The parameters. Stateless — does next-token prediction and nothing else. "Claude Opus 4.x" and "GPT-5.x" are models. On its own a model can't do anything agentic; it has to be harnessed.
Models can't read files, run commands, browse the web, or remember yesterday — it takes tokens in and predicts tokens out, once per model provider request. Everything that feels like an agent working — choosing tools, reading results, looping until the task is done — is the harness orchestrating many of those predictions in a row.
Model providers ship models in tiers: a large one that's smartest but slow and expensive, and smaller ones that are faster and cheaper but less capable. Picking a tier is a real decision — heavyweight for planning and hard debugging, lightweight for mechanical changes — and harnesses let you switch mid-session.
Being strict about the word also sharpens diagnosis. "The model is bad at this" is a specific claim — the same model in a different harness, or with a different context, often behaves completely differently. Before blaming the model, check what it was given: most disappointing output traces back to context or harness, not parameters.
Usage:
"Should we switch the model from Sonnet to Opus for the planning step?"
"Try it — but the harness is doing most of the lifting on this task. The model swap won't help if the system prompt and tools are wrong."