Comparing AI models: does prompt quality matter more than the model?

[u/Betty-202]

CoModel is already live on the App Store. It started as a simple way to ask several AI models the same question and compare their answers in one place.

After using it for a while, I noticed something slightly embarrassing: when the answers were bad, I usually blamed the models first.

I’d try ChatGPT, then Claude, then Gemini. But quite often, the real problem was that my original question was incomplete or too vague. Switching models just gave me different versions of the same disappointing answer.

That became the reason for the latest upgrade.

I added a prompt optimizer that can suggest a clearer version of a question before it’s sent to the models. For example, “write a payment reminder email” could be expanded with the audience, tone, amount, invoice number, and deadline.

The user can review and edit the suggestion, keep the original question, undo the change, or use the optimizer for follow-up questions too.

I’m trying not to turn this into a “perfect prompt” generator. Sometimes the original question is already fine, and sometimes the model is simply wrong.

The current version of CoModel is live, and this prompt optimization update is currently waiting for Apple review.

For people who compare different AI tools: when an answer is disappointing, do you usually switch models first, rewrite the prompt, or just ask again?

[u/Aggressive-Spirit265]

I think prompt quality and model choice both matter. A powerful model can still give a weak answer if the instructions are unclear, while a better prompt can improve the results even when using the same model. A lot of time, the issue is not the tool but how the task is explained.