Case Study: Combining GPT-5.6 Luna and Sol for Cost-Efficient AI

I wanted to test whether I could combine Luna and Sol so that Luna would start working on a task and automatically hand it over to Sol when it recognized that the problem was beyond its capabilities. My hope was to get Sol-level performance at a fraction of the cost. I added a simple tool to my test harness that Luna could call when it decided that continuing on its own was no longer productive and that a more capable model (Sol) should take over the investigation. It didn't quite get Sol performance, but the results were still interesting. In terms of performance, Luna + Sol performed much better than Luna alone, but slightly worse than Sol: Luna alone: 31.6% Luna + Sol: 74.5% Sol alone: 87.2% Regarding the median cost per challenge, it was $0.07 with Luna + Sol, compared with $0.29 for Sol alone. So the combined approach didn't quite reach Sol's performance, but it was close enough, and the cost savings were substantial.