Elastic Introduces Best-in-Class Embedding Models for High Performance Semantic Search

Key Terms

embedding models technical

Embedding models are machine learning tools that convert text, images or other data into compact lists of numbers that capture meaning and relationships, like assigning coordinates on a map so similar items sit near each other. For investors, they power smarter search, faster document review, automated news and sentiment analysis, and better recommendation or fraud detection systems, which can cut costs and speed decisions. Their use can influence a company’s efficiency, product strength and competitive edge.

multilingual technical

Multilingual describes the ability of a company, product, document or communication system to use more than one language. For investors it matters because multilingual capabilities expand potential customers and markets, help meet regulatory and disclosure requirements in different regions, and reduce the risk of costly misunderstandings — like a store putting up signs in several languages so more people can find and use it safely and confidently.

vector search technical

Vector search is a way computers find items by comparing compact numerical “fingerprints” that capture meaning or patterns, rather than exact words or labels. Think of it like a librarian who groups books by theme and style, so you find similar ideas even if the wording differs; for investors, it matters because it powers smarter recommendations, faster insights from large datasets, and new AI services that can drive revenue, cut costs, or create competitive advantage.

RAG technical

A RAG (red-amber-green) status is a simple color-coded system used in reports and dashboards to show the health, progress, or risk level of a project, metric, or business area. Think of it as a traffic light: green means on track, amber means caution or potential issues, and red means serious problems. Investors use RAG indicators to quickly spot emerging risks or improvements that could affect future performance and value.

AI agents technical

AI agents are computer programs designed to perform tasks or make decisions automatically, often by learning from data and adapting to new information. They act like virtual assistants or robots that can handle complex activities without human intervention, which can help businesses and individuals save time and improve efficiency. For investors, AI agents matter because they can enhance decision-making and automate processes that influence markets and financial outcomes.

Elastic Inference Service (EIS) technical

An elastic inference service is a cloud offering that provides on‑demand hardware acceleration for machine‑learning model predictions, letting applications attach and pay for only the extra processing power they need when making real‑time inferences. For investors it matters because this can reduce operating costs and improve product responsiveness—like temporarily adding a turbocharger for short bursts of speed—so companies can scale AI features more cheaply and flexibly without buying permanent, expensive equipment.

hybrid search technical

A hybrid search is a way of finding documents that combines exact keyword matching with meaning-based (semantic) matching so results include both items that contain the precise words you typed and items that discuss the same ideas using different language. For investors, it improves due diligence and monitoring by surfacing relevant filings, research or announcements that a simple keyword search might miss—think of it as looking for both the exact spellings and any close synonyms or related concepts at once.

New Jina AI small language models deliver unmatched quality and efficiency on search and semantic tasks

SAN FRANCISCO--(BUSINESS WIRE)-- Elastic (NYSE: ESTC), the Search AI Company, today announced the availability of jina-embeddings-v5-text, a family of two small, Elasticsearch-native multilingual embedding models at 0.2B and 0.6B parameters that deliver state-of-the-art performance across key search and semantic tasks.

Despite their compact size, they outperform significantly larger models with 7B to 14B parameters and achieve best-in-class results on the MMTEB (Multilingual MTEB) benchmark among models of comparable size and purpose. Their small footprint enables outstanding hybrid search at lower infrastructure cost, faster query response, and new deployment scenarios where memory and compute budgets are tight - including edge devices and resource-constrained environments.

jina-embeddings-v5-text are available through multiple channels: as open-weight models on HuggingFace for self-hosted deployment via vLLM, llama.cpp, or MLX, and on Elastic Inference Service (EIS), a GPU-accelerated inference-as-a-service that makes it easy to run fast, high-quality inference without complex setup. By bringing the Jina v5 family to EIS, users get a complete data platform that consolidates state-of-the-art multilingual embedding models, a high-performance vector database, and more into one unified enterprise stack across cloud and on-premises.

“Vector search, RAG, and AI agents depend on high-quality retrieval,” said Steve Kearns, general manager, Search, Elastic. “With the addition of the Jina v5’s multilingual embeddings, Elasticsearch continues to be the platform of choice for end-to-end context engineering.”

The family includes two models, jina-embeddings-v5-text-small (239M parameters) and jina-embeddings-v5-text-nano (677M parameters). Both models are optimized for four common tasks in search and agentic applications:

• Retrieval: Allowing users to query with natural language and find the most relevant documents
• Text Matching: Allowing users to find duplicates in their data, and align paraphrases or translations
• Classification: Allowing users to categorize documents, detect sentiments, and find anomalies
• Clustering: Allowing users to group documents by topic, subject, or meaning

Availability

The Jina v5 models are now available through the Elastic Inference Service (EIS) on Elastic Cloud Serverless and Elastic Cloud Hosted. All Elastic Cloud Trials include access to EIS. To get started, visit the Elastic Inference Service (EIS) documentation.

These models are also available via an online API, and available for local hosting via vLLM, llama.cpp and MLX. Detailed instructions can be found on Hugging Face.

Additional Resources

• Release Note: jina-embeddings-v5-text: New SOTA Small Multilingual Embeddings
• Hugging Face weights
• MMTEB Leaderboard
• Technical Report on arXiv
• Elasticsearch integration guide
• Blog: jina-embeddings-v5-text: Compact state-of-the-art text embeddings for search and intelligent applications

About Elastic

Elastic (NYSE: ESTC), the Search AI Company, integrates its deep expertise in search technology with artificial intelligence to help everyone transform all of their data into answers, actions, and outcomes. Elastic's Search AI Platform — the foundation for its search, observability, and security solutions — is used by thousands of companies, including more than 50% of the Fortune 500. Learn more at elastic.co.

Elastic and associated marks are trademarks or registered trademarks of elasticsearch BV and its subsidiaries. All other company and product names may be trademarks of their respective owners.

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Source: Elastic N.V.