An artificial intelligence model learns patterns from training data and represents those patterns in parameters that are used during inference. Depending on its design, the model can classify information, retrieve or rank candidates, reason over a problem, generate language or code, or support another specialized task.
Models differ in capability, latency, cost, privacy characteristics, deployment options, and reliability. Those differences mean that a model should be evaluated in the context of the workload it will perform, rather than treated as universally better or worse than every alternative.
