OpenAI text-embedding-3-large

First benchmark establishes baseline for text-embedding-3-large

OpenAI's text-embedding-3-large enters benchmarking with strong performance across multiple evaluation domains. The model demonstrates particular strength in retrieval tasks, achieving 54.90 on NDCG@10 and 49.40 on the MIRACL benchmark, indicating robust multilingual retrieval capabilities. Classification performance stands at 71.15, while clustering reaches 47.80, showing balanced competency across different embedding use cases. The model produces 3072-dimensional embeddings with a context window of 8191 tokens, providing substantial capacity for processing longer documents. Reranking capabilities score at 59.36, positioning this as a versatile embedding model suitable for various semantic search and information retrieval applications. The STS (Semantic Textual Similarity) score of 53.26 reflects solid performance in understanding nuanced semantic relationships. As a large-scale embedding model, it appears designed for production environments requiring high-quality vector representations across diverse languages and tasks. Users should note this baseline establishes the expected performance envelope, with future benchmarks tracking consistency and any performance shifts over time.