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DeepSearch
Search, read and reason until best answer found.
Reader
Convert any URL to Markdown for better grounding LLMs.
Embeddings
World-class multimodal multilingual embeddings.
Reranker
World-class reranker for maximizing search relevancy.
Classifier
Zero-shot and few-shot classification for image and text.
Segmenter
Cut long text into chunks and do tokenization.
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Auto codegen for your copilot IDE or LLM
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DeepSearch

Search, read and reason until best answer found.
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DeepSearch API

Fully compatible with OpenAI's Chat API schema, simply swap api.openai.com with deepsearch.jina.ai to get started.
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Chat with DeepSearch
Vibe check with a simple chat UI. DeepSearch is best for complex questions that require iteratively reasoning, world-knowledge or up-to-date information.
open_in_new
Messages
A list of messages between the user and the assistant comprising the conversation so far. You can add images (webp, png, jpeg) or files (txt, pdf) to the message.
Attach Image/Document
Different message types (modalities) are supported, like text (.txt, .pdf), images (.png, .webp, .jpeg). Files are supported up to 10MB and must be encoded into data URI upfront.
{
  "role": "user",
  "content": "hi"
}
upload
Request
curl https://deepsearch.jina.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d @- <<EOFEOF
  {
    "model": "jina-deepsearch-v1",
    "messages": [
        {
            "role": "user",
            "content": "Hi!"
        },
        {
            "role": "assistant",
            "content": "Hi, how can I help you?"
        },
        {
            "role": "user",
            "content": "what's the latest blog post from jina ai?"
        }
    ],
    "stream": true,
    "reasoning_effort": "medium",
    "max_attempts": 1,
    "no_direct_answer": false
  }
EOFEOF
info
This is the last chunk of the stream which contains the final answer, visited URLs and the token usage. Click the button above to get real-time response.
download
Response
fiber_manual_record 200 OK
timer
0.0 s
straighten
196,526 Tokens
{
  "id": "1742181758589",
  "object": "chat.completion.chunk",
  "created": 1742181758,
  "model": "jina-deepsearch-v1",
  "system_fingerprint": "fp_1742181758589",
  "choices": [
    {
      "index": 0,
      "delta": {
        "content": "The latest blog post from Jina AI is titled \"Snippet Selection and URL Ranking in DeepSearch/DeepResearch,\" published on March 12, 2025 [^1]. This post discusses how to improve the quality of DeepSearch by using late-chunking embeddings for snippet selection and rerankers to prioritize URLs before crawling. You can read the full post here: https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch\n\n[^1]: Since our DeepSearch release on February 2nd 2025 we ve discovered two implementation details that greatly improved quality In both cases multilingual embeddings and rerankers are used in an in context manner operating at a much smaller scale than the traditional pre computed indices these models typically require  [jina.ai](https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch)",
        "type": "text",
        "annotations": [
          {
            "type": "url_citation",
            "url_citation": {
              "title": "Snippet Selection and URL Ranking in DeepSearch/DeepResearch",
              "exactQuote": "Since our DeepSearch release on February 2nd 2025, we've discovered two implementation details that greatly improved quality. In both cases, multilingual embeddings and rerankers are used in an _\"in-context\"_ manner - operating at a much smaller scale than the traditional pre-computed indices these models typically require.",
              "url": "https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch",
              "dateTime": "2025-03-13 06:48:01"
            }
          }
        ]
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 169670,
    "completion_tokens": 27285,
    "total_tokens": 196526
  },
  "visitedURLs": [
    "https://github.com/jina-ai/node-DeepResearch/blob/main/src/utils/url-tools.ts",
    "https://huggingface.co/jinaai/jina-embeddings-v3",
    "https://github.com/jina-ai/reader",
    "https://zilliz.com/blog/training-text-embeddings-with-jina-ai",
    "https://threads.net/@unwind_ai/post/DGmhWCVswbe/media",
    "https://twitter.com/JinaAI_/status/1899840196507820173",
    "https://jina.ai/news?tag=tech-blog",
    "https://docs.llamaindex.ai/en/stable/examples/embeddings/jinaai_embeddings",
    "https://x.com/jinaai_",
    "https://x.com/JinaAI_/status/1899840202358784170",
    "https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/funding-and-investors",
    "https://jina.ai/models",
    "https://linkedin.com/posts/imohitmayank_jinaai-has-unveiled-the-ultimate-developer-activity-7300401711242711040-VD64",
    "https://medium.com/@tossy21/trying-out-jina-ais-node-deepresearch-c5b55d630ea6",
    "https://huggingface.co/jinaai/jina-clip-v2",
    "https://arxiv.org/abs/2409.10173",
    "https://milvus.io/docs/embed-with-jina.md",
    "https://seedtable.com/best-startups-in-china",
    "https://threads.net/@sung.kim.mw/post/DGhG-J_vREu/jina-ais-a-practical-guide-to-implementing-deepsearchdeepresearchthey-cover-desi",
    "https://elastic.co/search-labs/blog/jina-ai-embeddings-rerank-model-open-inference-api",
    "http://status.jina.ai/",
    "https://apidog.com/blog/recreate-openai-deep-research",
