ChatGPT vs Claude: Which AI Needs Different Prompts?

You've probably noticed that the same prompt can produce very different results in ChatGPT versus Claude. That's because these models have different strengths, training approaches, and response patterns.

Understanding these differences helps you write better prompts for each—or create prompts that work well across both.

Quick Comparison

Aspect	ChatGPT	Claude
Instruction Following	Good, sometimes creative	Excellent, more literal
Length Handling	Can be verbose	More concise by default
Code Generation	Strong, especially GPT-4	Very strong with Claude 3.5
Context Length	128K (GPT-4o)	200K (Claude 3.5)
Structured Output	May need examples	Follows format instructions well
Tone Consistency	Can drift	More consistent

Key Differences in Prompt Behavior

1. Instruction Precision

Claude tends to follow instructions more literally. If you ask for "5 items," you'll get exactly 5.

ChatGPT is more likely to interpret the spirit of your request. Ask for "5 items" and you might get 4 or 6 if it feels that's more appropriate.

Implication for prompts:

For Claude, your instructions are taken at face value. Be precise about what you want.

# Works well with Claude
List exactly 5 benefits, no more, no less.

For ChatGPT, you may need to emphasize constraints more explicitly:

# Better for ChatGPT
List 5 benefits. Important: provide exactly 5, not 4, not 6.

2. Verbosity Control

Claude tends to be more concise by default, sometimes too brief.

ChatGPT tends toward verbosity, sometimes over-explaining.

For Claude - Ask for more when needed:

Provide a comprehensive answer with examples and detailed explanations.

For ChatGPT - Constrain when needed:

Be concise. Keep the response under 200 words.

3. Format Adherence

Claude excels at following format specifications on the first try.

Return your response as JSON:
{
  "summary": "...",
  "key_points": ["...", "..."],
  "recommendation": "..."
}

Claude will typically return clean, valid JSON.

ChatGPT may need more explicit instructions or examples:

Return ONLY valid JSON, nothing else. No explanation before or after.
{
  "summary": "...",
  "key_points": ["...", "..."],
  "recommendation": "..."
}

4. Context Utilization

Claude has a larger context window (200K tokens) and handles long-form context well. It's excellent at:

• Analyzing long documents
• Maintaining consistency across long outputs
• Referencing earlier parts of conversations

ChatGPT (GPT-4o) has 128K tokens but sometimes struggles with very long contexts. For long-form work, you may need to:

• Summarize key points upfront
• Reference specific sections explicitly
• Break long tasks into chunks

5. Code Generation

Both are excellent at code, but with different characteristics:

Claude 3.5 Sonnet tends to:

• Write cleaner, more idiomatic code
• Include good comments
• Handle edge cases more consistently

ChatGPT (GPT-4) tends to:

• Be more creative with solutions
• Sometimes over-engineer
• Explain code extensively

For code prompts, Claude often needs less guidance:

# Works well for Claude
Write a Python function to validate email addresses. Handle edge cases.

ChatGPT may benefit from more structure:

# Better for ChatGPT
Write a Python function to validate email addresses.
Requirements:
- Simple and readable
- Handle special characters
- Return boolean
- Include 3 test cases in comments

6. Role-Playing and Personas

ChatGPT tends to embrace roles more theatrically, sometimes adding flourishes the persona might have.

Claude adopts roles but remains more grounded, maintaining the persona without overdoing it.

If you want a more "creative" interpretation of a role, ChatGPT may deliver more interesting results. For a consistent, professional persona, Claude is often more reliable.

Prompt Patterns That Work Across Both

Universal Best Practices

These patterns work well regardless of model:

1. Clear task statement first
2. Context and constraints
3. Format specification
4. Examples if complex

The Universal Prompt Template

[ROLE - if relevant]

I need you to [CLEAR TASK].

Context:
[RELEVANT BACKGROUND]

Requirements:
- [REQUIREMENT 1]
- [REQUIREMENT 2]

Format:
[HOW TO STRUCTURE THE OUTPUT]

[EXAMPLE IF HELPFUL]

This template works for both ChatGPT and Claude because it:

• Provides clear structure
• Separates concerns
• Specifies format explicitly
• Includes examples when needed

Model-Specific Optimizations

Optimizing for Claude

Claude responds well to:

✅ Direct, literal instructions

Provide exactly what I ask, no more.

