Jina v5 Text Small is Jina AI's 677M-parameter multilingual text embedding model, built on Qwen3-0.6B-Base and distilled from Qwen3-Embedding-4B. It is the larger of the two v5 text models, above v5-text-nano; v5-omni-small is the multimodal counterpart.
Pick it if
you embed long documents, up to 32K tokens per input
you need multilingual retrieval with vectors truncatable from 1024 down to 32
you want open weights to self-host for non-commercial work
Skip it if
you need image input; v5-omni-small covers multimodal at the same price
short inputs suffice and v5-text-nano costs less per token
Jina v5 Omni Small is Jina AI's multimodal embedding model for text, images, video, audio and PDF in one vector space. It extends jina-embeddings-v5-text-small, returns matching text vectors at the same price, and has a cheaper sibling, omni-nano.
Pick it if
you search images, video, audio or PDFs alongside text in one index
you already index with v5-text-small; its text vectors match
you want 1024-dimension vectors that truncate down to 32
Skip it if
you embed only text; v5-text-small gives matching vectors
short inputs fit omni-nano, which costs less per token
FAQ
Which is cheaper, Jina v5 Text Small or Jina v5 Omni Small?
Jina v5 Text Small and Jina v5 Omni Small cost the same, $0.05/M tokens.
Which handles longer documents?
Both take up to 32k tokens per text.
Can I switch from Jina v5 Text Small to Jina v5 Omni Small 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.