how accurate are the online openai token counters when estimating embedding costs for a semantic search engine using pgvector
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
how accurate are the online openai token counters when estimating embedding costs for a semantic search engine using pgvector
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.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
OpenAI embeddings API token counting tokenizer cl100k_base text-embedding-3-small documentation pricing 2026 Google ↗
OpenAI cookbook embedding token count tiktoken embeddings API usage response usage prompt_tokens Google ↗
- How can I tell how many tokens a string will have before I try to embed it? · help.openai.com
- Vector embeddings · developers.openai.com
- text-embedding-3-small Model · developers.openai.com
- Create embeddings · developers.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI embeddings API token counting tokenizer cl100k_base text-embedding-3-small documentation pricing 2026 OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
OpenAI cookbook embedding token count tiktoken embeddings API usage response usage prompt_tokens OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |