Text Embedding 3 Large (256d)
Text Embedding 3 Large (256d) is OpenAI's third-generation large embedding model with its output dimensionality truncated to 256 dimensions. OpenAI's embedding v3 models natively support dimension reduction via a technique that preserves representation quality even at lower dimensions, making 256d a practical configuration for storage-constrained or latency-sensitive deployments.
At 256 dimensions, this variant offers a significant reduction in vector storage and retrieval cost compared to the full 3072-dimension output, while retaining substantially better semantic quality than older smaller models like Ada-002. It is well-suited for semantic search, document clustering, and retrieval-augmented generation pipelines where index size and query speed are concerns.
Key Features
256-dimension output via OpenAI's native Matryoshka truncation technique
Strong semantic quality retention despite reduced dimensionality
Significantly lower storage and index cost vs. full 3072-dimension variant
Suitable for large-scale vector search with reduced query latency
Multilingual text representation across major languages
Compatible with standard vector databases and ANN search libraries
Ideal Use Cases
Semantic search over large document corpora with reduced index size
Retrieval-augmented generation (RAG) with cost-optimized vector stores
Document clustering and topic modeling at scale
Duplicate and near-duplicate content detection
Recommendation systems requiring fast approximate nearest-neighbor search
Example Prompts for Text Embedding 3 Large (256d)
Technical Specifications
| Provider | OpenAI |
| Category | Embedding |
| Modality | Text -> Vector |
| Max Output | 256 dimensions |
Frequently Asked Questions
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