The AA benchmarks seem to be mostly "complete this small intelligence-proving task from scratch". That consistently puts models with high intelligence to output tokens on the frontier, especially the OpenAI models, on the frontier. But a lot of software engineering work is many-turn work on established code bases. There's a lot of reading and a relatively large context unless it's carefully controlled. Writing only a few output tokens doesn't really cut costs because most of the spend is read tokens that get recycled through dozens of turns. That's basically the ideal case for DeepSeek because of how cheap cached tokens are.