Recent evaluations of prompt optimization techniques have highlighted the performance of various AI models, notably Claude Sonnet, OpenAI's O1, and Deepseek R1. Benchmarking tests involving five distinct datasets and five optimization algorithms revealed that prompt optimization can enhance accuracy by two to three times compared to baseline prompts. Claude Sonnet emerged as the top performer, surpassing O1 in effectiveness. While O1 demonstrated partial success in specific applications, other models such as Gemini 2.0 and Claude struggled significantly. Observers noted that Claude Sonnet is not only more effective but also cheaper and faster for simpler tasks, although it still encounters challenges with more complex tasks.
I think a lot of people are sleeping on using claude-sonnet to do meta-prompting/prompt optimization we found its better than o1 (and cheaper/faster) it still struggles for complex tasks (curious to see how o3 would do) but it works quite well for simpler one https://t.co/AymCk48MkF
After testing Deepseek R1 and OpenAI O1 extensively, i still find Claude 3.5 Sonnet with this system prompt far more useful for most tasks i do daily: https://t.co/PhneoO2LWw
📀Exploring prompt optimization We created 5 different datasets and 5 different algorithms for prompt optimization. Here's what we learned: 🥇Claude sonnet performs best (> o1) 🧠Prompt optimization ~ memory: most effective on tasks where the model lacks domain knowledge https://t.co/8pno8efJoB