text-embedding-3-large
text-embedding-3-large produces high-dimensional text embeddings for semantic search, clustering, and classification.
Provider
OpenAI
Model family
OpenAI Embeddings
Embedding model
Cost tier
Large
Status
Current
Why teams choose it
Broad capability envelope
Useful when the same stack must cover chat, multimodal inputs, tooling, or structured-output shapes without juggling many SKUs.
Long-context analysis
Helps teams summarize, compare, and extract insights from long documents without losing important nuance.
Coding and tools
Works well for code assistance, tool calling, and agent workflows where instructions must stay consistent across steps.
Cost-efficient routing
Useful as part of a routing stack where cheap models handle drafts and confirmations and this tier handles genuinely hard passages.
Tradeoffs to know
- Not generative—pair with an LLM for answers.
- Cost at billion-token scale needs caching and batching.
Technical specs
- Inputs
- text
- Outputs
- vector
- Capabilities
- semantic search, deduplication, classification
- License
- See vendor
Benchmarks
No independent or vendor benchmark scores in this catalog entry.
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text-embedding-3-large FAQ
What is text-embedding-3-large?
text-embedding-3-large produces high-dimensional text embeddings for semantic search, clustering, and classification. Teams pair it with pgvector or SaaS vector DBs for RAG; output dimensions can be reduced with tradeoffs described in OpenAI documentation.
When does text-embedding-3-large fit best?
Enterprise RAG retrieval layers
What should teams watch out for with text-embedding-3-large?
Not generative—pair with an LLM for answers.
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