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World-class multimodal multilingual embeddings for search/RAG/agents.
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Embeddings
new_releases v3 release!

Top-performing multimodal multilingual long-context embeddings for search, RAG, agents applications.

v3: Frontier Multilingual Embeddings

jina-embeddings-v3 is a frontier multilingual text embedding model with 570M parameters and 8192 token-length, outperforming the latest proprietary embeddings from OpenAI and Cohere on MTEB. Read our blog post and research paper below.
Read Release Notearrow_forward

Embedding API

Try our world-class embedding models to improve your search and RAG systems. Start with a free trial!
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Example inputs
Change them and see how the response changes!
upload
Request
curl https://api.jina.ai/v1/embeddings \
	 -H "Content-Type: application/json" \
	 -H "Authorization: Bearer " \
	 -d '{
	"model": "undefined",
	"normalized": true,
	"embedding_type": "float",
	"input": [
		"Organic skincare for sensitive skin with aloe vera and chamomile: Imagine the soothing embrace of nature with our organic skincare range, crafted specifically for sensitive skin. Infused with the calming properties of aloe vera and chamomile, each product provides gentle nourishment and protection. Say goodbye to irritation and hello to a glowing, healthy complexion.", 
		"Bio-Hautpflege für empfindliche Haut mit Aloe Vera und Kamille: Erleben Sie die wohltuende Wirkung unserer Bio-Hautpflege, speziell für empfindliche Haut entwickelt. Mit den beruhigenden Eigenschaften von Aloe Vera und Kamille pflegen und schützen unsere Produkte Ihre Haut auf natürliche Weise. Verabschieden Sie sich von Hautirritationen und genießen Sie einen strahlenden Teint.", 
		"Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla: Descubre el poder de la naturaleza con nuestra línea de cuidado de la piel orgánico, diseñada especialmente para pieles sensibles. Enriquecidos con aloe vera y manzanilla, estos productos ofrecen una hidratación y protección suave. Despídete de las irritaciones y saluda a una piel radiante y saludable.", 
		"针对敏感肌专门设计的天然有机护肤产品：体验由芦荟和洋甘菊提取物带来的自然呵护。我们的护肤产品特别为敏感肌设计，温和滋润，保护您的肌肤不受刺激。让您的肌肤告别不适，迎来健康光彩。", 
		"新しいメイクのトレンドは鮮やかな色と革新的な技術に焦点を当てています: 今シーズンのメイクアップトレンドは、大胆な色彩と革新的な技術に注目しています。ネオンアイライナーからホログラフィックハイライターまで、クリエイティビティを解き放ち、毎回ユニークなルックを演出しましょう。"
    ]}'

API Pricing

Our API pricing is structured around the number of tokens sent in the requests. For Reader API, it is the number of tokens in the responses. This pricing model is applicable to all products in Jina AI's search foundation: Embedding, Reranking, Reader, Auto Fine-Tuning APIs. With the same API key, you have access to all API services.
Auto-recharge when tokens are low
Recommended for uninterrupted service in production. When your token balance is below the threshold you set, we will automatically recharge your credit card for the same amount as your last top-up. If you purchased multiple packs in the last top-up, we will recharge only one pack.
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speed
Understand the rate limit
Rate limits are the maximum number of requests that can be made to an API within a minute per IP address (RPM). Find out more about the rate limits for each product and tier below.
Rate Limit
ProductAPI EndpointDescription Allowed RequestWithout API Key (RPM)With API Key (RPM)With Premium API Key (RPM)Average Latency (s)Token Usage Counting
Embedding APIhttps://api.jina.ai/v1/embeddingsConvert text/images to fixed-length vectorsPOSTblock60keyboard_double_arrow_up300keyboard_double_arrow_up
bolt
depends on the input size
Count the number of tokens in the input request.
Reader APIhttps://r.jina.aiConvert URL to LLM-friendly textGET/POST2020010001.6Count the number of tokens in the output response.
Reader APIhttps://s.jina.aiSearch the web and convert results to LLM-friendly textGET/POST5401007.7Count the number of tokens in the output response.
Segmenter APIhttps://segment.jina.aiTokenize and segment long textGET/POST2020010000.3Token is not counted as usage.
Reranker APIhttps://api.jina.ai/v1/rerankTokenize and segment long textPOSTblock60keyboard_double_arrow_up300keyboard_double_arrow_up
bolt
depends on the input size
Count the number of tokens in the input request.
Top up this API key with more tokens
Depending on your location, you may be charged in USD, EUR, or other currencies. Taxes may apply.
Please input the right API key to top up

