These models search document pages as images, taking tables, diagrams and layout into account without an OCR step first. They suit archives of scans, technical documentation and slide decks. Check quality on documents in your languages, index size and GPU requirements.
Tencent models based on Qwen3.5 for searching scans and PDFs as images. According to the model card, among the top of the ViDoRe leaderboard at release.
Strong multilingual rerankers; m0 also ranks pages as images (scans, slides). The latest versions are open for non-commercial use only.
A small 1B reranker from NVIDIA. The vl version also takes document pages as images, not just text. The card states multilingual support without listing the languages.
A compact (250M) model for searching document pages as images. According to the authors, it matches models 10 times larger and runs without a GPU.
Searches PDFs and scans as images: pages do not need to be OCR'd first, the model finds the right one for a question directly, including tables and charts. Trained on English.
Search across PDF pages and scans as images. The cards list English, Italian, French, German and Spanish — Russian is not among them.
A small model for searching document pages as images, from the team behind a popular RAG framework. The card lists English, Italian, French, German and Spanish.
One vector for text, for an image and for a text-image pair: a single model can find a product by photo, a document page by question and an image by description. The card lists English and Chinese.
Turns an image-plus-text model into an embedding model: one vector for a page, a diagram or a captioned photo. The card states English.
A reranker for document pages as images: after a visual search it reorders the found pages by how well they answer the question. The card does not state the languages.
Searches page screenshots: the page is not OCRed but turned into a single vector, so the index is more compact than with late-interaction models. The card lists English and French.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
