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Reader
Convert any URL to Markdown for better grounding LLMs.
Embeddings
Multimodal multilingual embeddings.
Reranker
Reranker for maximizing search relevancy.
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Run Jina models natively inside Elasticsearch.
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Reranker

Maximize the search relevancy and RAG accuracy with our cutting-edge reranker API.

Reranker API

Try our cutting-edge reranker API to maximize your search relevancy and RAG accuracy. Starting for free!
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help_outlineFAQ
Status
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Number of returned documents
top_n
top_k
Maximum number of top-ranked documents to return.
upload
Request
POST
curl "https://api.jina.ai/v1/rerank" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $JINA_API_KEY" \
  -d @- <<EOFEOF
{
  "model": null,
  "query": "Organic skincare products for sensitive skin",
  "top_n": 3,
  "documents": [
    text_fieldsclose"Organic skincare for sensitive skin with aloe vera and chamomile: Imagine the soothing embrace of na…ing, healthy complexion.",
    text_fieldsclose"New makeup trends focus on bold colors and innovative techniques: Step into the world of cutting-edg…atement with every look.",
    text_fieldsclose"Bio-Hautpflege für empfindliche Haut mit Aloe Vera und Kamille: Erleben Sie die wohltuende Wirkung u…einen strahlenden Teint.",
    text_fieldsclose"Neue Make-up-Trends setzen auf kräftige Farben und innovative Techniken: Tauchen Sie ein in die Welt…jedes Mal ein Statement.",
    text_fieldsclose"Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla: Descubre el poder de la n…el radiante y saludable.",
    text_fieldsclose"Las nuevas tendencias de maquillaje se centran en colores vivos y técnicas innovadoras: Entra en el … y destaca en cada look.",
    text_fieldsclose"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。",
    text_fieldsclose"新的化妆趋势注重鲜艳的颜色和创新的技巧：进入化妆艺术的新纪元，本季的化妆趋势以大胆的颜色和创新的技巧为主。无论是霓虹眼线还是全息高光，每一款妆容都能让您脱颖而出，展现独特魅力。",
    text_fieldsclose"敏感肌のために特別に設計された天然有機スキンケア製品: アロエベラとカモミールのやさしい力で、自然の抱擁を感じてください。敏感肌用に特別に設計された私たちのスキンケア製品は、肌に優しく栄養を与え、保護します。肌トラブルにさようなら、輝く健康な肌にこんにちは。",
    text_fieldsclose"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。"
    + add input
  ],
  "return_documents": false
}
EOFEOF

v3.5: Faster Listwise Reranking with Hybrid Attention and Self-Distillation

jina-reranker-v3.5 is a 0.6B parameter multilingual listwise reranker with a 131K context window. Hybrid attention and self-distillation make it both faster and more robust across domains than v3, and it is a drop-in replacement: the request schema is unchanged.
Read Release Notearrow_forward

m0: Multilingual Multimodal Document Reranker

Our new multimodal multilingual reranker for retrieving visual documents across multiple languages, with SOTA performance on multilingual long documents and code searching tasks.
Read Release Notearrow_forward

What is a Reranker?

