The modern cycle of creating simulation models is not complete without analysts, modelers, developers, and specialists from various fields. There are numerous well-known tools available to simplify simulation modeling, and in addition, it is proposed to use large language models (LLMs), consisting of neural networks. The article considered the GPT-4 model as an example. Such models have the potential to reduce costs, whether financial or time-related, in the creation of simulation models. Examples of using GPT-4 were presented, leading to the hypothesis that LLMs can replace or significantly reduce the labor intensity of employing a large number of specialists and even skip the formalization stage. Work has been conducted comparing the processes of creating models and conducting experiments using different simulation modeling tools, and the results have been formatted into a comparative table. The comparison was conducted based on the main simulation modeling criteria. Experiments with GPT-4 have successfully demonstrated that the creation of simulation models using LLMs is significantly accelerated and has great perspective in this field.

Keywords: Simulation modeling, large language model, neural network, GPT-4, simulation environment, mathematical model