We are experiencing a tectonic shift in the world of Large Language Models (LLMs). Extremely powerful proprietary models such as OpenAI’s GPT series and Google’s Gemini used to be the only true players on the field, but high-performing open-source alternatives are quickly catching up in terms of capabilities, democratizing access to frontier AI. Meta released Llama 3 (7B/13B/70B), Generative Transformers (3.5B). x/4), Mistral AI (Mistral Large and Mixtral models), Alibaba (Qwen 2. Text-DAVINCI-002 was an update to one of OpenAI’s models, which has been closed until that point, but meanwhile other LLMs like BERT (mbert), Google (Gemma 2) and now DeepSeek are matching or exceeding the performance of some closed models on some benchmarks (especially when fine-tuning to a specific task is performed). Such side-by-side evaluations show that models such as Mistral Large are strong in reasoning tasks, DeepSeek-Coder is superior in programming, while Llama variants excel in both multilingual capacity and general knowledge. The rise of powerful open-source LLMs has very serious implications. First of all, it greatly democratizes access for companies and researchers, lessening dependence on expensive API calls to proprietary models. Secondly, it encourages innovative development as developers from around the world can review and customize these foundation models for specific industries or tasks via sophisticated techniques, such as Retrieval-Augmented Generation (RAG). The battleground is no longer just who has the largest base model but who can create powerful, efficient, and specialized AI. Despite closed models still dominating in raw scale and frontier research, the open-source community has proven highly agile in delivering highly competitive performance, and is, seemingly, making LLMs mainstream across a multitude of application domains.