Choosing the Right Embeddings

Our embedding models are designed to cover diverse search and GenAI applications.
Multimodal Embeddings
view_in_ar
jina-clip-v1
General-Purpose Embeddings
view_in_ar
jina-embeddings-v2-base
Bilingual Embeddings
view_in_ar
jina-embeddings-v2-base-de
view_in_ar
jina-embeddings-v2-base-zh
view_in_ar
jina-embeddings-v2-base-es
Code Embeddings
view_in_ar
jina-embeddings-v2-base-code
API Integrations
Our Embedding API is natively integrated with various renowned databases, vector stores, RAG, and LLMOps frameworks. To begin, just copy and paste your API key into any of the listed integrations for a quick and seamless start.
Vector Store
LLMOps
RAG
Observability
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MongoDB
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DataStax
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Qdrant
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Pinecone
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Chroma
open_in_new
Weaviate
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Milvus
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Epsilla
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MyScale
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LlamaIndex
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Haystack
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Langchain
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Dify
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SuperDuperDB
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DashVector
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Portkey
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Baseten
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TiDB
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LanceDB
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Carbon

On-premises deployment

Deploy Jina Embeddings models in AWS Sagemaker and Microsoft Azure, and soon in Google Cloud Services, or contact our sales team to get customized Kubernetes deployments for your Virtual Private Cloud and on-premises servers.
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Our Publications

Understand how our frontier search models were trained from scratch, check out our latest publications. Meet our team at EMNLP, SIGIR, ICLR, NeurIPS, and ICML!
arXiv
September 18, 2024
jina-embeddings-v3: Multilingual Embeddings With Task LoRA
arXiv
September 07, 2024
Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
arXiv
August 30, 2024
Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever
arXiv
June 21, 2024
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
ICML 2024
May 30, 2024
Jina CLIP: Your CLIP Model Is Also Your Text Retriever
arXiv
February 26, 2024
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings
arXiv
October 30, 2023
Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
EMNLP 2023
July 20, 2023
Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models
8 publications in total.

Learning about Embeddings

Where to start with embeddings? We've got you covered. Learn about embeddings from the ground up with our comprehensive guide.
Dynamic image showing the characters "V3" formed by bright green dots varying in size on a black background.
September 18, 2024 • 10 minutes read
Jina Embeddings V3: A Frontier Multilingual Embedding Model
jina-embeddings-v3 is a frontier multilingual text embedding model with 570M parameters and 8192 token-length, outperforming the latest proprietary embeddings from OpenAI and Cohere on MTEB.
Jina AI
Dark-themed coding interface displaying English and Japanese characters with "JINA COLBERT V2" highlighted in the center.
August 30, 2024 • 10 minutes read
Jina ColBERT v2: Multilingual Late Interaction Retriever for Embedding and Reranking
Jina ColBERT v2 supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.
Jina AI
Diagram illustrating the 'Late Chunking' and 'Long Document Model' processes in machine learning on a black background.
August 22, 2024 • 8 minutes read
Late Chunking in Long-Context Embedding Models
Chunking long documents while preserving contextual information is challenging. We introduce the "Late Chunking" that leverages long-context embedding models to generate contextual chunk embeddings for better retrieval applications.
Michael Günther
Han Xiao
Rows of numbered wooden pieces on a white background, ranging from single digits to high numbers.
July 24, 2024 • 10 minutes read
Can Embedding/Reranker Models Compare Numbers?
A lot of LLMs can't figure out that 9.11 is actually smaller than 9.9. Can our embedding and reranker models do any better?
Han Xiao

