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World-class reranker for maximizing search relevancy.
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Reranker
new_releases m0 release!

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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Number of returned documents
The number of most relevant documents to return for the query.

Example query
Change it and see how the response changes!
Example candidate documents to rank
Change them and see how the response changes!

upload
Request
curl https://api.jina.ai/v1/rerank \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer " \
  -d @- <<EOFEOF
  {
    "query": "Organic skincare products for sensitive skin",
    "top_n": 3,
    "documents": [
        "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.",
        "New makeup trends focus on bold colors and innovative techniques: Step into the world of cutting-edge beauty with this seasons makeup trends. Bold, vibrant colors and groundbreaking techniques are redefining the art of makeup. From neon eyeliners to holographic highlighters, unleash your creativity and make a statement with every look.",
        "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.",
        "Neue Make-up-Trends setzen auf kräftige Farben und innovative Techniken: Tauchen Sie ein in die Welt der modernen Schönheit mit den neuesten Make-up-Trends. Kräftige, lebendige Farben und innovative Techniken setzen neue Maßstäbe. Von auffälligen Eyelinern bis hin zu holografischen Highlightern – lassen Sie Ihrer Kreativität freien Lauf und setzen Sie jedes Mal ein Statement.",
        "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.",
        "Las nuevas tendencias de maquillaje se centran en colores vivos y técnicas innovadoras: Entra en el fascinante mundo del maquillaje con las tendencias más actuales. Colores vivos y técnicas innovadoras están revolucionando el arte del maquillaje. Desde delineadores neón hasta iluminadores holográficos, desata tu creatividad y destaca en cada look.",
        "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。",
        "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。",
        "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。"
    ],
    "return_documents": false
  }
EOFEOF


jina-reranker-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 is the best-in-class reranker released on Jun 25th 2024; it is built for Agentic RAG. It features function-calling support, multilingual retrieval for over 100 languages, code search capabilities, and offers a 6x speedup over v1. Read more about 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.

Three Ways to Purchase

Subscribe to our API, purchase through cloud providers, or obtain a commercial license for your organization.
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With a commercial license for on-prem use
Require 100% control and privacy? Purchase a commercial license to use our models on-premises.
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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.
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Google Cloud
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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/ API Keykeyw/ Premium API KeykeyAverage LatencyToken Usage CountingAllowed Request
Reader APIhttps://r.jina.aiConvert URL to LLM-friendly text20 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 RPMtrending_up1000 RPM2.5sEvery request costs a fixed number of tokens, starting from 10000 tokensGET/POST
DeepSearchhttps://deepsearch.jina.ai/v1/chat/completionsReason, search and iterate to find the best answer0.5 RPM50 RPM500 RPM56.7sCount the total number of tokens in the whole process.POST
Embedding APIhttps://api.jina.ai/v1/embeddingsConvert text/images to fixed-length vectorsblock500 RPM & 1,000,000 TPMtrending_up2,000 RPM & 5,000,000 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 queryblock500 RPM & 1,000,000 TPMtrending_up2,000 RPM & 5,000,000 TPM
ssid_chart
depends on the input size
help
Count the number of tokens in the input request.POST
Classifier APIhttps://api.jina.ai/v1/trainTrain a classifier using labeled examplesblock20 RPM & 200,000 TPM60 RPM & 1,000,000 TPM
ssid_chart
depends on the input size
Tokens counted as: input_tokens × num_itersPOST
Classifier API (Few-shot)https://api.jina.ai/v1/classifyClassify inputs using a trained few-shot classifierblock20 RPM & 200,000 TPM60 RPM & 1,000,000 TPM
ssid_chart
depends on the input size
Tokens counted as: input_tokensPOST
Classifier API (Zero-shot)https://api.jina.ai/v1/classifyClassify inputs using zero-shot classificationblock200 RPM & 500,000 TPM1,000 RPM & 3,000,000 TPM
ssid_chart
depends on the input size
Tokens counted as: input_tokens + label_tokensPOST
Segmenter APIhttps://api.jina.ai/v1/segmentTokenize and segment long text20 RPM200 RPM1,000 RPM0.3sToken is not counted as usage.GET/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
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Reranker
Microsoft Azure
Embeddings
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Google Cloud
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Reranker

Performance Benchmark

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Show benchmark for v2 model (latest)
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Jina Reranker v2 is the best-in-class reranker released on Jun 25th 2024; it is built for Agentic RAG. It features function-calling support, multilingual retrieval for over 100 languages, code search capabilities, and offers a 6x speedup over v1. Read more about v2 model.

MKQA (Multilingual Knowledge Questions and Answers)
Recall 10 scores reported for different reranking models for MKQA dataset
BEIR (Heterogeneous Benchmark on Diverse IR Tasks)
NDCG 10 scores reported for different reranking models for Beir dataset
ToolBench. The benchmark collects over 16 thousand public APIs and corresponding synthetically-generated instructions for using them in single and multi-API settings.
Recall 3 scores reported for different reranking models for ToolBench dataset
NSText2SQL
Recall 3 scores reported for different reranking models for NSText2SQL dataset
CodeSearchNet. The benchmark is a combination of queries in docstring and natural language formats, with labelled code-segments relevant to the queries.
MRR 10 scores reported for different reranking models for CodeSearchNet dataset
Throughput of Jina Reranker v2 on RTX4090
Throughput (documents retrieved in 50ms) scores reported for different reranking models on an RTX 4090 GPU.