    "https://youtube.com/watch?v=QxHE4af5BQE",
    "https://sdxcentral.com/articles/news/cisco-engages-businesses-on-ai-strategies-at-greater-bay-area-2025/2025/02",
    "https://aws.amazon.com/blogs/machine-learning/build-rag-applications-using-jina-embeddings-v2-on-amazon-sagemaker-jumpstart",
    "https://reddit.com/r/perplexity_ai/comments/1ejbdqa/fastest_open_source_ai_search_engine",
    "https://search.jina.ai/",
    "https://sebastian-petrus.medium.com/build-openais-deep-research-open-source-alternative-4f21aed6d9f0",
    "https://medium.com/@elmo92/jina-reader-transforming-web-content-to-feed-llms-d238e827cc27",
    "https://openai.com/index/introducing-deep-research",
    "https://python.langchain.com/docs/integrations/tools/jina_search",
    "https://varindia.com/news/meta-is-in-talks-for-usd200-billion-ai-data-center-project",
    "https://varindia.com/news/Mira-Murati%E2%80%99s-new-AI-venture-eyes-$9-billion-valuation",
    "https://53ai.com/news/RAG/2025031401342.html",
    "https://arxiv.org/abs/2409.04701",
    "https://bigdatawire.com/this-just-in/together-ai-raises-305m-series-b-to-power-ai-model-training-and-inference",
    "https://github.blog/",
    "https://cdn-uploads.huggingface.co/production/uploads/660c3c5c8eec126bfc7aa326/MvwT9enRT7gOESHA_tpRj.jpeg",
    "https://cdn-uploads.huggingface.co/production/uploads/660c3c5c8eec126bfc7aa326/JNs_DrpFbr6ok_pSRUK4j.jpeg",
    "https://app.dealroom.co/lists/33530",
    "https://api-docs.deepseek.com/news/news250120",
    "https://sdxcentral.com/articles/news/ninjaone-raises-500-million-valued-at-5-billion/2025/02",
    "https://linkedin.com/sharing/share-offsite?url=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
    "https://twitter.com/intent/tweet?url=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
    "https://platform.openai.com/docs/api-reference/chat/create",
    "https://mp.weixin.qq.com/s/-pPhHDi2nz8hp5R3Lm_mww",
    "https://huggingface.us17.list-manage.com/subscribe?id=9ed45a3ef6&u=7f57e683fa28b51bfc493d048",
    "https://automatio.ai/",
    "https://sdk.vercel.ai/docs/introduction",
    "https://app.eu.vanta.com/jinaai/trust/vz7f4mohp0847aho84lmva",
    "https://apply.workable.com/huggingface/j/AF1D4E3FEB",
    "https://facebook.com/sharer/sharer.php?u=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
    "https://facebook.com/sharer/sharer.php?u=http%3A%2F%2F127.0.0.1%3A3000%2Fen-US%2Fnews%2Fsnippet-selection-and-url-ranking-in-deepsearch-deepresearch%2F",
    "https://reddit.com/submit?url=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
    "https://apply.workable.com/huggingface",
    "https://news.ycombinator.com/submitlink?u=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
    "https://news.ycombinator.com/submitlink?u=http%3A%2F%2F127.0.0.1%3A3000%2Fen-US%2Fnews%2Fsnippet-selection-and-url-ranking-in-deepsearch-deepresearch%2F",
    "https://docs.github.com/site-policy/privacy-policies/github-privacy-statement",
    "https://discord.jina.ai/",
    "https://docs.github.com/site-policy/github-terms/github-terms-of-service",
    "https://bigdatawire.com/this-just-in/qumulo-announces-30-million-funding",
    "https://x.ai/blog/grok-3",
    "https://m-ric-open-deep-research.hf.space/",
    "https://youtu.be/sal78ACtGTc?feature=shared&t=52",
    "https://mp.weixin.qq.com/s/apnorBj4TZs3-Mo23xUReQ",
    "https://perplexity.ai/hub/blog/introducing-perplexity-deep-research",
    "https://githubstatus.com/",
    "https://github.blog/changelog/2021-09-30-footnotes-now-supported-in-markdown-fields",
    "https://openai.com/index/introducing-operator",
    "mailto:[email protected]",
    "https://resources.github.com/learn/pathways",
    "https://status.jina.ai/",
    "https://reuters.com/technology/artificial-intelligence/tencents-messaging-app-weixin-launches-beta-testing-with-deepseek-2025-02-16",
    "https://scmp.com/tech/big-tech/article/3298981/baidu-adopts-deepseek-ai-models-chasing-tencent-race-embrace-hot-start",
    "https://microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks",
    "javascript:UC_UI.showSecondLayer();",
    "https://resources.github.com/",
    "https://storm-project.stanford.edu/research/storm",
    "https://blog.google/products/gemini/google-gemini-deep-research",
    "https://youtu.be/vrpraFiPUyA",
    "https://chat.baidu.com/search?extParamsJson=%7B%22enter_type%22%3A%22ai_explore_home%22%7D&isShowHello=1&pd=csaitab&setype=csaitab&usedModel=%7B%22modelName%22%3A%22DeepSeek-R1%22%7D",
    "https://app.dover.com/jobs/jinaai",
    "http://localhost:3000/",
    "https://docs.cherry-ai.com/",
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    "https://docs.jina.ai/",
    "https://skills.github.com/",
    "https://partner.github.com/",
    "https://help.x.com/resources/accessibility",
    "https://business.twitter.com/en/help/troubleshooting/how-twitter-ads-work.html",
    "https://business.x.com/en/help/troubleshooting/how-twitter-ads-work.html",
    "https://support.twitter.com/articles/20170514",
    "https://support.x.com/articles/20170514",
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  ],
  "readURLs": [
    "https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch",
    "https://github.com/jina-ai/node-DeepResearch",
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    "https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch",
    "https://x.com/jinaai_?lang=en",
    "https://jina.ai/news",
    "https://x.com/joedevon/status/1896984525210837081",
    "https://github.com/jina-ai/node-DeepResearch/blob/main/src/tools/jina-latechunk.ts"
  ],
  "numURLs": 98
}

