The current flagship in the world of LLMs is the Generative Pre-trained Transformer 4 model (GPT-4) from OpenAI. It is based on 8 separate models, each containing dozens of network layers and 220 billion parameters, which are supposedly linked together using the Mixture of Experts (MoE) architecture. GPT-4 is built on a transformer architecture, combining self-attention and feed-forward neural networks to process input tokens. Each token represents a text string containing a word or phrase. Therefore, the token limit represents the amount of text that an LLM can consider at a given time as input. Early LLM releases had very low token limits since LLM calculation time is strongly dependent on the token length. Initial releases of GPT3 had a token limit of 2,048 tokens, but recent releases of GPT-4 has a token limit of 128,000 tokens. To put this into context, the average length of a PubMed abstract is 114 tokens (sd 48.83) and an article is 2,378 tokens (sd 1,604.79). So while increasing complexity of the LLMs has enabled using entire papers (or even groups of papers) as input, there is still the computational cost of running GPT-4 calculations to consider. As an example, when asking a question of medium complexity via a string of fewer than 10 tokens, such as 'Composition and property ranges of material XY', then the rough total cost estimate to answer this question for GPT-4 is about 7-10 Euros. Getting the same answer from a classical knowledge graph would incur only about one-hundredth of this cost and also take less time, provided the information is in the corpora and mapped in a graph accessible by search engines.