Echo: Cutting Costs to One-Third with Open-Weight Models
Fable-level results at one-third the cost.
1What is Echo?
Echo is an experiment that uses a pool of open-weight models to create a single AI system. It selects the most suitable models for each task and combines their outputs.
2Initial Experiment
The project started with models like GLM-5.2 and Kimi K2.7. Each model was subjected to the same evaluations, and the best combinations were analyzed.
3Hypothesis and Reality
The hypothesis was that knowing the best models for each problem in advance would improve performance. Echo aims to achieve this advantage without prior knowledge.
4Computation Allocation
Echo decides how much computational power to allocate, which models to use, and how to combine outputs for each request.
5Model Complementarity
A model that is generally weaker can be extremely useful in specific problems or combinations. Echo leverages this complementarity.
6Results and Cost
Echo achieves results similar to Fable at one-third the cost. However, it can make wrong allocation or combination decisions in some cases.
Original article: Hacker News — Echo: Açık Ağırlıklı Modellerle Maliyeti Üçte Bire Düşürme