Scott Martens
Principal Tech Writer II
56% faster, up to 50% better retrieval performance: What's inside Jina's new 600 million parameter listwise reranker
Jina Reranker 3.5 beats v3 by 50%+ on case law, closes the gap with models 7x its size on legal, medical, and financial benchmarks, and beats them outright on structured data. It's a drop-in replacement for v3, with no API changes.
On-prem in under 5 minutes: Jina embedding models now available for on-prem deployment
All 28 Jina AI models, including rerankers, as ready-to-deploy Docker containers, with zero telemetry and no license server. Drop-in compatible with OpenAI, Cohere, Voyage AI and Elastic Inference Service APIs.
Small model, big benchmarks: how Jina-VLM beat the competition at 2.4B and what ICLR told us is coming next
Jina-VLM is a 2.4B open multilingual VLM leading VQA benchmarks across 29 languages. Plus: five days of ICLR 2026 takeaways on RLVR, sparse embeddings and retrieval.
One index, all media: Introducing jina-embeddings-v5-omni
jina-embeddings-v5-omni lets you embed text, images, video, and audio into a single Elasticsearch index and query across all of them at once.
jina-embeddings-v5-text: Compact state-of-the-art text embeddings for search and intelligent applications
Introducing jina-embeddings-v5-text models, including jina-embeddings-v5-text-small and jina-embeddings-v5-text-nano, and explaining how to use these multilingual embedding models via Elastic Inference Service (EIS).
An introduction to Jina models, their functionality, and uses in Elasticsearch
Explore Jina multimodal embeddings, Reranker v3, and semantic embedding models, and how to use them natively in Elasticsearch.
jina-vlm: Seeing like an AI with vision language models
Learn about vision language models (VLMs), what jina-vlm can do, how to use it, and best practices.
