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.
Modalities
Dimensions
768
Max tokens
8K
Parameters
239M
Price / 1M tokens
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Type
| MTEB ENGLISH V2 | 71.00 |
| MMTEB | 65.50 |
| Release date | 2026-02-18 |
| License | CC BY-NC 4.0 |
| Model ID | jina-embeddings-v5-text-nano |
| Provider | 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.
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.
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.