's closer to how I personally work.

And I think that's the real lesson.

There probably isn't one AI that's simply “the best.”

There is the AI that works better for the task you're doing and the way you think.

For this experiment, my choice was ChatGPT.

Claude gave me a very good explanation.

ChatGPT gave me an explanation I could think with.

And for writing, that difference matters to me.