The goal of a search system is to find the most relevant results quickly and efficiently. Traditionally, methods like BM25 or tf-idf have been used to rank search results based on keyword matching. Recent methods, such as embedding-based cosine similarity, have been implemented in many vector databases. These methods are straightforward but can sometimes miss the subtleties of language, and most importantly, the interaction between documents and a query's intent. This is where the "reranker" shines. A reranker is an advanced AI model that takes the initial set of results from a search—often provided by an embeddings/token-based search—and reevaluates them to ensure they align more closely with the user's intent. It looks beyond the surface-level matching of terms to consider the deeper interaction between the search query and the content of the documents.
account_tree
Here's how it works:
1
Initial Retrieval
A search system uses embeddings/BM25 to find a broad set of potentially relevant documents based on the user's query.
2
Reranking
The reranker then takes these results and analyzes them at a more granular level, considering the nuances of how the query terms interact with the document content.
3
Improved Results
It reorders the search results, placing the ones it deems most relevant at the top, based on this deeper analysis.
The reranker can significantly improve the search quality because it operates at a sub-document and sub-query level, meaning it looks at the individual words and phrases, their meanings, and how they relate to each other within the query and the documents. This results in a more precise and contextually relevant set of search results.
Jina Reranker v2, released in June 2024, was built for Agentic RAG: function-calling support, multilingual retrieval across more than 100 languages, and code search. It remains available, though jina-reranker-v3.5 supersedes it for text reranking. Read more about the v2 model.
Multilingual Retrieval
Reranker v2 enables document retrieval in over 100 languages, regardless of the query language.
Function-Calling & Code Search
Reranker v2 ranks code snippets and function signatures based on natural language queries, ideal for Agentic RAG applications.
Tabular and Structured Data Support
Reranker v2 ranks the most relevant tables based on natural language queries, helping to sort different table schemas and identify the most relevant one before generating an SQL query.

Two Ways to Purchase

Subscribe to our API or purchase through cloud providers.
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With 3 cloud service providers
Using AWS or Azure? You can deploy our models directly on your company's cloud platform and handle billing through the CSP account.
AWS SageMaker
Embeddings
Reranker
Microsoft Azure
Embeddings
Reranker
Google Cloud
Embeddings
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With Jina Search Foundation API
The easiest way to access all of our products. Top-up tokens as you go.
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
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/API key (RPM). Find out more about the rate limits for each product and tier below.
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Rate Limit
Rate limits are tracked in three ways: RPM (requests per minute), and TPM (tokens per minute). Limits are enforced per IP/API key and will be triggered when either the RPM or TPM threshold is reached first. When you provide an API key in the request header, we track rate limits by key rather than IP address.
ProductAPI EndpointDescriptionarrow_upwardw/o API Keykey_offw/ Free API Keykeyw/ Paid API Keykeyw/ Premium API KeykeyAverage LatencyToken Usage CountingAllowed Request
Reader APIhttps://r.jina.aiConvert URL to LLM-friendly text20 RPM500 RPM500 RPMtrending_up5000 RPM7.9sCount the number of tokens in the output response.GET/POST
Reader APIhttps://s.jina.aiSearch the web and convert results to LLM-friendly textblock100 RPM100 RPMtrending_up1000 RPM2.5sEvery request costs a fixed number of tokens, starting from 10000 tokensGET/POST
Embedding APIhttps://api.jina.ai/v1/embeddingsConvert text/images to fixed-length vectorsblock100 RPM & 100,000 TPM500 RPM & 2,000,000 TPMtrending_up5,000 RPM & 50,000,000 TPM
ssid_chart
depends on the input size
help
Count the number of tokens in the input request.POST
Reranker APIhttps://api.jina.ai/v1/rerankRank documents by queryblock100 RPM & 100,000 TPM500 RPM & 2,000,000 TPMtrending_up5,000 RPM & 50,000,000 TPM
ssid_chart
depends on the input size
help
Count the number of tokens in the input request.POST

On-premises deployment

Deploy Jina Reranker on 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.
AWS SageMaker
Embeddings
Reranker
Microsoft Azure
Embeddings
Reranker
Google Cloud
Embeddings

