Benchmarking Coding Agents on Databricks' Multi-Million Line Codebase
Databricks, Wednesday, July 8th, 2026
Databricks benchmarks coding agents on real tasks from its own multi-million-line codebase, finding the harness matters most.
Databricks created an internal benchmark to evaluate coding agents on real-world tasks drawn from its multi-million-line codebase.
The research finds that the best cost-to-performance models come from multiple providers, that open-source models like GLM 5.2 now compete with proprietary options, and that the harness used matters significantly more than token price alone.
The findings show task-level benchmarking is crucial, since models vary dramatically in reasoning efficiency on actual coding work.