Text completion machines and language models are both techniques for predicting what might come next in a sequence of text, but they have some key differences:

Text completion models are task-specific - they are trained to complete a specific type of text, like sentences, paragraphs, etc. Language models are more general and aim to model the probabilities of sequences of words in any text.
Text completion models might use task-specific features or constraints, while language models typically just use previous words/tokens as context.
Text completion models are often trained on a small, focused datasets, while language models are trained on large, general corpora of text.
Text completion is an application of language models - you can think of a text completion model as a specific type of language model that is tailored to a certain task.

So in general, language models are a broader technique and text completion models are one way of applying language models to a specific task. But they are related and there is overlap between the two concepts.
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