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

Learning about Reranker

What is a reranker? Why is vector search or cosine similarity not enough? Learn about rerankers from the ground up with our comprehensive guide.
August 03, 2026 • 11 minutes read
jina-reranker-v3.5: Faster Listwise Reranking with Hybrid Attention and Self-Distillation
A 0.6B listwise reranker that beats Qwen3-Reranker-4B on BEIR, reranks up to 1.56x faster than v3, and gains 9.6 nDCG@10 on semi-structured retrieval.
Jina AI
jina-reranker-v3.5: An Efficient Listwise Reranker with Hybrid Attention and Self-Distillation
July 20, 2026
jina-reranker-v3.5: An Efficient Listwise Reranker with Hybrid Attention and Self-Distillation
Listwise rerankers are the discriminative core of agentic retrieval pipelines, yet production deployment demands efficiency, domain robustness, and fluency on semi-structured data at the same time. We present jina-reranker-v3.5, a 0.6B-parameter listwise reranker that meets these demands together without sacrificing the cross-document comparison that makes its predecessor jina-reranker-v3 effective. It replaces uniform global attention with a hybrid schedule of three sliding-window layers followed by two global layers, pinning the terminal layer to global as last-but-not-late readout requires. It trains on a curated multi-domain mixture spanning legal, medical, financial, multilingual, and structured retrieval, and transfers quality through a three-stage self-distillation recipe in which a full-attention teacher sets an upper bound that a sparse-attention student then recovers. jina-reranker-v3.5 reaches 63.20 nDCG@10 on BEIR, matching a 4B model at roughly 7x fewer parameters, and lifts semi-structured retrieval by 9.6 nDCG@10 over jina-reranker-v3 while cutting listwise inference latency by up to 1.56x.
Light blue background with stylized text in the center, composed of small dots or squares, evoking a modern and minimalistic
October 03, 2025 • 7 minutes read
Jina Reranker v3: 0.6B Listwise Reranker for SOTA Multilingual Retrieval
New 0.6B-parameter listwise reranker that considers the query and all candidate documents in a single context window.
Jina AI
jina-reranker-v3: Last but Not Late Interaction for Document Reranking
October 01, 2025
jina-reranker-v3: Last but Not Late Interaction for Document Reranking
jina-reranker-v3 is a 0.6B parameter multilingual document reranker that introduces a novel last but not late interaction. Unlike late interaction models such as ColBERT that perform separate encoding followed by multi-vector matching, our approach conducts causal self-attention between query and documents within the same context window, enabling rich cross-document interactions before extracting contextual embeddings from the last token of each document. This compact architecture achieves state-of-the-art BEIR performance with 61.94 nDCG@10 while being significant smaller than generative listwise rerankers.

FAQ

How much does the Reranker API cost?
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Reranker API pricing follows the same token-based structure as the Embeddings API, and tokens are shared across all Jina APIs on the same key. New API keys include free tokens to get started; beyond that, token packages are available for purchase. See the pricing section for details.
What are the differences between the Jina rerankers?
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jina-reranker-v3.5 is our current flagship: a 0.6B parameter multilingual listwise reranker with 131K context, and a drop-in replacement for jina-reranker-v3. It improves on v3 across every axis we measure, with the largest gains on structured-data and legal retrieval, and runs 1.22x to 1.56x faster. jina-reranker-v3 remains available. jina-reranker-m0 is the multimodal reranker for ranking visual documents. jina-reranker-v2-base-multilingual is a smaller cross-encoder supporting 100+ languages, useful when you need a non-Qwen-derived model. jina-colbert-v2 uses late interaction across 89 languages.
How are the Jina rerankers licensed?
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jina-reranker-v3.5, jina-reranker-v3, jina-reranker-m0, jina-reranker-v2-base-multilingual, and jina-colbert-v2 are released under CC-BY-NC 4.0. You are free to use, share, and adapt them for non-commercial purposes. Commercial production use requires a commercial license, which Elastic has sold as its own SKU since August 10, 2026 under the name Jina On-Prem. Contact Elastic Sales for a quote. Legacy jina-reranker-v1-* models remain Apache-2.0.
Do the rerankers support multiple languages?
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Yes, all current rerankers are multilingual. jina-reranker-v3.5 improves on v3 on MIRACL and on multilingual retrieval generally. jina-reranker-v3 and jina-reranker-v2-base-multilingual support 100+ languages, jina-reranker-m0 handles multilingual visual document ranking, and jina-colbert-v2 supports 89 languages.
What is the maximum context length for each reranker?
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Context length varies by model:

jina-reranker-v3.5: 131,072 tokens (query plus all documents combined) with auto-truncation
jina-reranker-v3: 131,072 tokens with auto-truncation
jina-reranker-m0: 10,000 tokens
jina-reranker-v2-base-multilingual: 1,024 tokens, with automatic chunking for longer documents
jina-colbert-v2: 8,192 tokens

