Why Pricing Transparency Matters in a Cost Comparison
The Compare Prices tool is the fastest way to put a shortlist of LLMs in a single grid — input cost, output cost, context window, and quality score in stacked columns you can scan vertically. Provider dropdowns let you mix models across Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, and xAI without leaving the page. Most teams compare LLMs on per-token input price and call it a day. Pricing Transparency is one of the under-attended dimensions that shows up in the Compare Prices grid — and ignoring it is where the surprise bills come from. Provider pricing pages aren't uniformly readable. OpenAI bundles GPT-5 input and Cached input; Anthropic separates cache-write and cache-read. The Compare Prices grid normalizes to the same input/output base rate. For the underlying math, see tokens-to-dollars conversion; for routing strategy see multi-model routing with quality scores.
How Pricing Transparency Appears in the Grid
In the grid, every model shows input cost per 1M and output cost per 1M in the same units. No bundles, no discounts, no surcharges — just the base rate.
Reading the Spread
When you want to model caching impact or batch discounts, do that downstream in /tools/api-cost-estimator. The grid is for cross-model comparison; the estimator is for total-cost modeling.
Practical Implications
Watch for: cache-read pricing (typically 10% of base), batch API pricing (typically 50% off), Anthropic prompt caching write surcharge (typically 25% above base). These bundle out in the grid for fairness.
Workflow: From Grid to Decision
Bookmark /tools/compare-prices and check it before signing a contract — vendor sales decks sometimes quote outdated or non-standard rates.