OpenAI Text Embedding 3 Large
Text Embedding 3 Large produces high-quality text embeddings for search, clustering, and recommendations.
What is Text Embedding 3 Large?
Text Embedding 3 Large from OpenAI encodes text into dense vectors for retrieval and analytics. Use it for RAG pipelines, semantic search, recommendations, and topic detection across languages. Optimized for quality and latency so it scales to large corpora.
Technical Specifications
8,191 tokens
3072-dimensional vector
2024
Active
Capabilities
Benchmark Scores
Pros & Cons
Pros
- Strong retrieval quality
- Fast inference and small vectors
- Works across languages
Cons
- Not a generative model
- Needs good chunking to avoid drift
- Quality depends on downstream index settings
Features
Semantic search
Encode queries and documents into the same vector space.
Multi-task
Use one embedding for search, recommendations, and clustering.
Scalable
Low latency and small vectors for large corpora.
Use Cases
RAG indexing
Embed knowledge bases for accurate retrieval-augmented generation.
Recommendations
Cluster similar items and surface relevant content.
Analytics
Detect topics, intent, and anomalies across text streams.