Multi-Model Prompt Converter
Paste one prompt and get it adapted for ChatGPT, Claude, and Gemini at once — each version tuned to that model’s actual conventions.
The same underlying request needs genuinely different formatting to perform well across models, since ChatGPT, Claude, and Gemini each respond differently to structure — a prompt written for one and pasted unchanged into another often underperforms. This tool takes one prompt and adapts it into each model's actual preferred conventions, not just a copy with the model name swapped.
How to use it
Written for any model, or written generically.
ChatGPT, Claude, and Gemini each get genuinely adapted versions, not the same text relabeled.
Review the structural differences between versions, not just the model name.
Tips for better results
- Notice what actually changes between versions, not just what stays the same. The structural differences are the useful part -- they show you what each model responds to.
- Test the converted versions, don't just trust the conversion. Model behavior updates over time -- periodically verify the conversion still reflects current preferences.
- Use this on your best-performing prompts first. Converting a prompt that already works well on one model is more valuable than converting a mediocre one.
Example output
“The same data-analysis request becomes role, task, format, and tone fields for ChatGPT; XML-tagged sections for Claude; and context, task, and format sections for Gemini -- same intent, genuinely different structure.”
TL;DR
A prompt written for one model rarely performs identically on another, even when the underlying request is the same — not because the models disagree on the task, but because each has genuinely different conventions for how instructions are best structured.
Why the same prompt doesn’t perform the same everywhere
Each model was trained with different conventions baked in — Claude’s training leans on structured, tagged input more than the others, which is why wrapping a task in explicit tags tends to sharpen its output more than it does for ChatGPT. Gemini’s grounding behavior for real-time information works differently from the other two, so a question involving current events benefits from an explicit instruction to search first, rather than assuming the model will do so automatically. None of this means one model is better — it means treating a single prompt as universally portable across models leaves real performance on the table.
This matters most for anyone maintaining a library of prompts used across multiple AI tools — a marketing team standardized on ChatGPT internally but occasionally testing Claude for higher-stakes writing, or a developer prototyping against several APIs before choosing one, both benefit from not having to manually reverse-engineer each model’s quirks from scratch every time a prompt needs porting.
Pasting a Python function-writing prompt with “Code generation” selected produces:
“=== CHATGPT VERSION ===
Write a Python function that takes a list of dictionaries…
(ChatGPT note: consider adding ‘return only the code, no explanation’…)
=== CLAUDE VERSION (XML-tag structure) ===
<task>
Write a Python function…
</task>
<output_format>Specify the exact format…</output_format>
=== GEMINI VERSION ===
Write a Python function…”
That gives three versions built around each model’s actual documented conventions, not just the same text copy-pasted three times.
FAQ
Does this actually call each AI model to test the prompt?
No — this is a client-side tool that restructures your prompt based on each model’s documented conventions. You still need to paste each version into the actual model to see real results.
Which version should I use if I only have time to test one?
Start with whichever model you already use most day to day, then spot-check the other two versions if the task is important enough for the model choice to genuinely matter.
What if my prompt doesn’t fit neatly into any of the four prompt types?
Pick the closest match — “General instruction” is a safe default that still applies the core structural adaptations even if the task-specific notes aren’t a perfect fit.
Will the Claude XML-tag version work if I’m not using the API directly?
Yes — the tag structure works fine pasted into Claude’s regular chat interface too, not just the API, though the API-specific prefill note in the output only applies if you are building directly against the raw API yourself.
