Can these agent-benchmaxxed implementations actually beat the existing machine learning algorithm libraries, despite those libraries already being written in a low-level language such as C/C++/Fortran? Here are the results on my personal MacBook Pro comparing the CPU benchmarks of the Rust implementations of various computationally intensive ML algorithms to their respective popular implementations, where the agentic Rust results are within similarity tolerance with the battle-tested implementations and Python packages are compared against the Python bindings of the agent-coded Rust packages:
Сафонов подписал контракт с ПСЖ летом 2024 года. Вместе с клубом он выиграл шесть трофеев, включая победы в Лиге чемпионов и Суперкубке УЕФА.。一键获取谷歌浏览器下载对此有专业解读
，这一点在服务器推荐中也有详细论述
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For each model reasoning was enabled, and the reasoning effort is set to high. I included GPT 5.2 because it could be argued that it can reason better than mini. However, I couldn't test GPT 5.2 as much as the other models because it was too costly. Gemini 3 Pro was costly as well, but it didn't spend as much time as GPT 5.2 during reasoning which made it more affordable in my experience.