Chat with DeepSearch

Vibe check with a simple chat UI. DeepSearch is best for complex questions that require iteratively reasoning, world-knowledge or up-to-date information.
We've just launched a new DeepSearch UI that's lightning-fast, minimalist and FREE. Check it out at https://search.jina.ai or click the button below to give it a try!open_in_newVisit new UI
Chat Clients
For the best experience, we recommend using professional chat clients. DeepSearch is fully compatible with OpenAI's Chat API schema, making it easy to use with any OpenAI-compatible client.
open_in_new
Chatwise
open_in_new
Cherry Studio
open_in_new
Chatbox
open_in_new
LobeChat
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NextChat

What is DeepSearch?

DeepSearch combines web searching, reading, and reasoning for comprehensive investigation. Think of it as an agent that you give a research task to - it searches extensively and works through multiple iterations before providing an answer.

Standard LLMs

attach_money
about 1000 tokens
access_time
about 1s
check
Quick answers to general knowledge questions
close
Cannot access real-time or post-training information
Answers are generated purely from pretrained knowledge with a fixed cutoff date

RAG and Grounded LLMs

attach_money
about 10,000 tokens
access_time
about 3s
check
Questions requiring current or domain-specific information
close
Struggles with complex questions requiring multi-hop reasoning
Answers generated by summarizing a single-pass search results
Can access current information beyond training cutoff

DeepSearch

attach_money
about 500,000 tokens
access_time
about 50s
check
Complex questions requiring thorough research and reasoning
info
Takes longer than simple LLM or RAG approaches
Autonomous agent that iteratively searches, reads, and reasons
Dynamically decides next steps based on current findings
Self-evaluates answer quality before returning results
Can perform deep dives into topics through multiple search and reasoning cycles

