Article URL: https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase Comments URL: https://news.ycombinator.com/item?id=48837696 Points: 3…

At Databricks, the way we build software is changing quickly as we aggressively adopt AI for engineering. The landscape of models and harnesses for code authoring has rapidly expanded in the last year, giving developers more choices than ever. With more options, it has become increasingly important to understand which coding agents offer the best performance on real-world coding tasks as well as understanding how task-performance varies with price. This article shares the results and methodology of the internal coding benchmark we built at Databricks, which evaluates tools on actual coding tasks our engineers performed on the Databricks codebase. Tasks featured edits against a multi-million line codebase covering many popular languages (Python, Go, Typescript, Scala, etc.) and both tasks and solutions were carefully reviewed to ensure accuracy. This isn't meant to be comprehensive, but the exercise surfaced insights that have already made our engineering team meaningfully more efficient with coding agents. Below, you can see how models and harnesses scored on the overall benchmark: Specific results being a couple points off can often even out in real world tasks. We focused more on the thematic patterns that help us reason about which models to use for various tasks. In fact, the results showed clear clustering of the models and harnesses into 3 capability tiers. Figure 2: Three distinct capability tiers emerged in our overall results, with nuance in which models were effective in each group At the upper end of performance, we see that the most intelligent models are very effective at solving all kinds of problems, but they’re very expensive. Medium and lower intelligence models are still highly effective at the common tasks, and in many cases, they’re also significantly cheaper. Day to day, engineers do a lot of different things that vary significantly in complexity: common operational tasks like flipping a flag or updating configs don’t require extremely intelligent models, but deeper design explorations do. However, in the past, our default models were always the most expensive ones. Based on this analysis we determined we should push more work to the Haiku and GPT 5.4 Mini class of models. There’s been a lot of excitement about GLM 5.2, and our results showed evidence that GLM can be a daily driver model for a lot of our developers. It landed in the top capability tier, statistically tied with Opus 4.8 on quality, but costing $1.28/task against Opus’s $1.94. The GLM quality scores are consistent with qualitative feedback we’ve gotten from internal developers who have been piloting GLM for daily development. Because of its great performance for everyday coding tasks, we’ve been focused on serving GLM with the best performance, and the evidence shows it’s time to start deploying these as daily drivers for coding.