Claude vs GPT-4o: How Prompting Differences Actually Affect Results

I've been using both Claude and GPT-4o for production work for a while now, and one thing I've learned: a prompt that works great on one model often underperforms on the other. Not because one model is better — because they're different, and the differences are consistent and learnable.

Here are the most practically useful differences I've found.

The Headline Differences

Before getting into specifics: both models are capable of most tasks, and for simple prompts the differences are often minor. The gap shows up when you're doing something complex, long, or nuanced.

Claude tends to be better at:

• Following nuanced, multi-part instructions precisely
• Long document analysis (especially >20k tokens)
• Tasks where you want explicit structure and detailed formatting
• Nuanced reasoning that requires integrating multiple considerations

GPT-4o tends to be better at:

• Coding tasks with broad library coverage
• Vision and image understanding
• Creative tasks with more open-ended output
• Tasks where JSON-structured output is critical

Structural Formatting: XML vs. Markdown

This is the biggest practical difference for prompt engineering.

Claude: XML is your friend

Claude was trained with XML tags as a first-class structuring tool. Use them to separate sections:

<context>
You are helping a user write code for a web scraping project.
</context>

<instructions>
Review the code below. Identify:
1. Any bugs that would cause it to fail
2. Performance issues for large-scale use
3. Missing error handling
</instructions>

<code>
[paste code here]
</code>

<output_format>
Use headers for each category. Be specific about line numbers.
</output_format>

Claude reads this as clearly structured information — the tags help it parse what's context, what's instructions, what's data, what's format specification.

GPT-4o: Markdown works better

GPT-4o responds more naturally to markdown-style formatting:

# Context
You are helping a user write code for a web scraping project.

## Task
Review the code below and identify:
1. Any bugs that would cause it to fail
2. Performance issues for large-scale use
3. Missing error handling

## Code
[paste code here]

## Format
Use headers for each category. Be specific about line numbers.

Neither approach is wrong for either model, but these are the native "dialects" each was trained on most heavily.

Instruction Following: Precision vs. Flexibility

Claude follows instructions very precisely — sometimes almost literally. If you say "respond in bullet points," it will respond in bullet points even when a paragraph would read better. If you say "do not mention X," it will almost never mention X.

This is good when you want exact compliance. It can be frustrating when you've written slightly imprecise instructions and Claude interprets them literally in a way you didn't intend.

GPT-4o uses more judgment about when instructions should be followed rigidly vs. when to deviate for better results. It's more likely to use prose when bullets would be awkward, even if you asked for bullets.

Practical implication: With Claude, write instructions you actually want followed precisely. With GPT-4o, you can be slightly looser and the model will exercise judgment — but that also means less predictable formatting behavior.

Long Context: Claude Has the Edge

For tasks involving very long documents — legal contracts, research papers, full codebases, lengthy chat histories — Claude generally performs better.

A few reasons:

• Claude's 200K token context window is among the largest available
• Claude tends to pay attention to information throughout the context more consistently (less "lost in the middle" degradation)
• Claude's instruction following helps it stay on task despite distracting long-context content

For most users this doesn't matter — the documents you're working with fit in either model's context. But if you're doing long-document work, it's worth testing both and checking whether Claude retains more of the full document's details.

Tone and Personality

Claude tends to be slightly more verbose by default. It hedges, adds caveats, and provides context. This is often good — but sometimes you just want the direct answer.

For Claude, be explicit about brevity:

Answer in one sentence. Do not add caveats.

GPT-4o is slightly more direct by default and tends toward shorter responses. For GPT-4o, be explicit when you want depth:

Provide a thorough answer with examples and explanations.

Both models follow these instructions well once you give them.

Roleplay and Persona

GPT-4o tends to be slightly more willing to stay deeply in character for roleplay and persona-based tasks. Claude's safety training is more conservative here — it will step out of character more readily if it judges the character's behavior to be potentially harmful.

For legitimate roleplay and persona-based applications (writing fiction, practice conversations, customer service personas), both work well. For more edgy or boundary-testing scenarios, GPT-4o gives you slightly more latitude.

JSON and Structured Output

Both models support structured output, but GPT-4o's JSON mode (where you specify a JSON schema and the model is guaranteed to return valid JSON) is particularly reliable.

GPT-4o with JSON mode:

response = client.chat.completions.create(
    model="gpt-4o",
    response_format={"type": "json_schema", "json_schema": your_schema},
    messages=[...]
)
# Guaranteed valid JSON matching your schema

Claude for structured output:

<output_format>
Return a JSON object with exactly these keys:
- "summary": string (1-2 sentences)
- "sentiment": "positive" | "negative" | "neutral"
- "confidence": number between 0 and 1

Return only the JSON object, no other text.
</output_format>

Claude follows structured output instructions reliably but doesn't have a native schema validation mode the way GPT-4o does. For applications where you absolutely need guaranteed valid JSON, GPT-4o's JSON mode is worth using.

Quick Reference

The honest answer is that for most tasks, the prompt quality matters more than the model choice. A great prompt on GPT-4o usually beats a mediocre prompt on Claude and vice versa.

But when you're optimizing for production: test both models with your actual prompts and evaluate on your actual use case. The differences above are tendencies, not guarantees — your specific task may behave differently.

Both models are impressive, and the competition between them means both are improving fast. The techniques on MasterPrompting.net — chain-of-thought, few-shot examples, XML structure — apply to both. The differences here are in calibration, not fundamentals.