text-embedding-3-large produces 3072-dimensional vectors with the highest MTEB and MIRACL scores in the text-embedding-3 family, with built-in Matryoshka dimension reduction for flexible quality-storage tradeoffs in production retrieval systems.
import { embed } from 'ai';
const result = await embed({ model: 'openai/text-embedding-3-large', value: 'Sunny day at the beach',})OpenAI released text-embedding-3-large on January 25, 2024 as the accuracy-maximizing option in the third-generation embedding family.
The MTEB (Massive Text Embedding Benchmark) score tells the broadest story. At 64.6%, text-embedding-3-large spans retrieval, classification, clustering, and semantic similarity, outperforming its predecessor ada-002 by 3.6 points. But the multilingual gap deserves close attention. On MIRACL, the standard cross-language retrieval benchmark, the score jumps from ada-002's 31.4% to 54.9%. That 23.5-point improvement is not incremental. It's the difference between a multilingual search system that frustrates users and one that works.
The model uses Matryoshka Representation Learning, a technique that front-loads the most important semantic information into the earliest vector dimensions. The practical consequence: you can request 256 dimensions and still outperform a full 1,536-dimension ada-002 embedding. This turns vector storage and memory from fixed infrastructure costs into tunable parameters. Teams managing indexes with hundreds of millions of documents gain a lever that directly affects their infrastructure bill.
At native 3,072 dimensions, the vectors capture the finest semantic distinctions the model can represent. Reducing dimensions trades some granularity for smaller index sizes, faster nearest-neighbor lookups, and lower memory consumption. The right setting depends on your corpus and application. A legal document search engine and a product recommendation system have very different tolerances for recall degradation.