Text Embedding 004 is an embeddings model that generates vector representations of text to capture semantic meaning and context. It excels in tasks such as semantic search, text classification, and clustering, making it a versatile tool for understanding text relationships.

Some noteworthy use cases of Text Embedding 004 include:

• Semantic search for relevant content
• Text classification to categorize documents
• Clustering similar texts together

Metric	Value
Parameter Count	Unknown
Mixture of Experts	No
Context Length	Unknown
Multilingual	Yes
Quantized*	Unknown

*Quantization is specific to the inference provider and may vary.

Note: While Text Embedding 004 supports multiple languages, there have been reports of identical vector outputs for certain languages, which may affect its performance in those cases.