API Pricing

API pricing is based on the token usage. One API key gives you access to all search foundation products.
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.
keyboard_arrow_down
Rate Limit
Rate limits are tracked in three ways: QPS (requests per second), RPM (requests per minute), and TPM (tokens per minute). QPS has a fixed limit of 100 per IP regardless of your paid tier, which prevents our system from being overwhelmed by high-volume concurrent requests. Other 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 RPM200 RPMtrending_up2000 RPM4.6sCount the number of tokens in the output response.GET/POST
Reader APIhttps://s.jina.aiSearch the web and convert results to LLM-friendly textblock40 RPMtrending_up400 RPM5.7sEvery 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 answer1 RPM10 RPM100 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

FAQ

What is DeepSearch?
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DeepSearch is an LLM API that performs iterative search, reading, and reasoning until it finds an accurate answer to a query or reaches its token budget limit.
How is DeepSearch different from OpenAI and Gemini's deep research capabilities?
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Unlike OpenAI and Gemini, DeepSearch specifically focuses on delivering accurate answers through iteration rather than generating long-form articles. It's optimized for quick, precise answers from deep web search rather than creating comprehensive reports.
What API key do I need to use DeepSearch?
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You need a Jina API key. We offers 1M free tokens for new API keys.
What happens when DeepSearch reaches its token budget? Does it return an incomplete answer?
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It generates a final answer based on all accumulated knowledge, rather than just giving up or returning an incomplete response.
Does DeepSearch guarantee accurate answers?
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No. While it uses an iterative search process to improve accuracy, the evaluation shows it achieves a 75% pass rate on test questions, significantly better than the 0% baseline (gemini-2.0-flash) but not perfect.
How long does a typical DeepSearch query take?
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It varies significantly - queries can take anywhere from 1 to 42 steps, with an average of 4 steps based on evaluation data. That's 20 seconds. Simple queries might resolve quickly, while complex research questions can involve many iterations and up to 120 seconds.
Can DeepSearch work with any OpenAI-compatible client like Chatwise, CherryStudio or ChatBox?
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Yes, the official DeepSearch API at deepsearch.jina.ai/v1/chat/completions is fully compatible with the OpenAI API schema, using 'jina-deepsearch-v1' as the model name. Therefore it is super easy to switch from OpenAI to DeepSearch and use with local clients or any OpenAI-compatible client. We highly recommend Chatwise for a seamless experience.
What are the rate limits for the API?
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Rate limits vary by API key tier, ranging from 10 RPM to 30 RPM. This is important to consider for applications with high query volumes.
What is the content inside the <think> tag?
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DeepSearch wraps thinking steps in XML tags ... and provides the final answer afterward, following the OpenAI streaming format but with these special markers for the chain of thoughts.
Does DeepSearch use Jina Reader for web search and reading?
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Yes. Jina Reader is used for web search and reading, providing the system with the ability to efficiently access and process web content.
Why does DeepSearch use so many tokens for my queries?
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Yes, the token usage of DeepSearch on complex queries is arguably high - averaging 70,000 tokens compared to 500 for basic LLM responses. This shows the depth of research but also has cost implications.
Is there a way to control or limit the number of steps?
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The system is primarily controlled by token budget rather than step count. Once the token budget is exceeded, it enters Beast Mode for final answer generation. Check reasoning_effort for more details.
How reliable are the references in the answers?
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References are considered so important that if an answer is deemed definitive but lacks references, the system continues searching rather than accepting the answer.
Can DeepSearch handle questions about future events?
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Yes, but with extensive research steps. The example of 'who will be president in 2028' shows it can handle speculative questions through multiple research iterations, though accuracy isn't guaranteed for such predictions.

How to get my API key?

video_not_supported

What's the rate limit?

Rate Limit
Rate limits are tracked in three ways: QPS (requests per second), RPM (requests per minute), and TPM (tokens per minute). QPS has a fixed limit of 100 per IP regardless of your paid tier, which prevents our system from being overwhelmed by high-volume concurrent requests. Other 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 RPM200 RPMtrending_up2000 RPM4.6sCount the number of tokens in the output response.GET/POST
Reader APIhttps://s.jina.aiSearch the web and convert results to LLM-friendly textblock40 RPMtrending_up400 RPM5.7sEvery 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 answer1 RPM10 RPM100 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
API-related common questions
code
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.
code
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.
code
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
code
Do API keys expire?
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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.
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.
code
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 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.
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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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Yes, an invoice will be issued to the email address associated with your Stripe account upon the purchase of tokens.
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402 Floor 4, Fu'an Technology Building, Shenzhen, China
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