Echo AI routes tasks across open-weight models to match top performance at lower cost

A developer has built Echo, an AI system that dynamically distributes incoming requests across a pool of open-weight models — including GLM-5.2 and Kimi K2.7 — rather than relying on a single model for every task. For each request, Echo determines how much compute to allocate, which models should contribute, and how their outputs should be merged. In benchmark evaluations, Echo matched the aggregate performance of Fable, a stronger reference system, while using roughly one-third of the inference cost. The builder noted that weaker models often proved surprisingly complementary, performing well on specific problem types or within certain combinations. Echo is publicly accessible via a chat interface and an OpenAI-compatible API, with the developer actively investigating failure cases and testing the approach on coding and agentic tasks.