Why Testing AI Prompts Across Multiple Models is a Pain (and How I Fixed It)
Over the past year, the number of AI models has exploded.
We have OpenAI, Anthropic, Mistral, Google, Cohere… the list keeps growing.
As a developer, I often found myself asking:
- Which model gives the best answer for my use case?
- Why does this prompt work perfectly on one model but fail on another?
- Do I really have to copy-paste the same prompt across 10 different playgrounds just to compare?
That frustration led me to build PromptOp a platform where you can run one prompt across 25+ AI models in one place, compare results side by side, and save your best prompts for future use.
Why this matters for developers
If you’re building with AI, testing prompts isn’t just a fun experiment it’s essential:
- Reliability: Different models interpret instructions differently.
- Cost optimization: Sometimes a smaller, cheaper model performs just as well as a flagship one.
- Consistency: You don’t want your app breaking because a prompt suddenly outputs something strange.
How I approached the problem
Instead of juggling multiple dashboards, I wanted one workflow:
- Type a prompt once.
- See results from multiple models instantly.
- Save and reuse prompts that work.
Here’s a quick example:
“Explain recursion as if I’m 5 years old, then as if I’m a software engineer.”
In PromptOp, I can see how GPT-4, Claude, and Mistral each handle it side by side.
What’s next
I’m working on adding:
- Team collaboration (share prompt libraries with colleagues)
- Model benchmarks (speed, cost, accuracy comparisons)
- Advanced tagging/search for saved prompts
🙌 I’d love feedback from the Dev.to community:
- What’s your current workflow for testing prompts?
- Do you care more about speed, cost, or accuracy when choosing a model?
You can try PromptOp free here: PromptOp.net
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