Neon and Mubit release a template for an agent that cuts its own frontier-model tokens by 73%

Neon and Mubit published a template for a data analyst agent on Neon Functions and Lakebase Postgres that sends every model call through Mubit to Neon AI Gateway. In Mubit's benchmark on 20 CL-bench questions run three times each, the setup cut gpt-5.5 input tokens by 73% and answered 40% faster, with accuracy at 65% versus 60% for gpt-5.5 alone. Mubit moves routine steps such as listing tables and writing up answers to a small tuned model after it passes an exam built from the task's own traffic, while gpt-5.5 still writes every SQL query.