How we built task-aware routing across models and harnesses to cut costs on real-world coding tasks at Databricks
by Ankit Mathur, Ivan Zhou, Bryan Qiu, Rohit Agrawal, Elise Gonzales and Kelly Albano
The price and performance frontier for coding tasks features a huge diversity of models and harnesses: in 2026 alone, we’ve seen 33 new models released. In our prior post about benchmarking against the Databricks codebase, we found that models cluster into capability tiers and that much everyday work (e.g., flipping a flag, a single-file edit, a well-scoped bug fix) did not require the most expensive models.
So how do you reduce AI coding costs without sacrificing developer productivity? One of the biggest opportunities is matching each task to the right model instead of defaulting every task to the most capable (and most expensive) option. Just leveraging lower cost models can save you 50%+, but it’s incredibly daunting for users. With a proliferation of great models and capable harnesses, coding agent users are constantly faced with choice overload. Instead of wasting time trying to select the best model for every task, many are setting the most capable at the highest effort and moving on. Instead of asking users to choose or stunting productivity with hard caps, we knew we needed to innovate.