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.

FAQ

How much does the Reranker API cost?
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The pricing for the Reranker API is aligned with our Embedding API pricing structure. It begins with 10 million free tokens for each new API key. Beyond the free tokens, different packages are available for purchase. For more details, please visit our pricing section.
What is the difference between the two rerankers?
keyboard_arrow_down
jina-reranker-v2-base-multilingual excels in multilingual support, outperforming bge-reranker-v2-m3 and offering 15x faster throughput than jina-reranker-v1-base-en. It also supports agentic tasks and code retrieval. jina-colbert-v2 improves upon ColBERTv2, delivering 6.5% better retrieval performance and adding multilingual support for 89 languages. It features user-controlled embedding sizes for optimal efficiency and precision.
Are Jina Rerankers open source?
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Yes, both jina-reranker-v2-base-multilingual and jina-colbert-v2 are open source and available under the CC-BY-NC 4.0 license. You are freely to use, share, and adapt the models for non-commercial purposes.
Do the rerankers support multiple languages?
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Yes, both jina-reranker-v2-base-multilingual and jina-colbert-v2 support 100+ languages, including English, Chinese, and other major global languages. They are optimized for multilingual tasks and outperform previous models.
What is the maximum length for queries and documents?
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The maximum query token length is 512. There is no token limit for documents.
What is the maximum number of documents I can rerank per query?
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You can rerank up to 2048 documents per query.
What is the batch size and how many query-document tuples can I send in one request?
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There is no concept of batch size unlike our Embedding API. You can send only one query-document tuple per request, but the tuple can include up to 2048 candidate documents.
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 your endpoints be hosted privately on AWS, Azure, or GCP?
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Yes, our services are available on AWS, Azure, and GCP marketplaces. If you have specific requirements, please contact us at sales AT jina.ai.
launchAWS SageMakerlaunchGoogle CloudlaunchMicrosoft Azure
Do you offer a fine-tuned reranker on domain-specific data?
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If you are interested in a fine-tuned reranker tailored to specific domain data, please contact our sales team. Our team will respond to your inquiry promptly.
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.

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/ API Keykeyw/ Premium API KeykeyAverage LatencyToken Usage CountingAllowed Request
Reader APIhttps://r.jina.aiConvert URL to LLM-friendly text20 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 RPMtrending_up1000 RPM2.5sEvery request costs a fixed number of tokens, starting from 10000 tokensGET/POST
DeepSearchhttps://deepsearch.jina.ai/v1/chat/completionsReason, search and iterate to find the best answer0.5 RPM50 RPM500 RPM56.7sCount the total number of tokens in the whole process.POST
Embedding APIhttps://api.jina.ai/v1/embeddingsConvert text/images to fixed-length vectorsblock500 RPM & 1,000,000 TPMtrending_up2,000 RPM & 5,000,000 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 queryblock500 RPM & 1,000,000 TPMtrending_up2,000 RPM & 5,000,000 TPM
ssid_chart
depends on the input size
help
Count the number of tokens in the input request.POST
Classifier APIhttps://api.jina.ai/v1/trainTrain a classifier using labeled examplesblock20 RPM & 200,000 TPM60 RPM & 1,000,000 TPM
ssid_chart
depends on the input size
Tokens counted as: input_tokens × num_itersPOST
Classifier API (Few-shot)https://api.jina.ai/v1/classifyClassify inputs using a trained few-shot classifierblock20 RPM & 200,000 TPM60 RPM & 1,000,000 TPM
ssid_chart
depends on the input size
Tokens counted as: input_tokensPOST
Classifier API (Zero-shot)https://api.jina.ai/v1/classifyClassify inputs using zero-shot classificationblock200 RPM & 500,000 TPM1,000 RPM & 3,000,000 TPM
ssid_chart
depends on the input size
Tokens counted as: input_tokens + label_tokensPOST
Segmenter APIhttps://api.jina.ai/v1/segmentTokenize and segment long text20 RPM200 RPM1,000 RPM0.3sToken is not counted as usage.GET/POST

Do I need a commercial license?

CC BY-NC License Self-Check

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Are you using our official API or official images on Azure or AWS?
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Yes
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Are you using a paid API key or free trial key?
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Paid API key
No restrictions. Use as per your current agreement.
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Free API key
Free trial key can be only used for non-commercial purposes. Please purchase a paid package for commercial use.
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Are you using our official model images on AWS and Azure?
No restrictions. Use as per your current agreement.
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No
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Are you using these models?
jina-reranker-m0
jina-clip-v2
jina-embeddings-v3
jina-reranker-v2-base-multilingual
jina-colbert-v2
reader-lm-1.5b
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This is non-commercial. You can use the models freely.
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API-related common questions
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Can I use the same API key for reader, embedding, reranking, classifying and fine-tuning APIs?
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Yes, the same API key is valid for all search foundation products from Jina AI. This includes the reader, embedding, reranking, classifying and fine-tuning APIs, with tokens shared between the all services.
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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.
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No, our API keys do not have an expiration date. However, if you suspect your key has been compromised and wish to retire it, please contact our support team for assistance. You can also revoke your key in the API Key Management dashboard.
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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 user input data used for training your models?
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We adhere to a strict privacy policy and do not use user input data for training our models. We are also SOC 2 Type I and Type II compliant, ensuring high standards of security and privacy.
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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We offer a welcoming free trial to new users, which includes ten millions 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.
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Are tokens charged for failed requests?
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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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