Open LLM API price history: 3 months of verified price changes (dataset + calculator Space)

A maintainer of the open aicostbudget-ai/ai-api-pricing dataset, which tracks list prices for about 100 LLM APIs with each row linked to the provider's official pricing page and a verification date, has published three months of logged price changes alongside a new history dataset, aicostbudget-ai/llm-api-price-history. The dataset records 22 new models added in September and October and 16 deprecation and retirement notices, with each change labeled either as a provider price change or as a correction on the maintainers' side so fixes do not register as market moves. Prices are accepted only from a provider's own pricing page, docs, API or announcement, never from third-party aggregators, and unknown prices are left empty rather than set to zero; a companion LLM API Pricing Calculator Space loads the data live for monthly cost estimates at user-specified token volumes.

We maintain an open dataset of LLM API list prices about 100 models where every row links to the provider’s official pricing page and carries the date it was verified. Since July we also log every change, so here is what moved in the last three months. Price cuts Price increases Churn: 22 new models in September and October, 16 deprecation and retirement notices. Try it: the LLM API Pricing Calculator Space https://huggingface.co/spaces/aicostbudget-ai/llm-api-pricing-calculator has a price table, a monthly cost estimate for your token volumes, and the change log. It loads the data live, so it stays current. Use the data: current prices are in aicostbudget-ai/ai-api-pricing https://huggingface.co/datasets/aicostbudget-ai/ai-api-pricing ; the history dated snapshots and change events is in aicostbudget-ai/llm-api-price-history : python from datasets import load dataset changes = load dataset "aicostbudget-ai/llm-api-price-history", "price changes", split="train" .to pandas print changes changes "change type" == "price update" In the snapshots, each change is labeled either as a provider price change with its event or as a correction on our side, so dataset fixes don’t show up as fake market moves. How the prices are verified: prices are only accepted from the provider’s own pricing page, docs, API or announcement; third-party aggregators are never used as evidence. Each release captures the source passage, validates the schema and tiers, and is rehearsed against the website and the Hugging Face export before publishing. Unknown prices stay empty, never zero. Feedback welcome: which providers or pricing modes batch, caching, long context should we cover next, and is a price you rely on missing or wrong?