Name: Text Embedding 3 Small API
Brand: OpenAI

Overview

text-embedding-3-small Description

Basic Information

Model Name: text-embedding-3-small
Developer/Creator: OpenAI
Release Date: January 25, 2024
Version: text-embedding-3-small
Model Type: Text Embedding

Overview:text-embedding-3-small is an efficient and compact embedding model designed to enhance performance over its predecessor, text-embedding-ada-002. It transforms text into numerical representations that can be easily processed by machine learning models.
Key Features:
- Improved Performance: Achieves higher scores on benchmarks for multi-language retrieval (MIRACL) and English tasks (MTEB).
- Cost Efficiency: Offers a 5x reduction in cost compared to text-embedding-ada-002.
- Compact Size: Embedding size of 512 dimensions, suitable for memory and storage-constrained environments.
Intended Use:
- Search: Enhance search algorithms by ranking results based on relevance.
- Clustering: Group similar text strings for data analysis.
- Recommendations: Suggest related items based on text similarity.
- Anomaly Detection: Identify outliers in data.
- Diversity Measurement: Analyze the diversity of text data.
- Classification: Classify text strings by their most similar labels.
Language Support:Supports multiple languages, improving accessibility and usability across diverse linguistic datasets.

Technical Details

Architecture:Utilizes a transformer-based architecture optimized for efficiency and performance.
Training Data:Trained on a diverse set of text sources to capture a wide range of linguistic patterns and semantics.
Data Source and Size:Extensive dataset comprising millions of text documents, ensuring a broad understanding of language.
Diversity and Bias:Training data is selected to minimize bias and ensure robust performance across different demographics and use cases.

Performance Metrics

Comparison to Other Models:
- MIRACL Score: Improved from 31.4% (ada-002) to 44.0%.
- MTEB Score: Increased from 61.0% (ada-002) to 62.3%.
Accuracy:Demonstrates higher accuracy in both multi-language and English-specific benchmarks.
Speed:More efficient than previous models, reducing latency and computational requirements.
Robustness:Handles diverse inputs effectively, ensuring reliable performance across various applications.

Uptime and performance

Uptime (24h): 67.0%
Uptime (7d): 95.0%
Availability (24h): 36.0%
Error rate (24h): 64.0%
End-to-end latency (24h median): 227ms

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

Semantic search & retrieval

Embed documents and queries to build content search systems across languages (web, enterprise, knowledge bases).

Clustering & recommendation

Generate vector representations for content classification, similarity analysis, and recommendation systems.

Anomaly detection

Use embedding distances to flag outliers in text data—useful in logging, moderation, or compliance monitoring.

RAG pipelines

Combine dense embeddings with vector databases for retrieval-augmented workflows in chatbots, summarization, or QA systems.

Multilingual vector applications

Implement multilingual chatbots, classification, search and recommendations benefiting from cross-language embeddings.

‍

How to use Text Embedding 3 Small API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to openai/text-embedding-3-small.

import requests

r = requests.post(
    "https://api.aimlapi.com/v1/embeddings",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "openai/text-embedding-3-small",
      "input": "The quick brown fox"
    },
)
print(r.json())

const r = await fetch("https://api.aimlapi.com/v1/embeddings", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "openai/text-embedding-3-small",
    "input": "The quick brown fox"
  }),
});
console.log(await r.json());

curl -X POST https://api.aimlapi.com/v1/embeddings \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/text-embedding-3-small","input":"The quick brown fox"}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.