Jina AI

Our Birds of a Feather session on embeddings, rerankers & small language models for better search went off — full house at EMNLP 2026! Shoutout to everyone who made it. Enjoy the rest of the conf in Budapest! docs.google.com/presentation/d…

We are hosting the 4th BoF on embeddings, rerankers & small language models for search at EMNLP2026 in Budapest. We welcome talks on Jev-like decision models this year. Submit your work here:
jina.ai/news/emnlp2026…

Announcing jina-ocr-v1, our new visual document parser with 3.4B total parameters and 570M active parameters, with speculative decoding built in. Throw PDFs, scans, tables, charts, or invoices at it and get clean markdown back. Available on 🤗 & Jina Reader `x-respond-with` today

Pareto front or ngmi. Releasing jina-reranker-v3.5: our latest 0.6B listwise reranker that takes v3's last-but-not-late interaction, makes it faster, and makes it actually good at the data enterprises search. 63.20 nDCG@10 on BEIR, beating Qwen3-Reranker-4B at ~7× fewer params.

jina-embeddings-v5-omni is here! Our first universal embedding model for text, images, audio, and video. Available in two sizes: small (1.57B, 1024-dim, 32K context) and nano (0.95B, 768-dim, 8K context). Both support Matryoshka truncation down to 32 dimensions.

v5-omni is back-compatible: if you already use jina-embeddings-v5-text-small/nano, the existing text indexes work with v5-omni out of the box. Without reindexing the text, just index your multimodal content with v5-omni and start searching images, audio, and video.