✅ XML-style tags for structure

<instruction>Your task here</instruction>
<context>Background info</context>
<format>Output format</format>

✅ Asking for concise vs. comprehensive explicitly

Give me a comprehensive response with full details.

or

Keep it brief—just the essential points.

✅ Trusting format instructions without examples

Return as markdown with H2 headers for main sections.

Optimizing for ChatGPT

ChatGPT responds well to:

✅ Explicit constraints repeated

Important: Do not include [X]. This is critical.

✅ Clear formatting examples

Format your response like this:
## Section Name
- Point 1
- Point 2

✅ Step-by-step for complex tasks

Think through this step by step:
1. First, consider...
2. Then, evaluate...
3. Finally, recommend...

✅ Direct reminders about length

Keep this under 300 words.

When to Use Each Model

Use Claude When:

• Working with long documents - Better context handling
• Needing precise format compliance - JSON, markdown, specific structures
• Writing technical documentation - Clear, consistent output
• Requiring adherence to constraints - It follows rules literally
• Using system prompts in apps - More predictable behavior

Use ChatGPT When:

• Wanting creative interpretation - More likely to surprise you
• Doing exploratory brainstorming - Generates more varied ideas
• Needing Code Interpreter - Can run Python, analyze files
• Creating more conversational content - Natural dialogue flow
• Using plugins/GPTs - Broader ecosystem

Use Both When:

• Verifying important outputs - Cross-check between models
• Generating alternatives - Compare approaches
• Your prompts are well-structured - Both perform well with good prompts

Real-World Examples

Example 1: Code Review

Same prompt, different results:

Review this Python function for bugs and improvements:

def process_data(items):
    result = []
    for i in range(len(items)):
        if items[i] > 0:
            result.append(items[i] * 2)
    return result

Claude will typically:

• List issues concisely
• Suggest the pythonic fix
• Stop there

ChatGPT will typically:

• Explain each issue in detail
• Provide multiple alternative implementations
• Discuss performance implications

Optimized for Claude:

Review this function. Give detailed explanations and multiple fix options.

Optimized for ChatGPT:

Review this function briefly. Just bullet the issues and one recommended fix.

Example 2: Blog Writing

Same prompt:

Write a blog post introduction about remote work productivity.

Claude might give you 100 words, tight and focused.

ChatGPT might give you 300 words, more flowing and narrative.

To get length you want:

For Claude:

Write a 250-word blog introduction about remote work productivity. Be engaging and include a hook.

For ChatGPT:

Write a blog introduction about remote work productivity. Keep it under 150 words, no fluff.

Converting Prompts Between Models

If you have a prompt that works well in one model:

ChatGPT → Claude

1. Remove repeated emphasis on constraints (Claude follows them)
2. Cut example-based formatting if you've specified structure
3. Allow for more literal interpretation

Claude → ChatGPT

1. Add explicit length or format constraints
2. Include examples of output format
3. Emphasize critical requirements ("Important:", "Must:")
4. Consider adding step-by-step structure

Frequently Asked Questions

Are prompts interchangeable between models?

Yes, but results vary. A well-structured prompt works reasonably well on both. Model-specific optimizations improve results by 20-30%.

Which is better for beginners?

Claude is often easier for beginners because it follows instructions more literally. You get what you ask for without needing to anticipate model quirks.

Do I need separate prompt libraries?

Not necessarily. A good prompt library works for both. You may want to add notes on model-specific variations for complex prompts.

How do I handle API rate limits?

Keep prompts for both models. If one provider has issues, you can switch. This is also good for cost optimization.

Which is more accurate for facts?

Neither should be trusted for critical facts. Both can hallucinate. Always verify important information.

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Browse prompts optimized for all major AI models in our prompt directory. Each prompt works well across ChatGPT, Claude, Gemini, and more. If you're comparing AI coding tools, check out our Cursor vs Windsurf vs Claude Code comparison.