Claude vs ChatGPT: Do They Need Different Prompts?

Is Claude more literal than ChatGPT?

Not in the way the advice claims. The split was real: earlier GPT models filled gaps in a prompt generously, so a vague request often came back closer to what you meant than what you wrote. OpenAI retrained that away, starting with GPT-4.1, and said so in its GPT-4.1 prompting guide:

Anthropic describes its models the same way. Its prompting guide for Claude Opus 4.8 is the most explicit statement of what literal means in practice:

OpenAI says the same of its GPT-5-class models: they follow prompt contracts closely, so conflicting rules can cause more instability than missing detail. Its newest model, GPT-6 Astra, moves partway back — OpenAI describes it as filling routine gaps from context and asking a focused question when a gap could change the outcome. Neither family will reliably guess at a scope or a number you left out, so the practical advice is the same on both. An instruction applies to exactly what it names. "Rewrite the first paragraph in plain English" rewrites one paragraph, and "keep it short" means whatever the model decides short is. If you want an instruction applied everywhere, say everywhere; if you want a length, give a number.

• Scope: "Apply this to every section, not just the first one" instead of trusting the model to generalize.
• Quantity: "three options" or "under 120 words" instead of "a few" or "short".
• Ambition: if you want more than the literal request, ask for it. Anthropic's guide puts it plainly — "above and beyond" behaviour has to be requested, not inferred.

What actually differs between Claude and ChatGPT prompts?

Five things, and none of them is the wording of the task itself. They are defaults and plumbing:

The default-shape row is the one most people notice. Two models given the same unformatted request return differently shaped answers, which reads as "they need different prompts". It is really "the prompt left the shape unspecified, so each model used its default". State the shape and the difference mostly goes away — Output Format Mastery covers how.

Do XML tags work on ChatGPT?

Yes. XML tags are often presented as a Claude technique, but OpenAI's own prompt engineering guide recommends combining Markdown and XML: Markdown headings to mark sections, XML tags to show where a piece of content begins and ends. In its GPT-4.1 long-context testing, XML-wrapped documents performed well and JSON-wrapped ones performed particularly poorly.

Anthropic recommends XML tags for the same reason: a prompt that mixes instructions, context, examples and variable input is easier to parse when each part sits in its own named tag. So XML is the portable choice. A prompt sectioned with tags like <instructions>, <context> and <input> reads the same way to both families.

How do system prompts differ?

In the APIs, the difference is mostly naming. Claude takes system-level instructions in a separate system parameter, and Anthropic recommends putting the role there — even a single sentence makes a difference. OpenAI takes them in an instructions parameter or a developer message. Its Model Spec ranks instructions in a chain of command: OpenAI's root rules first, then system-level rules and system messages, then the developer, then the user.

The bigger difference is between the chat apps and the APIs. Anthropic publishes the system prompt it runs on claude.ai and its mobile apps, notes that it shapes behaviour such as Markdown code formatting, and states that it does not apply to the API. OpenAI's Model Spec describes the same layer from its side: system-level rules, sent through system messages, that can vary with the surface the model is served on. A prompt tuned in the ChatGPT or Claude app is therefore tuned against a layer that is absent once you ship it through the API — test it where it will run.

What does not differ?

Almost everything that decides whether a prompt works. Read the two vendors' guides side by side and the core advice is largely the same:

• Be specific about the output: its format, its length and its constraints.
• Show examples of the input and the output you want.
• Leave out all-caps and bribes. Anthropic says its Opus 4.5 and 4.6 models over-trigger tools on "CRITICAL: You MUST" and recommends plain phrasing; OpenAI says all-caps is generally unnecessary and can make GPT-4.1 follow an instruction too strictly.
• Put long reference material in clearly delimited blocks, separate from the instructions.

These are the decisions that Prompt Engineering Frameworks organise into slots. A prompt that gets them right on one model rarely fails on the other for reasons of wording.

How do you write one prompt that works on both?

Write for the stricter reading and specify the defaults. If a literal model gets what you need from the prompt, so will the other one. Concretely:

1. Section the prompt with XML tags: role, task, context, input, output format.
2. Give every scope and quantity explicitly: which items, how many, how long.
3. Specify the output shape, including whether you want Markdown at all.
4. Use plain, firm instructions instead of capitals and "MUST".
5. Keep system-level rules in the system slot, and per-request input in the user message.
6. Leave out tricks aimed at one model, such as a phrase that is said to work only on Claude.

<role>
You are a support editor for a B2B software company.
</role>

<task>
Rewrite every reply in <input> so a non-technical customer can follow it.
Apply the rules to each reply, not only the first.
</task>

<rules>
- Keep each reply under 120 words.
- Keep every product name and number exactly as written.
- If a reply promises a date, keep the date; do not add new promises.
</rules>

<output_format>
Plain text, no Markdown. One rewritten reply per block, in the original order,
each preceded by its number on its own line.
</output_format>

<input>
[paste replies]
</input>

Then run it on both and compare the outputs, not the prompts. One run per model proves little, because each model varies between runs on the same input. How to Evaluate a Prompt covers how many runs settle it.

Where does VantagePrompt fit?

VantagePrompt writes one prompt, not a Claude version and a ChatGPT version. For text use cases the optimized prompt comes back as XML with named sections — role, task, requirements, output format — which is the portable structure described above. The model you choose, or the one auto-routing picks, writes the optimized prompt; it is not a target setting, so the result is meant to be pasted into whichever assistant you use. Model Selection & Smart Routing covers choosing that model.