OpenAI's smaller text-embedding-3 model, which OpenAI calls an improved, more performant version of its ada embedding model. It returns 1536-dimension vectors by default and costs less than its sibling text-embedding-3-large.
Pick it if
you want the hosted embedding model every Octomind plan includes, Free too
you want embeddings without loading model weights into your machine's memory
you want shorter vectors via the dimensions parameter to shrink your index
Skip it if
a single input runs past 8,192 tokens, the per-input limit
Jina v5 Text Nano is the smaller of Jina AI's two v5 text embedding models, a 239M-parameter multilingual model built on EuroBERT-210M. It costs less than jina-embeddings-v5-text-small and reads 8K tokens against Small's 32K.
Pick it if
you want the cheapest Jina v5 text embeddings for multilingual search
Jina's reported English MTEB 71.0, near v5-text-small's 71.7, is enough
you want 768-dimension vectors that truncate down to 32 dimensions
Skip it if
your documents exceed 8K tokens; v5-text-small reads 32K
you embed images, audio or video; this model reads text only
FAQ
Which is cheaper, OpenAI Embedding 3 Small or Jina v5 Text Nano?
OpenAI Embedding 3 Small and Jina v5 Text Nano cost the same, $0.02/M tokens.
Which handles longer documents?
Both take up to 8k tokens per text.
Can I switch from OpenAI Embedding 3 Small to Jina v5 Text Nano without re-indexing?
No. Vectors from different embedding models are not comparable, so a switch means re-embedding the whole corpus.
Try either model
Switch between models inside the same Octomind session.