One Prompt Does Not Fit All
A prompt that produces excellent results with Claude might return mediocre output from ChatGPT, and vice versa. Each model family has been trained differently, and these differences affect how they interpret and respond to your instructions.
Understanding these differences is not about memorizing quirks. It is about adapting your communication style to get the best out of each tool.
Claude: Structure and Directness
Claude (from Anthropic) tends to respond best to prompts that are:
Direct and explicit
Claude follows instructions literally. If you want something specific, state it clearly rather than hinting. Vague instructions lead to vague output.
Structured with XML-style tags
Claude handles sectioned prompts exceptionally well. Using tags like <context>, <task>, and <rules> helps the model parse long, complex prompts without losing track of each section's purpose.
System-prompt aware
Claude makes strong use of system prompts for persistent instructions. If you have rules that should apply to every response (tone, format, constraints), putting them in the system prompt keeps them consistently applied.
Candid about uncertainty
When Claude is unsure, it tends to say so rather than fabricate. You can reinforce this by including instructions like "If you are not confident in your answer, say so."
What works well with Claude:
- Long, detailed prompts with multiple sections
- Explicit constraint lists
- XML-style formatting for clarity
- Direct instruction without excessive politeness or framing
ChatGPT / GPT-4: Conversational and Role-Oriented
OpenAI's GPT-4 and ChatGPT tend to respond best to prompts that are:
Conversational in tone
GPT-4 was heavily trained on conversational data. It responds naturally to prompts written in a conversational style, including follow-up questions and back-and-forth refinement.
Role-play driven
The "act as" pattern works particularly well with ChatGPT. Assigning a detailed persona can significantly shift the quality and style of output.
System-message dependent
The system message in OpenAI's API is powerful for setting persistent behavior. Complex instruction sets in the system message are followed more consistently than the same instructions in a user message.
Responsive to temperature cues
Even in the prompt itself, you can signal whether you want creative or conservative output. "Be creative and explore unusual angles" versus "Stick strictly to established facts" meaningfully changes GPT-4's output.
What works well with ChatGPT:
- Persona-based setups ("You are a senior data analyst at a Fortune 500 company")
- Conversational flow with refinement turns
- System messages for persistent rules
- Clear output format specifications
Gemini: Multimodal and Task-Decomposed
Google's Gemini models respond best to prompts that are:
Task-decomposed
Gemini handles complex tasks well when you break them into explicit steps. Rather than a single complex instruction, listing the steps in order tends to produce more thorough output.
Multimodal-aware
Gemini's native multimodal capabilities mean it can process images, audio, and video alongside text. When working with non-text inputs, providing explicit instructions about what to focus on in the media produces better results.
Grounded in specifics
Gemini responds well to concrete, specific prompts. Abstract or philosophical prompts can lead to verbose, unfocused responses. Anchoring your request in specific scenarios or examples helps.
Integration-friendly
Gemini works well within Google's ecosystem. When your task involves Google Workspace data, web search, or code execution, leaning into those integration points gives Gemini an advantage.
What works well with Gemini:
- Step-by-step task instructions
- Multimodal prompts combining text with images
- Specific, concrete examples
- Prompts that leverage Google ecosystem tools
Side-by-Side Comparison
| Aspect | Claude | ChatGPT / GPT-4 | Gemini |
|---|---|---|---|
| Best prompt style | Structured, direct | Conversational, role-based | Task-decomposed, specific |
| Structure format | XML tags | Markdown, natural language | Numbered steps |
| System prompt use | Strong | Strong | Moderate |
| Handles long prompts | Excellent | Good | Good |
| Negative constraints | Very responsive | Responsive | Moderately responsive |
| Few-shot examples | Highly effective | Highly effective | Effective |
Practical Takeaways
- Do not copy-paste the same prompt across models. Adjust structure and tone to match the model's strengths.
- Use XML tags for Claude, personas for ChatGPT, step lists for Gemini as your default formatting approach.
- Test your important prompts on multiple models if you have access. The best model for a task depends on the task.
- Keep your constraints explicit regardless of model. All three models benefit from clear boundaries.
Let the Tool Handle the Differences
Remembering format preferences across three model families is a lot to keep in your head. PromptArch's builder automatically adapts prompt structure based on your selected target model, so you get model-optimized output without needing to remember these differences yourself.
You can also browse model-specific prompt templates to see how the same use case looks when optimized for different models.