The fast progress of large language models (LLMs) over the last years has had a profound impact on many industries, specifically education and everyday life. Nevertheless, the hyper-expansion of model parameters and the computational resources to run them has raised concerns about affordability and efficiency. This article argues that, rather than utilizing general-purpose LLMs—often trained on extremely large datasets—specialized language models (SLMs) using educational data specific to a user domain will exceed performance, lowering both the cost and deployment of models, each personal to their own unique purposes. This research reviews advances in technology year-over-year in the field of LLMs, specifically exploits in advancing costefficiency and efficiency, while providing values of how personal SLMs will function as digital mentors and assistants for millions to improve learning and provide access to scalable personalized support. In this report, we investigate the recent stateof-the-art developments in LLMs, particularly those enhancing performance or lowering costs. With SLMs, individuals and organizations are able to leverage LLMs to create adaptive, domain-specific AI assistants that will improve learning outcomes for millions around the world and provide personalized support in a cost-effective way. The use of SLMs represents a significant step change in AI-enabled learning and digital mentorship. Keywords: Large Language Models (LLMs), Specialized Language Models (SLMs), Education, Efficiency, Affordability, Computational Resources, Personalized Support, AI Assistants, Cost-Effective, and Digital Mentorship.