Text Embedding 3 Small

OpenAI
Embeddings

OpenAI's compact embedding model. 1536 dimensions, great for semantic search and RAG.

Examples

See what Text Embedding 3 Small can generate

Semantic Search

Input:

"How to deploy a Node.js app to production"

Similar matches:

Deploying Node applications to cloud servers

94%

Setting up a Node.js production environment

89%

Node.js deployment best practices and CI/CD

85%

Document Clustering

Input:

"Machine learning model training techniques"

Similar matches:

Deep learning optimization and hyperparameter tuning

91%

Neural network training strategies for beginners

87%

Supervised learning algorithms and model evaluation

83%
Try Text Embedding 3 Small

Vector output (1536 dimensions):

-0.07190.37400.4652-0.1428-0.39820.3542-0.39760.2898-0.44960.4993-0.2908-0.29790.38090.1991-0.9900-0.81630.79100.99230.41040.0821-0.43380.32900.62160.0844... 1512 more
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Pricing

Price per Generation
Per generationFree

API Integration

Use our OpenAI-compatible API to integrate Text Embedding 3 Small into your application.

Install
npm install railwail
JavaScript / TypeScript
import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

const vectors = await rw.run("text-embedding-3-small", "Hello world", { type: "embed" });
console.log(vectors[0].length); // embedding dimensions

// Or use the embed() method for full control
const res = await rw.embed("text-embedding-3-small", ["Hello", "World"]);
for (const item of res.data) {
  console.log(item.embedding.length);
}
Specifications
Avg. latency
500ms
Provider
OpenAI
Category
Embeddings
Tags
affordable
compact
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