new Pareto front: jina-embeddings-v5-omni is here - our best-performing open-weight omni embedding model under 2B parameters, handles 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. Hugging Face: https://lnkd.in/gx_CAvtC arXiv: https://lnkd.in/gaaaVRuu Blog: https://lnkd.in/gZewAwwF
Jina AI
Software Development
Sunnyvale, California 20,569 followers
Your Search Foundation, Supercharged!
About us
Founded by Dr. Han Xiao in 2020, Jina AI is a leading search AI company. We provide Reader, Embeddings, Rerankers, and Small Language Models to help businesses build the best search. On October 9, 2025, Jina AI was acquired by Elastic (NYSE: ESTC).
- Website
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https://jina.ai
External link for Jina AI
- Industry
- Software Development
- Company size
- 11-50 employees
- Headquarters
- Sunnyvale, California
- Type
- Privately Held
- Founded
- 2020
- Specialties
- Neural Search, Information Retrieval, Search, rag, embeddings, reranker, and rerank
Locations
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Primary
Get directions
710 Lakeway Dr
Suite 200
Sunnyvale, California 94085, US
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Get directions
Prinzessinnenstraße 19-20
Berlin, 10969, DE
Employees at Jina AI
Updates
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Introducing jina-embeddings-v5-text. Our fifth-generation multilingual embedding models in two efficient sizes: a 677M small and 239M nano model — with task-specific LoRA adapters, Matryoshka dimensions, 32K context, and GGUF/MLX quantization for edge deployment, setting new benchmarks across MMTEB, MTEB English, and retrieval tasks.
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After releasing Jina VLM, we tore apart 70+ VLM and noticed something interesting. Language models have scaled to hundreds of billions of parameters, but 𝐯𝐢𝐬𝐢𝐨𝐧 𝐞𝐧𝐜𝐨𝐝𝐞𝐫𝐬? Still the same handful. And most people still only know CLIP. Our 30-page survey covers the landscape of 𝐯𝐢𝐬𝐢𝐨𝐧 𝐞𝐧𝐜𝐨𝐝𝐞𝐫𝐬: three training paradigms, dynamic resolution handling, multi-encoder fusion, and whether encoder-free architectures actually work. Useful if you're picking vision encoders or just want to understand how today's VLM got here.
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Our 3rd BoF on Embeddings, Rerankers, Small LMs for Better Search at #EMNLP2025, Suzhou - after Singapore and Miami. 100+ attendees, 8 speakers featuring Andrianos Michail, Lucas Moeller, Ziyang Zeng, Hyukkyu Kang, Marc Briner, Siyue ZHANG, and Saba Sturua.
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Jina AI reposted this
“Jina AI’s team and technology bring cutting-edge models into the Elastic ecosystem, making our platform even more powerful for context engineering. Together, we are expanding what developers and enterprises can achieve with search-powered AI, while staying true to our commitment to openness and accessibility.” — Ashutosh Kulkarni, Elastic CEO. Today, we are excited to announce that we have joined forces with Jina AI, a pioneer in open source multimodal and multilingual embeddings, reranker, and small language models. This acquisition deepens Elastic’s capabilities in vector search, RAG, and context engineering to power agentic AI. Read more: https://go.es.io/4nBwD5L
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New 0.6B-parameter listwise reranker that considers the query and all candidate documents in a single context window. https://lnkd.in/duXy27p9
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We brought multimodal embeddings to llama.cpp and GGUF, and uncovered a few surprising issues along the way. https://lnkd.in/e7EU_k7r