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    Topic cluster

    Models & Providers

    Selecting the right model for a specific task and budget.

    What does models & providers cover?

    Comparing foundation models, providers, pricing and capability trade-offs for business workloads.

    Definitions in this cluster

    • Large language model

      A large language model is a neural network trained on very large text corpora to predict the next token in a sequence. That single capability, at scale, produces summarisation, translation, classification, code generation and reasoning-like behaviour.

    • Fine-tuning

      Fine-tuning further trains an existing model on your own examples so it adopts a specific format, tone or classification behaviour. It changes how a model responds, not what facts it knows.

    • Context window

      The context window is the maximum amount of text — measured in tokens — a model can consider in a single request, covering the instructions, supplied documents, conversation history and the response.

    • Token

      A token is the unit of text a language model processes — roughly three-quarters of an English word. Model pricing, context limits and latency are all measured in tokens.

    • Inference

      Inference is the act of running a trained model to produce an output. In business terms it is the recurring per-request cost of an AI feature, as distinct from the one-off cost of building it.

    Guides & analysis