A retail analytics platform processes product images to generate catalog descriptions and alt text at scale. The team is evaluating latency vs. cost for three approaches: (A) Claude Haiku with vision, (B) Claude Sonnet with vision, (C) Pre-classify images with a lightweight CNN, then route simple products to Haiku and complex lifestyle photos to Sonnet. Product mix is 70% simple product-on-white images and 30% lifestyle photography. Haiku cost is $0.25/1K images, Sonnet cost is $1.50/1K images. Which approach minimizes cost while maintaining catalog quality?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — approach C achieves the optimal cost-quality balance. At 70% Haiku / 30% Sonnet: (0.70 × $0.25) + (0.30 × $1.50) = $0.175 + $0.45 = $0.625/1K images — a 58% reduction versus Sonnet-all while preserving quality on the lifestyle images that require it.
Full explanation below image
Full Explanation
Approach C achieves the optimal cost-quality balance. At 70% Haiku / 30% Sonnet: (0.70 × $0.25) + (0.30 × $1.50) = $0.175 + $0.45 = $0.625/1K images — a 58% reduction versus Sonnet-all while preserving quality on the lifestyle images that require it. The CNN classifier adds minimal overhead and cost given its lightweight nature. Option A is cheapest but degrades quality on 30% of catalog images — lifestyle photos require understanding composition, mood, and context that Haiku handles less accurately. Option B wastes 70% of budget on simple images where Haiku is sufficient. Option D is aggressive in routing too much to Haiku (90%) — the mix should reflect actual complexity distribution, not an arbitrary cost target, to preserve quality.