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Get all the details on text-embedding-3-large, an AI model from OpenAI. This page covers its token limits, pricing structure, key capabilities such as embeddings, multilingual_embeddings, available API code samples, and performance strengths.
Key Metrics
Input Limit
No data tokens
Output Limit
No data tokens
Input Cost
$0.13/1M
Output Cost
N/A/1M
Sample API Code
from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
model="text-embedding-3-large",
input="The quick brown fox jumps over the lazy dog",
encoding_format="float"
)
print(response.data[0].embedding)Required Libraries
openai
openai
Notes
Most capable embedding model for both English and non-English tasks. 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
multilingual embeddings
Supported Data Types
Input Types
text
Output Types
embedding
Strengths & Weaknesses
Exceptional at
multilingual embeddings
high performance embedding
Good at
search
clustering
recommendations
anomaly detection
classification
Additional Information
Latest Update
Jan 25, 2024
Knowledge Cutoff
No data