By NeoTeo
Publication Date: 2026-10-08 23:37:00
On October 7, 2026, Perplexity announced pplx-embed-v2-late, a family of multimodal retrieval models in 0.6B and 9B sizes. Perplexity said both were publicly available on Hugging Face. The models search text and images by comparing individual tokens, rather than compressing each input into a single vector.
How late interaction and MaxSim work
An embedding turns an input into numerical vectors that a retrieval system can compare. Many systems represent a whole input with one vector. The late-interaction approach in pplx-embed-v2-late keeps multiple vectors, one for each token, with 128 dimensions per vector.
When a query is matched against a document, each query token finds its strongest match among the document tokens. MaxSim combines those strongest matches into a score for the query-document pair. This preserves token-level matches through scoring, at the cost of storing more vectors per document and doing more work to score candidates than with dense, single-vector retrieval. Perplexity says those costs depend on document length and deployment choices.
A 0.6B model can query an index built with 9B
Perplexity says the two variants share an embedding space. That means a system can use the 9B model to encode documents for an index, then use the 0.6B model to encode search queries against that index. Perplexity says using the smaller model for query encoding can reduce…
