What factors cause unexpected spikes in OpenAI API pricing and how can I cache responses effectively to keep our monthly invoice low?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What factors cause unexpected spikes in OpenAI API pricing and how can I cache responses effectively to keep our monthly invoice low?
1 real search · 1 real-search pass
OpenAI API pricing official input output cached input token rates prompt caching documentation usage dashboard rate limitsGoogle ↗
OBSERVEDOther / navigational
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
OpenAI API pricing official input output cached input token rates prompt caching documentation usage dashboard rate limits Google ↗
- Prompt caching · developers.openai.com
- Prompt caching · developers.openai.com
- Prompt caching · developers.openai.com
- OpenAI Usage Dashboard · platform.openai.com
- Reviewing API usage and costs · help.openai.com
- OpenAI API pricing · developers.openai.com