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
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
FAQ
Which is cheaper, Jina v5 Text Nano or Jina v5 Text Small?
Jina v5 Text Nano is cheaper: $0.02/M against $0.05/M.
Which handles longer documents?
Jina v5 Text Small takes longer inputs (32k tokens against 8k tokens), so it needs less chunking.
Can I switch from Jina v5 Text Nano to Jina v5 Text 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.