Four ways tools combine models
| Pattern | How it works | You get | Trade-off |
|---|---|---|---|
| Side by side | The same prompt goes to several models and each reply is shown | Every raw answer | You do the reconciling |
| Routing | A router picks the model it expects to suit the question | One model's answer, chosen for you | No cross-check unless added |
| Aggregation (synthesis) | Several models answer; another model merges them | One merged answer | The merge can hide minority views unless disagreement is surfaced |
| Judge or verifier | A model reviews drafts against each response | A checked draft | A judge is also a model and can be wrong |
Keplar combines routing, aggregation and a verifier: the router picks a panel, the answers are compared, a draft is checked, and one answer is written with the disagreements shown.
What published research reports
The best-known open example is Mixture-of-Agents (MoA). In the paper, layers of language-model agents each read the previous layer's outputs before answering. The authors report that an MoA built only from open-source models scored 65.1% on AlpacaEval 2.0 against 57.5% for GPT-4 Omni, and state-of-the-art results on MT-Bench and FLASK as well [1].
Read that carefully. It is one team's result on specific automated benchmarks from 2024, using that method; it does not mean any multi-model product, including Keplar, is more accurate on your questions. Keplar has not run or published a benchmark of its own.
Where combining models falls short
- Shared mistakes: models trained on similar data can be wrong in the same way, so agreement is a signal and not proof.
- Cost and speed: more models means more time and more compute; simple questions are usually fine with one.
- Merging can blur: a summary that hides a minority view removes information. The minority can be right.
- Stale knowledge: unless a model has live search, a panel only knows what its models were trained on. Keplar does not run web lookups for answers yet.
Collective intelligence, in context
Humans have long improved their joint problem-solving with institutions such as peer review and journals; Bostrom lists improving collective intelligence among the ways to enhance intelligence [2]. In his later taxonomy, a "collective superintelligence" is a system of many smaller intellects whose combined performance far outstrips any current cognitive system [3].
Multi-model AI is a small, practical echo of that idea: several intelligences, one output. It does not show that combining today's models leads to superintelligence, and Keplar makes no such claim. See What is superintelligence? for the wider debate.
How Keplar does it
For a simple question Keplar uses one model. For harder ones the router picks a panel with models from different families, the answers are compared into positions and an agreement level, a draft is checked by a verifier, and one answer is streamed. Under it you can open Consensus, Models consulted, Sources the models cite, Disagreements, Verification and Reasoning summary. The Consensus section measures how strongly the models agree; it is not a vote count or an accuracy score.
The Free plan uses only free models. Paid plans add premium models. See which models Keplar uses.
When multi-model is worth it
- Decisions with trade-offs: rent or buy, which tool to pick, how to study.
- Facts you cannot easily check yourself, where a split is a useful warning.
- Research, writing and coding questions where a second or third opinion saves a mistake.
- Not for: quick lookups, arithmetic, or anything you can verify in seconds.