Comparison of Reranker, Vector Search, and BM25

The table below provides a comprehensive comparison of the Reranker, Vector/Embeddings Search, and BM25, highlighting their strengths and weaknesses across various categories.
RerankerVector SearchBM25
Best ForEnhanced search precision and relevanceInitial, rapid filteringGeneral text retrieval across wide-ranging queries
GranularityDetailed: Sub-document and query segmentBroad: Entire documentsIntermediate: Various text segments
Query Time ComplexityHighMediumLow
Indexing Time ComplexityNot requiredHighLow, utilizes pre-built index
Training Time ComplexityHighHighNot required
Search QualitySuperior for nuanced queriesBalanced between efficiency and accuracyConsistent and reliable for a broad set of queries
StrengthsHighly accurate with deep contextual understandingQuick and efficient, with moderate accuracyHighly scalable, with established efficacy
Try reranker API for freeTry embedding API for free

The Evolution of Embeddings Poster

Discover the ideal poster for your space, featuring captivating infographics or breathtaking visuals tracing the evolution of text embedding models since 1950.
Learn how we made it
shopping_cartBuy a hard copy

FAQ

Rate Limit
ProductAPI EndpointDescription Allowed RequestWithout API Key (RPM)With API Key (RPM)With Premium API Key (RPM)Average Latency (s)Token Usage Counting
Embedding APIhttps://api.jina.ai/v1/embeddingsConvert text/images to fixed-length vectorsPOSTblock60keyboard_double_arrow_up300keyboard_double_arrow_up
bolt
depends on the input size
Count the number of tokens in the input request.
Reader APIhttps://r.jina.aiConvert URL to LLM-friendly textGET/POST2020010001.6Count the number of tokens in the output response.
Reader APIhttps://s.jina.aiSearch the web and convert results to LLM-friendly textGET/POST5401007.7Count the number of tokens in the output response.
Segmenter APIhttps://segment.jina.aiTokenize and segment long textGET/POST2020010000.3Token is not counted as usage.
Reranker APIhttps://api.jina.ai/v1/rerankTokenize and segment long textPOSTblock60keyboard_double_arrow_up300keyboard_double_arrow_up
bolt
depends on the input size
Count the number of tokens in the input request.
Embeddings-related common questions
How were the jina-embeddings-v2 models trained?
For detailed information on our training processes, data sources, and evaluations, please refer to our technical report available on arXiv.
launcharXiv
What is jina-clip-v1, can I use it for search text and image?
Jina CLIP jina-clip-v1 is the latest multimodal embedding model that supports text-text, text-image, image-image, and image-text retrieval tasks. Unlike OpenAI CLIP model that falls short on text-text search, Jina CLIP is trained to be your text retriever. You can read more about it from our tech report.
launcharXiv
Which languages do your models support?
Our models support English, German, Spanish, Chinese, various programming languages and images. For more details, please refer to our publication on bilingual models.
launcharXiv
What is the maximum length for a single sentence input?
Our models allow for an input length of up to 8192 tokens, which is significantly higher than most other models. A token can range from a single character, like 'a', to an entire word, such as 'apple'. The total number of characters that can be input depends on the length and complexity of the words used. This extended input capability enables our jina-embeddings-v2 models to perform more comprehensive text analysis and achieve higher accuracy in context understanding, especially for extensive textual data.
What is the maximum number of sentences I can include in a single request?
A single API call can process up to 2048 sentences or texts, facilitating extensive text analysis in one request.
How do I send images to the jina-clip-v1 model?
You can use either url or bytes in the input field of the API request. For url, provide the URL of the image you want to process. For bytes, encode the image in base64 format and include it in the request. The model will return the embeddings of the image in the response.
How do Jina Embeddings models compare to OpenAI's text-embedding-ada-002 model?
According to the MTEB Leaderboard, our Base model competes closely with OpenAI’s text-embedding-ada-002, exhibiting comparable performance on average. Furthermore, our Base model excels in several tasks, including classification, pair-classification, re-ranking, and summarization, outperforming OpenAI’s model.
How seamless is the transition from OpenAI's text-embedding-ada-002 to your solution?
