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Get all the details on text-embedding-3-small, an AI model from OpenAI. This page covers its token limits, pricing structure, key capabilities such as embeddings, available API code samples, and performance strengths.
Key Metrics
Input Limit
No data tokens
Output Limit
No data tokens
Input Cost
$0.02/1M
Output Cost
N/A/1M
Sample API Code
from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
model="text-embedding-3-small",
input="The quick brown fox jumped over the lazy dogs.",
encoding_format="float"
)
print(response.data[0].embedding)Required Libraries
openai
openai
Notes
text-embedding-3-small is our improved, more performant version of our 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.
Capabilities
embeddings
Supported Data Types
Input Types
text
Output Types
embedding
Strengths & Weaknesses
Exceptional at
generating embeddings
measuring text relatedness
Good at
search
clustering
recommendations
anomaly detection
classification tasks
Additional Information
Latest Update
Jan 25, 2024
Knowledge Cutoff
No data