Does Closed-Source AI Perform Better than Open-Source? A Case-Study and Evaluation

The article discusses the debate between open-source and closed-source AI models, highlighting their respective advantages and disadvantages. Open-source models like Llama2 family, Mistral models, Stable Diffusion models, etc., offer cost savings in terms of optimized hardware infrastructure and operational efficiency. They also allow for extensive validation and exploration, fostering a deeper understanding and control over both the development processes and the data. Closed-loop models, on the other hand, provide straightforward integration paths with minimal configuration and are often plug-and-play, significantly reducing the technical barrier for adoption. However, they may come with more stability and ongoing refinement but at the cost of less transparency and user control over the model's workings and decision-making processes. The article also explores the practicality of both models in a real-world scenario by comparing Deepgram's closed source Nova-2 model and OpenAI's open source Whisper model for basic transcription tasks. Both models were found to be relatively easy to invoke, with pretty solid support from their parent company/the developer community. The future will likely see the continued development of both types of models, each serving different needs in the AI space.