For the v1 and v2 rerankers, queries are auto-truncated and long documents are chunked with max-pooling across chunks.
Is there a limit on the number of documents I can rerank per query?
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There is no hard limit on the number of documents per request. Like our Embeddings API, the Reranker API batches inputs internally by token count for optimal GPU utilization. You can send as many documents as needed in a single request.
What latency can I expect when reranking 100 documents?
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Latency varies from 100 milliseconds to 7 seconds, depending largely on the length of the documents and the query. For instance, reranking 100 documents of 256 tokens each with a 64-token query takes about 150 milliseconds. Increasing the document length to 4096 tokens raises the time to 3.5 seconds. If the query length is increased to 512 tokens, the time further increases to 7 seconds.
Below is the time cost of reranking one query and 100 documents in milliseconds:
Number of tokens in each document
Number of tokens in the query256512102420484096
64156323136621073571
128194369137721233598
256273475139721554299
5124681385211435367068
Can the rerankers be hosted privately, on my own infrastructure or in my own cloud account?
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Yes. The rerankers are available on the AWS, Azure, and GCP marketplaces for deployment in your own cloud account. For self-managed, on-premises, or air-gapped infrastructure, Elastic sells a commercial license (Jina On-Prem) that ships the models as fully offline Docker containers. Contact Elastic Sales for a quote.
launchAWS SageMakerlaunchGoogle CloudlaunchMicrosoft Azure
Do you offer a reranker fine-tuned on domain-specific data?
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Before commissioning a fine-tune, try jina-reranker-v3.5: it was trained with self-distillation specifically for domain robustness and shows large gains on legal and structured-data retrieval over v3. A well-chosen off-the-shelf reranker plus better chunking usually closes more of the gap than a fine-tune does, and it costs nothing to test. If it still falls short on your data, a domain-specific reranker is a custom engagement: contact Elastic Sales to scope it.
Contact
What's the minimum image size for the documents?
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The minimum acceptable image size for the jina-reranker-m0 model is 28x28 pixels.
What is listwise reranking and how does it differ from pointwise?
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jina-reranker-v3 and jina-reranker-v3.5 use a listwise architecture: the query and all candidates share one context window and are scored in a single forward pass, so the model can compare documents against each other. Traditional pointwise rerankers, including jina-reranker-v2-base-multilingual, score each document independently against the query. Listwise scoring is more accurate because relevance is often relative to what else is in the candidate set.
Why does the API enforce a different context length than the model supports?
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Some rerankers are architecturally capable of longer context than the hosted API accepts. Very long sequences consume substantial GPU memory, and we tune the serving configuration to balance throughput, latency, and cost for the majority of use cases. If you need the full architectural context length, run the model in your own infrastructure and contact Elastic Sales about a commercial license.
What are the rate limits for the Reranker API?
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Rate limits depend on your API key type:

Free: 100 RPM, 100K TPM
Paid: 500 RPM, 2M TPM
Premium: 5,000 RPM, 50M TPM

There is also an IP-based limit of 10,000 requests per 60 seconds. The same limits apply to the Embeddings and Reranker APIs, and tokens are shared across all Jina APIs on the same key.
Which reranker should I choose?
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Use jina-reranker-v3.5 for text. It is a drop-in replacement for jina-reranker-v3: the request schema is unchanged, so switching the model string is the entire migration. Use jina-reranker-m0 when your candidates are images or visually rich documents. Use jina-reranker-v2-base-multilingual when you need a smaller model or one that is not derived from a Qwen backbone.

How to get my API key?

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What's the rate limit?

