Embeddings represent text meaning as numerical vectors, enabling searches based on concepts rather than exact keywords.
text-embedding-3-small offers cost-efficient retrieval, while text-embedding-3-large provides stronger overall and multilingual performance.
Changing embedding models requires re-embedding indexed content and recalibrating similarity thresholds for reliable retrieval.
OpenAI embeddings turn text into a long set of numbers that capture the meaning of that text. A sentence, a question, a product description, or a document can all receive a numerical representation. Text with a similar meaning tends to produce vectors that sit close to each other in a mathematical space. This idea forms the base of semantic search, document retrieval, recommendations, clustering, and several other AI tasks.
Traditional keyword search looks for matching words. Semantic search takes a different path. A search for 'how to reset a forgotten password' can find a document that says 'steps for recovering account access,' even though the two texts share few exact words. The system compares meaning rather than simple word matches.
Keyword search works well when the exact terms matter, yet it can miss useful information when different words express the same idea. Semantic search can solve that gap. A business knowledge base, for example, may contain thousands of support documents. A customer query can turn into a vector, and the search system can compare that vector with vectors for stored document sections.
The closest matches can then reach a language model as useful context. This approach forms a common pattern for retrieval-augmented generation, often called RAG. OpenAI states that embeddings support knowledge retrieval in ChatGPT and other retrieval tools.
OpenAI currently lists two main embedding models: text-embedding-3-small and text-embedding-3-large. The company describes text-embedding-3-small as a small, efficient model, while text-embedding-3-large holds the position as the most capable embedding model. Both models support semantic relatedness tasks, search, clustering, recommendations, anomaly detection, and classification.
The price difference is significant. text-embedding-3-small costs $0.02 per 1 million input tokens. text-embedding-3-large costs $0.13 per 1 million input tokens. At 100 million tokens, that means about $2 for the small model and $13 for the large model, before vector database and infrastructure costs.
Also Read - Is ChatGPT Safe for Assignments? Hidden Privacy Risks
OpenAI’s published launch results give useful benchmark figures. text-embedding-3-small scored 62.3 on MTEB and 44.0 on MIRACL. text-embedding-3-large scored 64.6 on MTEB and 54.9 on MIRACL. The older text-embedding-ada-002 scored 61.0 on MTEB and 31.4 on MIRACL in the same published comparison.
These figures show a clear advantage for the newer models, especially on multilingual retrieval. The MIRACL result for text-embedding-3-large stands out with a score of 54.9, compared with 44.0 for text-embedding-3-small.
text-embedding-3-small produces vectors with up to 1,536 dimensions. text-embedding-3-large supports up to 3,072 dimensions. OpenAI also added a dimensions option for the newer model family. This option lets a system request shorter vectors when lower storage use or faster vector search matters.
A smaller vector can reduce database storage and search costs. A larger vector can preserve more information about text meaning. The best choice depends on the retrieval task, database size, response targets, and quality tests.
A semantic search system starts with source material such as manuals, reports, product pages, or support articles. Large documents usually need smaller sections, often called chunks. Each chunk receives a vector representation. A search question receives another vector. A vector database then compares the question vector with stored vectors and returns the closest matches.
The language model can use those retrieved sections as context for a final answer. This design separates search from answer creation. The vector model finds relevant material, while the language model handles the final response.
Older systems may still use text-embedding-ada-002, which OpenAI now labels as an older embedding model. A model change needs careful evaluation. Vectors from different models do not form a simple drop-in replacement for one another. Existing source text may need fresh vector creation so the entire search index uses a consistent model.
Similarity scores also need fresh calibration after a model change. OpenAI community reports note that cosine similarity values can shift between models, so an old relevance threshold may not work well with text-embedding-3-small.
Also Read - Why Large Language Models Can't Always Solve Math Problems
text-embedding-3-small makes a strong choice for cost-sensitive search systems, large document collections, and many general retrieval tasks. Its $0.02 per million tokens price makes large-scale text processing relatively inexpensive.
text-embedding-3-large suits cases where retrieval quality matters more than raw embedding cost, especially for multilingual search. Its $0.13 per million tokens, 3,072 maximum dimensions, and stronger published MIRACL result give it a clear place in higher-quality retrieval systems.
The central lesson remains simple: embeddings turn meaning into numbers, semantic search compares those numbers, and retrieval brings the most relevant text closer to the final AI answer.
1. What are OpenAI embeddings?
They are numerical representations of text meaning that allow systems to compare semantic similarity between texts.
2. What is semantic search?
Semantic search retrieves information based on meaning and intent rather than relying only on matching keywords.
3. Which OpenAI embedding model should I choose?
Choose text-embedding-3-small for cost-efficient large-scale retrieval and text-embedding-3-large when higher retrieval quality is a priority.
4. Why are vector dimensions important?
Higher dimensions can preserve more information but may increase storage and search costs. Shorter vectors can improve efficiency.
5. Can I switch from an older embedding model easily?
Not directly. Existing content generally needs to be re-embedded consistently with the new model, followed by fresh relevance testing and threshold calibration.