Foundation Models vs Frontier Models: The Difference

Foundation Models vs Frontier Models: The Difference

The terminologies used to describe and categorize different classes of artificial intelligence models are expanding and evolving. The term “foundation model” was introduced in 2021 and another term called “frontier model” hit mainstream discourse beginning in 2024. There can be confusion between the two because of their seeming interchangeability. Some have also preferred using the latter because it is more forward-sounding. Others prefer using the former because of how it captures the very essence of these models. However, for the sake of preciseness, it is important to understand the differences between foundation models and frontier models.

Explaining the Difference Between Foundation Models and Frontier Models in Artificial Intelligence


It was in August 2021 when the Center for Research on Foundation Models of the Stanford Institute for Human-Centered Artificial Intelligence introduced the term “foundation models” to represent AI models that are trained on large and broad datasets and can be adapted to smaller and more specific tasks. These models are in contrast with earlier models that are often developed for a single purpose or a particular and defined set of tasks.

The aforementioned can also be said for frontier models. They can be adapted to smaller models that are remodeled for more specific tasks. The terminology first appeared in 2023 with the launching of the Frontier Model Forum by Anthropic, Google, Microsoft, and OpenAI in July 2023. The term gained further popularity across mainstream discourse in 2024 following the development of more advanced and larger models.

Both types of artificial intelligence models are trained on massive amounts of general data like text, images, videos, codes, or a combination of different data or multimodal data through artificial neural networks and machine learning architectures. They are also often based on self-supervised learning and attention mechanisms. Their large-scale data requirements and training techniques require and consume substantial computing resources.


It is important to reiterate the fact that the Center for Research on Foundation Models defines foundation models as large AI models that can be adapted to different ranges of downstream tasks through fine-tuning or remodeling. The Frontier Model Forum also defines frontier models as large AI models that can perform a variety of tasks but also noted that they exceed the capabilities currently present in the most advanced existing AI models.

Both foundation models and frontier models can be considered large general-purpose AI models. The main difference between the two is that the latter can also be regarded as cutting-edge and next-generation models. This means that frontier models are also foundation models but not all foundation models can be considered as frontier models. Another difference is that the latter tends to be more advanced in terms of parameters and capabilities.

Nevertheless, because of their general-purpose nature, both foundation models and frontier models can enable more effective and efficient development and deployment of smaller AI models. Frontier models have better future-proofing. However, because of ongoing progress, newer and better models are being trained and introduced constantly. This means that what can be considered “frontier” today might become standard tomorrow.