Echo – Fable-level results at 1/3 the cost using open-weight models

Echo treats model selection as a dynamic routing problem rather than a configuration choice made once at deployment. Instead of sending every task to the same API endpoint, it maintains a pool of open-weight models and routes each task to whichever member of the pool is most suited to it. The stated benchmark is Fable-level output quality at roughly one-third the cost. That claim needs scrutiny, but the underlying architecture — heterogeneous model pools with task-aware routing — is a genuinely interesting direction that is underexplored compared to the endless fine-tuning and prompt-engineering plays. The early-experiment framing is honest, which earns some credit. Reservation: the real test is whether the routing logic holds up on tasks that fall outside the training distribution of the routing model itself, and the benchmark methodology is not fully public yet. This is a Saturday-afternoon read and a thing to watch, not a production dependency today. -> Best for: AI engineer or SaaS team of 2-5 trying to cut inference costs without dropping to a clearly weaker model