Claude Fundamentals and Model Landscape
Model tiers and tradeoffs
Claude is offered in capability tiers-typically Opus (highest capability), Sonnet (balanced), and Haiku (fastest, lowest cost). Candidates should understand that these are tradeoffs, not a strict ranking for every task: a smaller model that passes your evaluation is usually the better production choice because it reduces latency and cost. Also understand the context window (how much input plus output the model can handle in one request), multimodal input (text and images), knowledge cutoff (training data has a date; the model does not automatically know recent events), and the distinction between base capability and alignment training (Constitutional AI and human feedback). The API is stateless: the model does not remember previous calls unless you resend conversation history.
Traps
Questions often imply the largest model is always correct, or that a model can browse the web by default. Neither is true. Another trap is confusing the training cutoff with the context window: a long context does not make the model aware of events after training. Candidates also confuse raw capability with better instruction following-always tie model choice to measured task performance.
Rule of thumb
Start with the smallest, cheapest tier that meets your quality bar on a representative evaluation set; escalate only when you can show a measurable gap. For example, if Haiku passes 92% of classification cases and Sonnet passes 93%, the extra cost is hard to justify unless the 1% errors are high-stakes.
Verify current model names, context sizes, and pricing against official documentation, since these change frequently.