Text Embedding 3 Small overview
Text Embedding 3 Small specs
- Model ID
openai/text-embedding-3-small- Developer
- OpenAI
- Input
- Text
- Output
- Text
- Endpoint
POST https://api.aimlapi.com/v1/embeddings
Basic Information
- Model Name: text-embedding-3-small
- Release Date: January 25, 2024
- Version: text-embedding-3-small
- 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.
Use cases
Embed documents and queries to build content search systems across languages (web, enterprise, knowledge bases).
Generate vector representations for content classification, similarity analysis, and recommendation systems.
Use embedding distances to flag outliers in text data, useful in logging, moderation, or compliance monitoring.
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.
Text Embedding 3 Small API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $0.02 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
How to use the Text Embedding 3 Small API
View docs
Build with your agent
Give your agent the model details, code and documentation.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl '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 jumps over the lazy dog."
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "openai/text-embedding-3-small",
"input": "The quick brown fox jumps over the lazy dog."
}
response = requests.post("https://api.aimlapi.com/v1/embeddings", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = 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 jumps over the lazy dog."
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to Text Embedding 3 Small
Text Embedding 3 Small FAQ
What inputs and outputs does Text Embedding 3 Small support?
Text Embedding 3 Small takes text as input and returns embedding.
When was Text Embedding 3 Small released?
Text Embedding 3 Small became available on December 12, 2025.
How much does Text Embedding 3 Small cost?
Text Embedding 3 Small costs $0.02 per 1M input tokens on AI/ML API, billed as you go with no subscription.
Who created Text Embedding 3 Small?
Text Embedding 3 Small was built by OpenAI.
How do I call Text Embedding 3 Small on AI/ML API?
Send a request to https://api.aimlapi.com/v1/embeddings with openai/text-embedding-3-small as the model id.
Is Text Embedding 3 Small available through a single API key?
Yes. Text Embedding 3 Small is served through AI/ML API, so the same key and endpoint format used for other models applies.
What is Text Embedding 3 Small designed for?
It is OpenAI's embedding model built for semantic search and similarity tasks, offered at a lower cost.
What is the Text Embedding 3 Small model ID?
Use openai/text-embedding-3-small as the model parameter. Requests go to https://api.aimlapi.com/v1/embeddings with your AI/ML API key.