OpenAIFastHigh

Text Embedding 3 Small

OpenAI's cost-efficient text embedding model. Generates vector representations for semantic search, clustering, and similarity tasks at very low cost.

37/0credits

input / output · per 1M tokens

Cost-efficient text embeddings

8,191 token max input per item

Default output dimensions: 1,536

dimensions parameter can shorten embeddings

Optimized for semantic search, clustering, classification, and RAG

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Test this model instantly in the Console Playground — no code required

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llms.txtllms.txt ↗

Model Specifications

Context Window

tokens

Max Output

tokens

Training Cutoff

n/a

Compatible SDK

OpenAI

Capabilities

Vision

Function Calling

Streaming

JSON Mode

System Prompt

Token Pricing (per 1M tokens)

Token Type	Credits	USD Equivalent
Input Tokens	37	$0.02
Output Tokens	0	$0.00

* 1,500 credits ≈ $1 (actual charges may vary based on usage)

Quick Start

curl -X POST "https://api.core.today/llm/openai/v1/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "text-embedding-3-small",
  "input": "What is the meaning of life?"
}'

Parameters

Parameter	Type	Required	Default	Description
`input`	string \| array	Yes	-	Text or array of texts to embed. Max 8,191 input tokens per item.
`model`	string	Yes	text-embedding-3-small	Model identifier.
`dimensions`	integer	No	1536	Output embedding dimensionality. Defaults to 1,536; can be reduced to shrink storage while preserving semantic quality.
`encoding_format`	string	No	float	Format of the returned embeddings: float or base64. floatbase64

Examples

Text Embedding

Generate embeddings for semantic search

curl -X POST "https://api.core.today/llm/openai/v1/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "text-embedding-3-small",
  "input": "What is the meaning of life?"
}'