Rate Limit
Rate limits are tracked in three ways: RPM (requests per minute), and TPM (tokens per minute). Limits are enforced per IP/API key and will be triggered when either the RPM or TPM threshold is reached first. When you provide an API key in the request header, we track rate limits by key rather than IP address.
ProductAPI EndpointDescriptionarrow_upwardw/o API Keykey_offw/ Free API Keykeyw/ Paid API Keykeyw/ Premium API KeykeyAverage LatencyToken Usage CountingAllowed Request
Reader APIhttps://r.jina.aiConvert URL to LLM-friendly text20 RPM500 RPM500 RPMtrending_up5000 RPM7.9sCount the number of tokens in the output response.GET/POST
Reader APIhttps://s.jina.aiSearch the web and convert results to LLM-friendly textblock100 RPM100 RPMtrending_up1000 RPM2.5sEvery request costs a fixed number of tokens, starting from 10000 tokensGET/POST
Embedding APIhttps://api.jina.ai/v1/embeddingsConvert text/images to fixed-length vectorsblock100 RPM & 100,000 TPM500 RPM & 2,000,000 TPMtrending_up5,000 RPM & 50,000,000 TPM
ssid_chart
depends on the input size
help
Count the number of tokens in the input request.POST
Reranker APIhttps://api.jina.ai/v1/rerankRank documents by queryblock100 RPM & 100,000 TPM500 RPM & 2,000,000 TPMtrending_up5,000 RPM & 50,000,000 TPM
ssid_chart
depends on the input size
help
Count the number of tokens in the input request.POST

Do I need a commercial license?

CC BY-NC License Self-Check
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Are you using our hosted API, or our official images on Azure, AWS, or GCP?
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Yes
No separate license needed. Commercial use is covered by the service terms: sign up and pay through this site or the cloud marketplace.
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No
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Are you running the model weights yourself, in a commercial product or service?
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No
Nothing to buy. Downloading, evaluating, benchmarking and research use are permitted under every license we publish under, with attribution.
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Yes
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Which license does the model carry? It is stated on the model's page on Hugging Face.
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Apache-2.0
Apache-2.0 permits commercial use. Nothing to buy. This covers our legacy v1 and v2 generation models.
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CC BY-NC 4.0
You need a commercial license. Since August 10, 2026, Elastic sells one for Jina models as its own SKU, called Jina On-Prem. It covers self-managed, on-premises, and air-gapped deployments, and it does not require an Elasticsearch subscription.
If you are already an Elastic customer, your account team can add it to your existing agreement.
Contact Elastic Sales
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Qwen Research License
A research license does not permit commercial use, and a commercial license for our other models does not extend to it. There is no commercial option for this one, self-hosted or through the API. Pick a model under one of the other two licenses instead.
API-related common questions
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Can I use the same API key across all Jina APIs?
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Yes. One API key is valid for all Jina AI search foundation products, including the Reader, Embeddings, Reranker, Classifier, and Segmenter APIs, with tokens shared across all of them.
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Can I monitor the token usage of my API key?
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Yes, token usage can be monitored in the 'API Key & Billing' tab by entering your API key, allowing you to view the recent usage history and remaining tokens. If you have logged in to the API dashboard, these details can also be viewed in the 'Manage API Key' tab.
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What should I do if I forget my API key?
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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. It's recommended to log in to keep your API key securely stored and easily accessible.
Contact
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Do API keys expire?
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No, our API keys do not have an expiration date. If a key is compromised, revoke it yourself in the API Key Management dashboard, which takes effect immediately; issue a replacement key first if you want to avoid downtime. Any remaining token balance stays on your account rather than on the revoked key. If you cannot access the dashboard, or believe the account itself is compromised, raise it with Elastic Support.
Contact
code
Can I transfer tokens between API keys?
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Yes, you can transfer tokens from a premium key to another. After logging into your account on the API Key Management dashboard, use the settings of the key you want to transfer out to move all remaining paid tokens.
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Can I revoke my API key?
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Yes, you can revoke your API key if you believe it has been compromised. Revoking a key will immediately disable it for all users who have stored it, and all remaining balance and associated properties will be permanently unusable. If the key is a premium key, you have the option to transfer the remaining paid balance to another key before revocation. Notice that this action cannot be undone. To revoke a key, go to the key settings in the API Key Management dashboard.
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Why is the first request for some models slow?
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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.
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Is my API data used to train your models?
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No. We never use your API requests, inputs, or outputs to train our embedding, reranker, or any other models. Your data remains yours.
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What are the rate limits for Jina APIs?
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Rate limits apply per API key:

