This is a really interesting study. My key takeaways (and a caveat at the end): - There is no best model-harness combination for your task. You have to run your own experiements and choose based on your priorities (cost, accuracy, speed, etc.). - Simple AI harnesses are often competitive with complex ones (and much cheaper to run), so they're a good place to start. For example, start with Pi and only upgrade to a more advanced harness if you don't get the results you want. - A good harness with a weaker model can outperform a bad AI harness with a strong and expensive model. That said (and here comes the caveat), I would caution against these aggregate results. Often, the failure modes between different model-harness configurations differ widely, so you should look into individual cases and do some error analysis to get a better understanding of how each config works.