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Get all the details on Text Embedding 004, an AI model from Google. This page covers its token limits, pricing structure, key capabilities such as embedding, available API code samples, and performance strengths.
Key Metrics
Input Limit
3.1K tokens
Output Limit
No data tokens
Input Cost
N/A/1M
Output Cost
N/A/1M
Sample API Code
import google.generativeai as genai
genai.configure(api_key='YOUR_API_KEY')
def embed_text(text):
model = genai.GenerativeModel('text-embedding-004')
embedding = model.embed_content(text=text)
return embedding
text_to_embed = "Hello, world!"
embedding_vector = embed_text(text_to_embed)
print(embedding_vector)Required Libraries
google-generative-ai
@google/generative-ai
Notes
State-of-the-art text embedding model.
Capabilities
embedding
Supported Data Types
Input Types
text
Output Types
embedding
Strengths & Weaknesses
Good at
semantic similarity
text representation
Additional Information
Latest Update
May 13, 2025
Knowledge Cutoff
No data