The transition is streamlined, as our API endpoint, https://api.jina.ai/v1/embeddings, matches the input and output JSON schemas of OpenAI’s text-embeddings-ada-002 model. This compatibility ensures users can easily replace the OpenAI model with ours when using OpenAI’s endpoint.
How tokens are calculated when using jina-clip-v1?
The tokens are calculated based on the text length and image size. For text in the request, tokens are counted in the standard way. For image in the request, the following steps are conducted: 1. Tile Size: Each image is divided into tiles of size 224x224 pixels. 2. Coverage: The number of tiles required to completely cover the input image is calculated. Even if the image dimensions are not perfectly divisible by 224, we will count partial tiles as full tiles. 3. Total Tiles: The total number of tiles covering the image determines the cost. For instance, if an image is 500x500 pixels, it would be covered by 3x3 tiles, resulting in 9 tiles. 4. Cost Calculation: Each tile contributes to the final cost of processing the image. The cost per tile is 1000 tokens. Example: For an image with dimensions 500x500 pixels: • The image is divided into 224x224 pixel tiles. • The total number of tiles required is 3 (horizontal) x 3 (vertical) = 9 tiles. • The cost will be 9*1000 = 9000 tokens
Do you provide models for embedding images or audio?
Yes, jina-clip-v1 can embed both images and texts. Embedding models on more modalities will be announced soon!
Can Jina Embedding models be fine-tuned with private or company data?
For inquiries about fine-tuning our models with specific data, please contact us to discuss your requirements. We are open to exploring how our models can be adapted to meet your needs.
Contact
Can your endpoints be hosted privately on AWS, Azure, or GCP?
Yes, our services are available on the AWS marketplace, and we are in the process of expanding to Azure and GCP marketplaces. If you have particular requirements, please contact us at sales AT jina.ai.
launchAWS SageMaker
API-related common questions
code
Can I use the same API key for embedding, reranking, reader, fine-tuning APIs?
Yes, the same API key is valid for all search foundation products from Jina AI. This includes the embedding, reranking, reader and fine-tuning APIs, with tokens shared between the all services.
code
Can I monitor the token usage of my API key?
Yes, token usage can be monitored in the 'Buy tokens' tab by entering your API key, allowing you to view the usage history and remaining tokens.
code
What should I do if I forget my API key?
If you have misplaced a topped-up key and wish to retrieve it, please contact support AT jina.ai with your registered email for assistance.
Contact
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Do API keys expire?
No, our API keys do not have an expiration date. However, if you suspect your key has been compromised and wish to retire it or transfer its tokens to a new key, please contact our support team for assistance.
Contact
code
Why is the first request for some models slow?
This is because our serverless architecture offloads certain models during periods of low usage. The initial request activates or 'warms up' the model, which may take a few seconds. After this initial activation, subsequent requests process much more quickly.
code
Is user input data used for training your models?
We adhere to a strict privacy policy and do not use user input data for training our models.
Billing-related common questions
attach_money
Is billing based on the number of sentences or requests?
Our pricing model is based on the total number of tokens processed, allowing users the flexibility to allocate these tokens across any number of sentences, offering a cost-effective solution for diverse text analysis requirements.
attach_money
Is there a free trial available for new users?
We offer a welcoming free trial to new users, which includes one million tokens for use with any of our models, facilitated by an auto-generated API key. Once the free token limit is reached, users can easily purchase additional tokens for their API keys via the 'Buy tokens' tab.
attach_money
Are tokens charged for failed requests?
No, tokens are not deducted for failed requests.
attach_money
What payment methods are accepted?
Payments are processed through Stripe, supporting a variety of payment methods including credit cards, Google Pay, and PayPal for your convenience.
attach_money
Is invoicing available for token purchases?
Yes, an invoice will be issued to the email address associated with your Stripe account upon the purchase of tokens.
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Shenzhen, China
402, Floor 4, Fu'an Technology Building, Shenzhen Nanshan, China
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