Why users shouldn’t choose their own LLM models (www.coderabbit.ai)

David Loker recently highlighted a critical perspective on offering users a selection of large language models (LLMs) for their applications, arguing that this approach can lead to suboptimal choices for non-technical users. While providing model options may seem appealing, it often results in confusion and inefficiencies unless the users are experienced ML engineers. Loker emphasizes that the reality of benchmarks can be misleading; they don't always translate to meaningful quality comparisons. Instead, a balanced blend of frontier and open models is recommended for better cost efficiency and performance. In a significant announcement, CodeRabbit has now integrated support for the NVIDIA Nemotron family of open models. This addition facilitates a more streamlined experience for self-hosted customers, promising to reduce code review times and bugs by up to 50%. By consolidating model choices and focusing on high-quality, well-supported options, CodeRabbit aims to enhance user experience and productivity, reinforcing the idea that model selection should be informed and streamlined rather than left to individual user preference.