Free: 100 RPM, 100K TPM
Paid: 500 RPM, 2M TPM
Premium: 5,000 RPM, 50M TPM

There is also an IP-based limit of 10,000 requests per 60 seconds. Limits vary by endpoint; see the rate limit table above for per-endpoint figures.
code
Are there batch size limits for the APIs?
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There is no batch size limit for either the Embeddings or Reranker APIs. You can send as many items or documents as needed per request. Both APIs batch inputs internally by token count for optimal GPU utilization.
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Are the Jina APIs the same thing as Jina models inside Elastic?
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No, they are three separate paths. The Jina APIs on this site are self-serve and pay-as-you-go with a Jina API key. The Elastic Inference Service (EIS) runs Jina models inside Elastic Cloud, billed through your Elastic subscription, with no infrastructure for you to manage. Jina On-Prem is a commercial license, sold by Elastic as its own SKU since August 10, 2026, for running the models in your own self-managed, on-premises, or air-gapped infrastructure. For the EIS and On-Prem paths, contact Elastic Sales.
Billing-related common questions
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Is billing based on the number of sentences or requests?
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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.
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Is there a free trial available for new users?
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Yes. New users get an auto-generated API key with free tokens usable across any of our models. Once the free tokens are consumed, you can purchase additional tokens for the key in the 'Buy tokens' tab.
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Are tokens charged for failed requests?
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No, tokens are not deducted for failed requests.
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What payment methods are accepted?
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Payments are processed through Stripe, supporting a variety of payment methods including credit cards, Google Pay, and PayPal for your convenience.
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Is invoicing available for token purchases?
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For self-serve token purchases, Stripe issues an invoice to the email address associated with your Stripe account at the time of purchase. If you need a formal purchase order, a negotiated contract, procurement paperwork, or consolidated billing, that runs through Elastic rather than Stripe: contact Elastic Sales.
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How do I buy a commercial license rather than API tokens?
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Token purchases on this site cover use of the hosted Jina APIs. They do not license you to run the model weights in your own infrastructure. For that, Elastic has sold a commercial license as its own SKU since August 10, 2026, priced annually rather than per token. Contact Elastic Sales for a quote.
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Can I pay by invoice or purchase order instead of card?
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Self-serve token purchases are processed through Stripe and invoiced automatically to your Stripe account email. For purchase orders, procurement processes, or volumes above what self-serve top-up supports, contact Elastic Sales.
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I paid, but my balance or rate limit has not changed. What should I check?
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Balance and rate limits belong to an API key, not to the account, so the first thing to check is the key itself rather than the account page: enter it in the API Key & Billing tab and confirm the balance and tier there. If the account holds more than one key, the tokens are on the key that was topped up, which may not be the key your application is sending. A new tier can also take a short time to propagate after payment. If the key shows the balance but is still limited at the previous tier after that, contact support.
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How do I cancel, stop auto top-up, or remove a saved payment method?
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Self-serve billing is managed from the customer portal reachable via the API Key & Billing tab, where auto top-up can be switched off and saved payment methods removed. Turning off auto top-up stops future charges but leaves any balance already purchased usable. If you also want the account and its data removed, or a refund considered, send that request to support; account deletion is handled manually and takes a few business days, and you will get written confirmation once it is done.
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