Bigger isn’t always better: Examining the business case for multi-million token LLMs

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The race to expand large language models (LLMs) beyond the million-token threshold has ignited a fierce debate in the AI community. Models like MiniMax-Text-01 boast 4-million-token capacity, and Gemini 1.5 Pro can process up to 2 million tokens simultaneously. They now promise game-changing applications and can analyze entire codebases, legal contracts or research papers in a single inference call. At the core of this discussion is context length — the amount of text an AI model can process and also remember at once. A longer context window allows a machine learning (ML) model to handle much more information in a single request and reduces the need for chunking documents into sub-documents or splitting conversations. For context, a model with a 4-million-token capacity could digest 10,000 pages of books in one go. In theory, this should mean better comprehe ... Read full article.