A startup needs to classify thousands of customer support emails per hour at minimal cost and fast latency. Which Claude model is MOST appropriate?
Select an answer to reveal the explanation.
Short Explanation
High-volume, simple classification is exactly Haiku's wheelhouse. Use the right tool for the job, and save Opus for the hard stuff.
Full Explanation
Claude Haiku is specifically designed for high-throughput, cost-sensitive, low-latency use cases. Email classification is a well-defined, relatively simple NLP task where Haiku's capabilities are more than sufficient. Using Opus for this would be like using a chainsaw to slice bread — overkill and expensive. The maximum classification accuracy choice is the most capable model but far more expensive and slower, so it's a poor fit for high-volume simple tasks. The Sonnet choice is wrong because multilingual support is not uniquely associated with Sonnet. The choice-of-model-has-no-effect claim is wrong because model choice has direct, significant impact on both cost and latency.