A large language model is a Transformer trained on broad text corpora to model the probability of the next token given everything before it. At sufficient scale, Brown et al. (2020) showed that this single objective yields models able to perform many tasks they were never explicitly trained on, simply by being prompted — the in-context learning behaviour that defines the modern LLM.
ChatGPT, Claude, Gemini and Grok are all LLMs. They differ in training data, scale, alignment and tooling, which is why their answers to the same question can diverge.
Why it matters at MultipleChat
MultipleChat runs several leading LLMs on the same prompt and compares their outputs, so the strengths of one cover the blind spots of another.