No. SleekRank renders whatever you put in your data source. The recommended pattern is to maintain two columns: claimed_accuracy from the vendor's marketing page and measured_accuracy from your testing or a cited third party. Both render side by side on every comparison page, giving readers the context to weigh claims against independent measurement.

When you re-run benchmarks, update the measured_accuracy and false_positive_rate columns in the sheet. Every per-detector and pair page reflects the new numbers on the next cache cycle. The last_benchmark_date column renders next to the figures so readers see how fresh the test is, keeping accuracy claims auditable across the catalog.

Track a detector_version column and update it when the vendor announces or you detect a model change. The version pill renders on the per-detector page next to the benchmark date, so readers see whether your accuracy figure reflects the current model or a prior version. Vendor silent updates show up in your audit cadence and propagate.

Yes. Add a pricing_model column (per credit, per month, institutional) and surface it via tag mapping. The pricing card adapts to the model type. Institutional-only tools show a contact-for-quote pill instead of a number; per-credit tools show cost per check; subscription tools show monthly tier. One template, three pricing modes from a column.

Store supported_models as a JSON array column (GPT-4, GPT-4o, Claude 3.5, Gemini 1.5, Llama 3, mixed). A list mapping renders the supported models grid on every page. When a detector adds Claude 3.7 detection, update one cell on that row and every per-tool and pair page reflects the addition on the next cache cycle.

Yes. The pairs page group joins both detector rows at render time and computes the delta in a template helper for accuracy and false-positive rate. Readers see Detector A: 99% accuracy / 2.8% false positive vs Detector B: 98% accuracy / 3.5% false positive plus the absolute delta. One join handles all pairs uniformly without per-page math.

Add a noindex column to the pairs sheet. Pairs where one detector lacks measured benchmarks can be held back via meta mapping until benchmarks fill in. Better to have 30 substantive comparisons indexed than 200 thin ones competing for crawl budget, especially in a YMYL-adjacent category readers fact-check carefully.

Remove the row from the sheet. After the cache window, the URL stops generating and falls out of the sitemap. Pair pages where the detector appeared also stop generating because the join fails on the missing row. Set up a 301 redirect from the old per-detector URL to a successor or category page to preserve link equity from existing inbound links.