Text Embedding 3 Small via the Tokenator API
What the model is for
text-embedding-3-small is OpenAI's improved, more performant version of the ada embedding model. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.
Specification
| Full name | Text Embedding 3 Small |
| API ID | text-embedding-3-small |
| Model vendor | OpenAI |
| Type | Embeddings (vector representations) |
| Context window | 8.2K tokens |
| Tokenator usage multiplier | 1× |
| Input modalities | text |
| Supported API formats | /v1/embeddings |
| Upstream providers | 1 |
| Current status | available |
| Data updated | 2026-09-15 |
Upstream providers
| Provider | Multiplier |
|---|---|
| Unified LLM API | 1× |
A request goes to the first available provider by priority; if it fails, Tokenator switches to the next one.
Example request
curl https://api.tokenator.top/v1/embeddings \ -H "Authorization: Bearer sk-your-tokenator-key" \ -H "Content-Type: application/json" \ -d '{ "model": "text-embedding-3-small", "input": "Hello" }'
See also
FAQ about Text Embedding 3 Small
What is the API ID of Text Embedding 3 Small?
text-embedding-3-small — put this into the model field of your request.
What context window does Text Embedding 3 Small have?
8.2K tokens.
How many tokens will a request cost?
input × 1× — embeddings have no output, so you pay for the input text only. See the documentation for details.
How do I call this model?
Through /v1/embeddings only: the chat endpoints refuse such a model. Parameters and examples are in the embeddings documentation.