Voyage 4 is the middle tier of Voyage AI's Voyage 4 embedding series, for general-purpose and multilingual retrieval. It sits between voyage-4-lite and voyage-4-large, and embeddings from all three can be used interchangeably.
Pick it if
you want half voyage-4-large's price; Voyage reports large 1.87% ahead on average
you need multilingual retrieval or 256-2048 dimension embeddings
you may mix it with other Voyage 4 models in one index
Skip it if
voyage-4-lite is accurate enough at a third of the price
you are indexing source code; voyage-code-4 is optimized for code retrieval
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, Voyage 4 or Jina v5 Text Small?
Jina v5 Text Small is cheaper: $0.05/M against $0.06/M.
Which handles longer documents?
Both take up to 32k tokens per text.
Can I switch from Voyage 4 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.