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Efficient embedding model with improved performance and reduced costs.

API for


text-embedding-3-small API enhances text representation, offering better accuracy and cost-efficiency compared to its predecessor, text-embedding-ada-002.


Model Overview Card: text-embedding-3-small

Basic Information

  • Model Name: text-embedding-3-small
  • Developer/Creator: OpenAI
  • Release Date: January 25, 2024
  • Version: text-embedding-3-small
  • Model Type: Text Embedding


  • 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.

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