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Introducing text-embedding-3-largeOpenAIReleased January 25, 2024

text-embedding-3-large

OpenAI’s most capable embedding model, providing 3072-dimensional vector representations for high-precision semantic search.

embeddingsProprietary API$0.13 / 1M tok (3072 dims)Context: 8.191K (8,191 tokens)
Official Model Card & DocsTry in AI Studio / PlaygroundGitHub Repository

Technical Specifications

Architecture Type
High-Capacity Matryoshka Representation Transformer
Total Parameters
High-Capacity Dense
Context Window
8.191K (8,191 tokens)
Max Output Tokens
3.072K (3,072 tokens)
Knowledge Cutoff
September 2021
Supported Modalities
text
License & Access
OpenAI Business Terms

Benchmark Evaluations

Mteb
64.6
Miracl
54.9

Deep Architectural Overview

text-embedding-3-large delivers state-of-the-art multilingual text retrieval, clustering, and recommendation embeddings. It scales from 256 to 3072 dimensions, optimizing precision on technical, legal, and multi-domain datasets.

Strengths & Considerations

Core Strengths
  • Highest MTEB retrieval accuracy in OpenAI catalog
  • 3072-dimensional semantic density
  • Flexible truncation to 256/1024 dimensions
Known Limitations
  • Higher vector database storage requirements than small model

Token & API Pricing

Input Tokens (1M)$0.13
Output Tokens (1M)Contact
Pricing is verified directly against OpenAI's developer documentation and API rate sheets.

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