In Episode #1020, Jon Krohn unpacks the two dials that increasingly decide what you get out of a large language model: which model size you pick and how much effort you tell it to spend. Using a July Anthropic blog post by Claude Code’s Lydia Holly as a jumping-off point, with guidance that generalizes to any model family, Jon explains what each setting actually does under the hood. Model size swaps which frozen weights handle your request (roughly, how capable), while effort sets how thorough and certain the model must be before calling a task done, not a simple “thinking-time slider.” He offers a clean diagnostic for when to raise effort versus move to a bigger model, shows why cheaper-per-token isn’t always cheaper-per-task and surveys how OpenAI, Google and open-weight labs have all converged on these same two dials.
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