When I added compare pages to the Top AI Tools directory, the first question I had to answer was: how many pairs am I actually looking at? With roughly 200 models across 8 pipeline tags, the naive upper bound is 200 × 199 / 2 ≈ 19,900 pairs. Generating content for each one with Claude Haiku would cost somewhere around $20 per run — not ruinous, but not something I wanted to run daily without thinking carefully.
Here's what I actually built, where it falls short, and what I'd do differently if starting over.
The combinatorics problem
Model compare pages exist for a specific type of query: "llama 3 vs mistral 7b", "stable diffusion vs sdxl", "whisper vs wav2vec2". These are high-intent queries — the user has already narrowed down to a shortlist and wants a concrete decision nudge. The static SSG approach I'm running means I need to precompute each compare page at build time, which puts pressure on how many pages I can afford to generate.
The solution I landed on: group by pipeline_tag, pair the top-4 models by download count within each group, then cap total pairs with a COMPARE_LIMIT env var. Within a single pipeline like text-generation, the top 4 models give 6 pairs (4 choose 2). Across 8 active pipelines that's roughly 48 pairs. The env cap of 50 means I stay within that budget while having room to grow.
