How to ask ChatGPT, Claude, and Gemini the same question
One question, three models, zero tab-switching. The method for a fair bake-off — and the economics that make it practical.
The short answer
Running multiple AI models in parallel means sending the same prompt, with the same context, to several models at once — so you compare answers on merit instead of guessing which model to trust. The discipline: identical input, judged by criteria, not vibes.
Why ask more than one model?
No single model is best at everything, and the ranking changes with every release. More importantly: a confidently wrong answer from one model looks exactly like a correct one. When two models agree and a third disagrees, you've found the exact spot that deserves your attention. That disagreement is the cheapest review you will ever get.
This matters most when the question has stakes. Pricing a retainer, choosing an architecture, wording a difficult email — these are decisions you make once and live with for months.
The old way: three tabs and a prayer
The manual version is painful: three accounts, three tabs, re-pasting the same prompt, re-uploading the same files. And it's secretly unfair — by the third tab you've paraphrased the question slightly differently, so you're no longer comparing models. You're comparing prompts.
The parallel method
1. Fix the context
Every model gets the exact same input — the same files, the same constraints. In Alyph this is the shared context above the branch point: every model branch inherits it identically.
2. Fix the prompt
Send the identical question, once, to all three. No re-pasting, no drift.
3. Judge by criteria
Read the answers side by side and score them on:
- Edge cases — who noticed the risk you didn't mention?
- Assumptions — who made silent ones, and who stated theirs?
- Fit — whose answer matches how you actually work?
- Signal — who answered the question versus who performed thoroughness?
4. Continue the winner
Take the strongest answer and keep working in that thread. The losing branches get deleted — you keep the result, not the noise.
The economics
Key takeaway
A three-model bake-off of a typical business question costs cents. Acting on a wrong answer costs the whole project.
A word of honesty: Alyph is not cheaper than a subsidized consumer subscription, and it doesn't try to be. What it removes is the broken workflow — one wallet instead of three accounts, one prompt instead of three paste jobs. You pay standard provider token rates plus a ~15% infrastructure margin, with hard spending limits. You're paying for the comparison being fair. The cents take care of themselves.
The checklist
- Same context for every model
- Identical question, sent at the same time
- Score on edge cases, assumptions, fit, signal
- Continue the winning thread; delete the rest
- Re-run bake-offs after major model releases
Written by Roman Abashin
Founder of Alyph
Roman runs a one-person startup and spends about $2,000 a month on AI, all of it through Alyph. Why he built it →
Keep reading
See it on the canvas
Reading about branching is one thing. Pruning thirteen messages of fighting down to one good answer — and watching the noise leave your context — is another.
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