Perplexity releases pplx-embed-v2-late, two open MIT-licensed embedding models with 0.6 and 9 billion parameters that handle text, images and document pages
Prepared by Sintelligent with the help of SI. Published in SI: . Updated: .
Open document-retrieval models that also handle images give developers a ready-made tool that is not tied to a closed service.
Perplexity has published two open "late-interaction" (ColBERT-style) retrieval models built on Qwen3.5 under the MIT license: a compact one with 0.6 billion parameters and a large one with 9 billion. They share a common vector space, so an index built with the large model can be queried with the small one. The models work with text, images and document pages without optical character recognition (OCR). According to Perplexity itself, the 9-billion-parameter model scored 92.4% on the MADQA test (500 questions over about 18 thousand PDF pages), 65.2% on ViDoRe v3 and 74.8% Recall@1000 over about 190 million documents.