How should I estimate token usage and pricing for the new OpenAI embeddings model when indexing our company's internal documentation?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How should I estimate token usage and pricing for the new OpenAI embeddings model when indexing our company's internal documentation?
2 real searches · 1 real-search pass
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 (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
OpenAI embeddings API models pricing per million tokens text-embedding-3-small text-embedding-3-large 2026 Google ↗
OpenAI API embeddings guide token limits dimensions usage tokenization documentation Google ↗
- Small model · developers.openai.com
- Large model · developers.openai.com
- API pricing · developers.openai.com
- Create embeddings · developers.openai.com
- Vector embeddings · developers.openai.com
- text-embedding-3-small Model · developers.openai.com
- Understanding and counting tokens · help.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI embeddings API models pricing per million tokens text-embedding-3-small text-embedding-3-large 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
OpenAI API embeddings guide token limits dimensions usage tokenization documentation OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |