DeepSearch

Search, read and reason until best answer found.

Demo

DeepSearch API

Fully compatible with OpenAI's Chat API schema, simply swap api.openai.com with deepsearch.jina.ai to get started.

Vibe check with a simple chat UI. DeepSearch is best for complex questions that require iteratively reasoning, world-knowledge or up-to-date information.

Model

ID of the model to use.

jina-deepsearch-v1

Streaming

Delivers events as they occur through server-sent events, including reasoning steps and final answers. We strongly recommend keeping this option enabled since DeepSearch requests can take significant time to complete. Disabling streaming may result in '524 timeout' errors.

Reasoning Effort

Constrains effort on reasoning for reasoning models. Currently supported values are low, medium, and high. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

medium

Budget Tokens

This determines the maximum number of tokens is allowed use for DeepSearch process. Larger budgets can improve response quality by enabling more exhausive search for complex queries, although DeepSearch may not use the entire budget allocated. This overrides the reasoning_effort parameter.

Max Attempts

The maximum number of retries for solving a problem in DeepSearch process. A larger value allows DeepSearch to retry solving the problem by using different reasoning approaches and strategies. This parameter overrides the reasoning_effort parameter.

No Direct Answer

Forces the model to take further thinking/search steps even when the query seems trivial. This is useful if you're using DeepSearch in scenarios where you're certain the query always needs DeepSearch, rather than for trivial questions like '1+1=?'

Max Returned URLs

The maximum number of URLs to include in the final answer/chunk. URLs are sorted by relevance and other important factors.

Structured Output

This enables Structured Outputs which ensures the final answer from the model will match your supplied JSON schema.

Good Domains

A list of domains that are given a higher priority for content retrieval. Useful for domain-specific, high-quality sources that provide valuable content.

Bad Domains

A list of domains to be strictly excluded from content retrieval. Typically used to filter out known spam, low-quality, or irrelevant websites.

Only Domains

A list of domains to be exclusively included in content retrieval. All other domains will be ignored. Useful for domain-specific searches.

Answer/Think Language

Force the language of the answer and think with the given language code. By default it is automatically determined from the primary language of the input messages. The quality of the answer may be subtly affected by the language.

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.

User

Assistant

User

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.

Plain text message

{
  "role": "user",
  "content": "hi"
}

Request

Bash

Language

curl https://deepsearch.jina.ai/v1/chat/completions \
  -H "Content-Type: application/json"\
  -H "Authorization: Bearer " \
  -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

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.

Response

200 OK

0.0 s

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
  },
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    "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_",
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    "https://arxiv.org/abs/2409.10173",
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    "https://seedtable.com/best-startups-in-china",
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    "https://sebastian-petrus.medium.com/build-openais-deep-research-open-source-alternative-4f21aed6d9f0",
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    "https://openai.com/index/introducing-deep-research",
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    "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/",
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    "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",
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    "https://reddit.com/submit?url=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
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    "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",
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    "https://m-ric-open-deep-research.hf.space/",
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    "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",
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    "javascript:UC_UI.showSecondLayer();",
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    "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",
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}

API key

Available tokens

This is your unique key. Store it securely!

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!Visit 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.

API Endpoint

Model Name

API Key

Chatwise

Cherry Studio

Chatbox

LobeChat

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

about 1000 tokens

about 1s

Quick answers to general knowledge questions

Cannot access real-time or post-training information

Answers are generated purely from pretrained knowledge with a fixed cutoff date

RAG and Grounded LLMs

about 10,000 tokens

about 3s

Questions requiring current or domain-specific information

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

about 500,000 tokens

about 50s

Complex questions requiring thorough research and reasoning

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.

Enter the API key you wish to recharge

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.

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.

Columns

Product	API Endpoint	Description	w/o API Key	w/ API Key	w/ Premium API Key	Average Latency	Token Usage Counting	Allowed Request
Reader API	`https://r.jina.ai`	Convert URL to LLM-friendly text	20 RPM	500 RPM	5000 RPM	7.9s	Count the number of tokens in the output response.	GET/POST
Reader API	`https://s.jina.ai`	Search the web and convert results to LLM-friendly text		100 RPM	1000 RPM	2.5s	Every request costs a fixed number of tokens, starting from 10000 tokens	GET/POST
DeepSearch	`https://deepsearch.jina.ai/v1/chat/completions`	Reason, search and iterate to find the best answer		50 RPM	500 RPM	56.7s	Count the total number of tokens in the whole process.	POST
Embedding API	`https://api.jina.ai/v1/embeddings`	Convert text/images to fixed-length vectors		500 RPM & 1,000,000 TPM	2,000 RPM & 5,000,000 TPM	depends on the input size	Count the number of tokens in the input request.	POST
Reranker API	`https://api.jina.ai/v1/rerank`	Rank documents by query		500 RPM & 1,000,000 TPM	2,000 RPM & 5,000,000 TPM	depends on the input size	Count the number of tokens in the input request.	POST
Classifier API	`https://api.jina.ai/v1/train`	Train a classifier using labeled examples		20 RPM & 200,000 TPM	60 RPM & 1,000,000 TPM	depends on the input size	Tokens counted as: input_tokens × num_iters	POST
Classifier API (Few-shot)	`https://api.jina.ai/v1/classify`	Classify inputs using a trained few-shot classifier		20 RPM & 200,000 TPM	60 RPM & 1,000,000 TPM	depends on the input size	Tokens counted as: input_tokens	POST
Classifier API (Zero-shot)	`https://api.jina.ai/v1/classify`	Classify inputs using zero-shot classification		200 RPM & 500,000 TPM	1,000 RPM & 3,000,000 TPM	depends on the input size	Tokens counted as: input_tokens + label_tokens	POST
Segmenter API	`https://api.jina.ai/v1/segment`	Tokenize and segment long text	20 RPM	200 RPM	1,000 RPM	0.3s	Token is not counted as usage.	GET/POST

Auto top-up on low token balance

Recommended for uninterrupted service in production. When your token balance drops below the set threshold, we will automatically recharge your saved payment method for the last purchased package, until the threshold is met.

We introduced a new pricing model on May 6th, 2025. If you enabled auto-recharge before this date, you'll continue to pay the old price (the one when you purchased). The new pricing only applies if you modify your auto-recharge settings or purchase a new API key.

< 1M Tokens

Top up when

FAQ

What is DeepSearch?

How is DeepSearch different from OpenAI and Gemini's deep research capabilities?

What API key do I need to use DeepSearch?

What happens when DeepSearch reaches its token budget? Does it return an incomplete answer?

Does DeepSearch guarantee accurate answers?

How long does a typical DeepSearch query take?

Can DeepSearch work with any OpenAI-compatible client like Chatwise, CherryStudio or ChatBox?

What are the rate limits for the API?

What is the content inside the <think> tag?

Does DeepSearch use Jina Reader for web search and reading?

Why does DeepSearch use so many tokens for my queries?

Is there a way to control or limit the number of steps?

How reliable are the references in the answers?

Can DeepSearch handle questions about future events?

How to get my API key?

What's the rate limit?

Rate Limit

Columns

Product	API Endpoint	Description	w/o API Key	w/ API Key	w/ Premium API Key	Average Latency	Token Usage Counting	Allowed Request
Reader API	`https://r.jina.ai`	Convert URL to LLM-friendly text	20 RPM	500 RPM	5000 RPM	7.9s	Count the number of tokens in the output response.	GET/POST
Reader API	`https://s.jina.ai`	Search the web and convert results to LLM-friendly text		100 RPM	1000 RPM	2.5s	Every request costs a fixed number of tokens, starting from 10000 tokens	GET/POST
DeepSearch	`https://deepsearch.jina.ai/v1/chat/completions`	Reason, search and iterate to find the best answer		50 RPM	500 RPM	56.7s	Count the total number of tokens in the whole process.	POST
Embedding API	`https://api.jina.ai/v1/embeddings`	Convert text/images to fixed-length vectors		500 RPM & 1,000,000 TPM	2,000 RPM & 5,000,000 TPM	depends on the input size	Count the number of tokens in the input request.	POST
Reranker API	`https://api.jina.ai/v1/rerank`	Rank documents by query		500 RPM & 1,000,000 TPM	2,000 RPM & 5,000,000 TPM	depends on the input size	Count the number of tokens in the input request.	POST
Classifier API	`https://api.jina.ai/v1/train`	Train a classifier using labeled examples		20 RPM & 200,000 TPM	60 RPM & 1,000,000 TPM	depends on the input size	Tokens counted as: input_tokens × num_iters	POST
Classifier API (Few-shot)	`https://api.jina.ai/v1/classify`	Classify inputs using a trained few-shot classifier		20 RPM & 200,000 TPM	60 RPM & 1,000,000 TPM	depends on the input size	Tokens counted as: input_tokens	POST
Classifier API (Zero-shot)	`https://api.jina.ai/v1/classify`	Classify inputs using zero-shot classification		200 RPM & 500,000 TPM	1,000 RPM & 3,000,000 TPM	depends on the input size	Tokens counted as: input_tokens + label_tokens	POST
Segmenter API	`https://api.jina.ai/v1/segment`	Tokenize and segment long text	20 RPM	200 RPM	1,000 RPM	0.3s	Token is not counted as usage.	GET/POST

Can I use the same API key for reader, embedding, reranking, classifying and fine-tuning APIs?

Can I monitor the token usage of my API key?

What should I do if I forget my API key?

Do API keys expire?

Can I transfer tokens between API keys?

Can I revoke my API key?

Why is the first request for some models slow?

Is user input data used for training your models?

Is billing based on the number of sentences or requests?

Is there a free trial available for new users?

Are tokens charged for failed requests?

What payment methods are accepted?

Is invoicing available for token purchases?