If asking several AI models is a good idea, is asking twenty better than asking five? Almost certainly not. The right number depends on the question, on how different the models are, and on what you are willing to spend in time and money.
One model is enough more often than you think
For greetings, definitions, simple facts, rewrites and many everyday questions, strong models agree almost every time, and the cost of a wrong answer is low. Asking five models adds delay and cost without changing the result. A system that always consults many models is wasting resources on easy questions.
Two models can tell you something, but not much
With two models, you can learn whether they agree. If they agree, you gain some reassurance. If they disagree, you have a split with no way to tell which side is more likely right, apart from reading the reasoning. Two is a check, not a panel.
Three is the first real panel
Three models can form a majority. When two agree and one differs, you have a minority view to examine: it might be an error, or it might be the one that noticed something. Three also gives a reviewer something to work with. For many everyday-but-nontrivial questions, three varied models capture most of the benefit.
Beyond five, returns shrink
Each new model adds cost and latency. The added value depends on how different it is from those already seated. A sixth model from a family already present repeats existing blind spots. Practical limits also apply: more responses make the comparison step harder, a long panel is held up by its slowest member, and the final answer can become a blur of positions.
Diversity beats count
What you want is independent errors. Diversity can come from:
- different providers and training lineages;
- open-weight versus closed models;
- different sizes and specialties, such as a reasoning model next to a fast general one;
- different approaches, such as one that retrieves and one that does not.
Three diverse models usually beat five near-copies.
The question decides
| Question type | Reasonable panel |
|---|---|
| Greeting, rewrite, simple fact | One model, or none if it is small talk |
| Everyday explanation, moderate analysis | About three, plus a review |
| Hard reasoning, high-stakes, contested topics | Up to about five, a reviewer, and a judge if they split |
| Exact arithmetic or counting | Compute with code; consult models only for the surrounding explanation |
Cost and time
A panel's cost grows with its size, and its latency is set by slower members unless you cut stragglers off. Systems need time budgets, a rule for "enough models have answered", and a way to say which ones did not respond. Missing models should not be treated as if they had voted no.
Signs you need more
- The first two models disagree and the stakes are real.
- The question concerns a contested or fast-moving topic.
- The reasoning has many steps with room for slips.
- You will act on the answer in a way that is hard to undo.
Signs you do not
- You can verify the answer in seconds yourself.
- The task is creative and there is no right answer.
- You only need a draft you will heavily edit.
A worked example
Take the question, "Should I use a fixed or variable rate for my mortgage?" A single model will give a sensible overview. Three varied models will probably agree on the structure of the trade-off and may differ on how to weight rate risk, which is exactly the part you want to see. A fourth and fifth model from families already present will mostly repeat the same two views. The extra value comes from a reviewer that reads the draft against the individual responses and notices that one model quietly assumed a rate environment the others did not. So the practical recipe is three diverse answerers, one reviewer, and a tie-break only when the panel actually splits.
FAQ
Is a bigger panel always more accurate?
No. Beyond a handful of diverse models, the gains are small compared with the cost, and correlated errors remain.
Should I pick the number of models myself?
Some tools let you. Others decide based on the question. Either way, size should follow stakes.
How Keplar approaches this
Keplar sets the number of models per question instead of always using the same panel. A rule-based classifier looks at the wording, length, attachments and topic. Simple questions get one model. Moderate ones get a three-model panel and a verifier. Complex ones can get up to five, a verifier, and a judge when the panel splits. The Free plan caps panels at three. Seats are picked for diversity first: different families, an open-weight seat where possible.
A time budget protects you from waiting on a straggler, and the answer says which models responded. You can also set thoroughness to Fastest, Balanced or Most thorough. Exact counting and arithmetic are computed by code. Read which questions use more models for the signals and thresholds.