Perplexity’s new contextual embedding model claims top spot in document retrieval

Perplexity AI has a new contextual embedding model, pplx-embed-v2-context-9b-preview, and the company says it sets a new state-of-the-art for document retrieval. The trick is simple to describe and hard to pull off. The model encodes an entire document once, so every piece of it carries the context of the whole. What the new model actually does The problem is chunking. Long documents get sliced into smaller pieces before they are embedded, and those pieces often lose the plot. A chunk that says “the company raised its forecast” is useless if the model has forgotten which company the document was about. Traditional approaches embed each chunk in isolation, like reading one page torn out of a novel. Perplexity’s contextual approach tries to fix that. By processing the full document in one pass, the model lets each chunk inherit information from the surrounding text. The model carries a “preview” tag, which signals it is an early release rather than a finished product. Perplexity is positioning it as the next step after its first generation of contextual models. The v1 benchmark bar it has to clear On February 26, 2026, the company released two embedding families: pplx-embed-v1 and pp...