Rohan Paul (@rohanpaul_ai) · 10/23/2026, 02:00:26
DeepMind Paper Proposes Model Bidding for Near-Optimal Routing
A new Google DeepMind paper introduces an auction-based routing mechanism where AI models bid on tasks, with the highest bidder answering. In simulations, this approach achieved 99.2% of the best possible performance, significantly outperforming traditional routing methods and showing exceptional robustness on difficult prompts.
SOURCE COVERAGEFull Original Report
Rohan Paul (@rohanpaul_ai) · 2026-10-22 18
New Google DeepMind paper shows that if AI models offer money to answer each question and the highest offer answers, results are 99.2% as good as always picking the best model.
i.e. say you don't know in advance which model is best for each new question. So who should answer?
The paper’s answer: let each model offer money for the job. The model that knows it is good at this question offers more. Highest offer gets to answer.
They proved the loss shrinks when several agents usually value a prompt highly, as when distilled models perform close to the best. They simulated auctions among 110 models on over 30,000 EmbedLLM prompts.
With all 110 models bidding, the auction reached 99.2% of the best possible value, versus 89.4% for routing each prompt type to its best model. On each method's worst prompt cluster, the auction held 96.0% while the single best model kept 6.0%.
If you run a model marketplace, a simple pay-your-bid auction works well when every bidding pool has several strong models.
