Comparing Top Large Language Models for Python Code Generation - 340

Explore a comprehensive comparison of leading large language models for Python code generation in scientific image analysis. Dive into an experiment testing Claude 3.5 Sonnet, ChatGPT-4, Meta AI Llama 3.1 405-B, Google Gemini 1.5 Flash, and Microsoft Copilot. Follow along as the models are tasked with segmenting nuclei in a multichannel microscopy image, measuring mean intensities of other channels in segmented regions, calculating ratios, and reporting results in CSV format. Discover which models excel at generating code for Stardist segmentation and which require more guidance. Gain insights into the strengths and limitations of each model, including their ability to produce statistically identical results for intensity measurements. Understand the importance of detailed prompting and domain expertise in obtaining reliable results, despite rapid AI advancements. Access all generated code through the provided GitHub repository link for further analysis and comparison.