text-embedding-3-large — reviews, specs & pricing
High-quality text embeddings for retrieval and semantic search.
Summary
Text-embedding-3-large is OpenAI's flagship embedding model, engineered to provide highly accurate vector representations for complex semantic search and retrieval tasks. It supports up to 3,072 dimensions and features native support for shortening embeddings, allowing developers to balance storage costs and search latency without losing significant performance. This model offers substantial improvements in multilingual capabilities and conceptual understanding compared to its predecessors.
Sample use case
An enterprise e-commerce platform can implement text-embedding-3-large to power its global semantic search engine. When a customer types a conversational query like 'lightweight waterproof jacket for summer hiking', the platform converts both the query and the product catalog into dense vector representations. Because the model supports flexible dimensions, the engineering team can truncate the vectors to 1,024 dimensions to optimize search speeds and reduce vector database costs while still delivering highly accurate, localized results.
Specifications
- Provider: openai
- License: closed
Pros
- Exceptional semantic retrieval and multilingual accuracy
- Supports flexible dimensionality reduction to optimize storage
- Generous context window of 8,191 input tokens
- Cost-effective pricing compared to legacy embedding models
Cons
- Higher cost and latency than text-embedding-3-small
- Large 3,072-dimension vectors increase storage requirements if untruncated
- Closed-source API reliance prevents offline or local deployment
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