Embedding Models/Jina Embeddings v5 Text Nano

Jina Embeddings v5 Text Nano by Jina AI — 768D

239M-parameter lightweight multilingual text embedding model scoring 71.0 on MTEB English v2 and 65.5 on MMTEB. Matches or exceeds all other sub-500M embedding models including KaLM-mini-v2.5 and Gemma-300M. Built on EuroBERT-210M with distillation from Qwen3-Embedding-4B. Supports 32K tokens, 768-dim with Matryoshka truncation. Ideal for resource-constrained deployments.

At a glance

Modalities

Dimensions

768

Max tokens

8K

Parameters

239M

Price / 1M tokens

Type

Dense

Matryoshka dimensions

3264128256512768

Output types

DenseLate Interaction

Language support

multilingual

Benchmarks

MTEB ENGLISH V2 71.00
MMTEB 65.50

Details

Release date 2026-02-18
License CC BY-NC 4.0
Model ID jina-embeddings-v5-text-nano
Provider Jina AI

Tags

textretrievaltext-matchingclusteringclassificationmteb

What You Need to Know About Jina Embeddings v5 Text Nano

Complete Specifications for Jina Embeddings v5 Text Nano by Jina AI

Get detailed specifications for Jina Embeddings v5 Text Nano, including output dimensionality of 768 dimensions, maximum input token length, supported input modalities, and pricing per million tokens. This embedding model is designed for semantic search, text classification, clustering, and retrieval-augmented generation applications where understanding the relationship between texts is essential.

Pricing and Cost Efficiency for Jina Embeddings v5 Text Nano

Review the pricing structure for Jina Embeddings v5 Text Nano and compare it against other embedding models from Jina AI and competitors. Understanding embedding model costs is essential when scaling vector search and RAG applications to millions of documents. We provide transparent pricing to help you budget effectively.

Use Cases and Applications for Jina Embeddings v5 Text Nano

Explore the ideal use cases for Jina Embeddings v5 Text Nano. Whether you are building a semantic search engine, recommendation system, document classification pipeline, or multilingual retrieval system, understanding this model's capabilities and dimensionality will help you choose the right embedding strategy for your project.