Multi-Step Web Search
Conducts dozens of iterative web searches per query, evaluating and refining results across each step to build a comprehensive picture of a topic.
Sonar Deep Research is a text generation model developed by Perplexity AI, released in February 2025. It is designed specifically for complex, multi-step research tasks that require gathering and synthesizing information from a large number of web sources. Rather than returning a single retrieved answer, it autonomously plans a research strategy, conducts dozens of iterative web searches, evaluates the results, and refines its approach before producing a detailed, citation-backed report. It operates with a 128,000-token context window, allowing it to handle substantial volumes of text and references within a single session. Sonar Deep Research is best suited for tasks where thoroughness and accuracy take priority over response speed, such as academic research, market analysis, competitive intelligence, and due diligence investigations. It includes a dedicated reasoning phase in which the model thinks through gathered material before generating its final output, which helps produce more nuanced and accurate responses. The model does not use customer queries or outputs for training purposes. It is well-suited for professionals, researchers, and developers working in domains like finance, technology, healthcare, and current events who need reliable, well-sourced reports.
High-signal model metadata in a structured two-column overview table.
The entity that provides this model.
The routed model identifier exposed by upstream providers.
The number of tokens supported by the input context window.
The number of tokens that can be generated by the model in a single request.
Whether the model's code is available for public use.
When the model was first released.
When the model's knowledge was last updated.
The providers that offer this model. This is not an exhaustive list.
Types of data this model can process.
A fuller summary of positioning, capabilities, and source-specific details for Sonar Deep Research.
Sonar Deep Research is a text generation model developed by Perplexity AI, released in February 2025. It is designed specifically for complex, multi-step research tasks that require gathering and synthesizing information from a large number of web sources. Rather than returning a single retrieved answer, it autonomously plans a research strategy, conducts dozens of iterative web searches, evaluates the results, and refines its approach before producing a detailed, citation-backed report. It operates with a 128,000-token context window, allowing it to handle substantial volumes of text and references within a single session.
Sonar Deep Research is best suited for tasks where thoroughness and accuracy take priority over response speed, such as academic research, market analysis, competitive intelligence, and due diligence investigations. It includes a dedicated reasoning phase in which the model thinks through gathered material before generating its final output, which helps produce more nuanced and accurate responses. The model does not use customer queries or outputs for training purposes. It is well-suited for professionals, researchers, and developers working in domains like finance, technology, healthcare, and current events who need reliable, well-sourced reports.
Conducts dozens of iterative web searches per query, evaluating and refining results across each step to build a comprehensive picture of a topic.
Combines findings from hundreds of sources into a single coherent report, with citations included throughout the output.
Applies a dedicated reasoning phase before generating a final response, allowing the model to evaluate gathered material and produce more accurate, nuanced outputs.
Supports up to 128,000 tokens per session, enabling large volumes of text, citations, and research material to be processed together.
Produces structured, long-form reports with inline citations linking back to the original web sources consulted during research.
Independently determines a research strategy for a given query, deciding which sources to consult and how to iterate without requiring user guidance at each step.
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Additional usage-cost dimensions synced into the project for this model.
Places where this model is available, based on the synced detail-page metadata.
Endpoint-level provider data currently available for this model.
The configurable options currently documented for this model.
Determines whether or not a request to an online model should return citations.
Determines whether or not a request to an online model should return images.
Parameters currently listed by OpenRouter or the local catalog for this model.
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
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AIME 2024
American math olympiad problems
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
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HLE
Questions that challenge frontier models across many domains
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LiveCodeBench
Real-world coding tasks from recent competitions
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MATH-500
Undergraduate and competition-level math problems
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MMLU-Pro
Expert knowledge across 14 academic disciplines
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SciCode
Scientific research coding and numerical methods
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Official model cards, release notes, docs, and other references synced from the source page.
Sonar Deep Research discussions are most active in r/perplexity_ai, r/openrouter. The strongest match in this snapshot has 3 upvotes and 6 comments.
I'm currently testing OpenRouter and noticed that using **"Perplexity: Sonar Deep Research"** is surprisingly expensive. I have two main concerns I'd like to clarify:
# (1). Is there an additional ~40% fee applied by OpenRouter?
According to the pricing listed on [this page ](https://openrouter.ai/perplexity/sonar-deep-research), the cost is:
* $2 per million input tokens
* $8 per million output tokens
For my usage (only 1 prompt), I had:
* 1,937 input tokens
* 83,128 output tokens
A simple calculation gives:
(1,937 * $2 / 1,000,000) + (83,128 * $8 / 1,000,000) = $0.668898
However, I was actually charged **$0.935** , which is significantly higher.
Doing the math:
$0.935 / $0.668898 ≈ 139.78%
This suggests that the total cost is about **39.78% higher** than expected. Could this be due to an extra fee from OpenRouter?
# (2). Why is the OpenRouter price higher than Perplexity's direct pricing?
Looking at Perplexity's official pricing \[here\]([https://docs.perplexity.ai/guides/pricing ](https://docs.perplexity.ai/guides/pricing)\#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro), it states:
* Output tokens are priced at **$8 per million**
* However, "reasoning tokens" (used internally during research) are only **$3 per million**
Now, here's what confuses me: If OpenRouter is charging me for reasoning tokens as if they were output tokens (i.e., at the **$8/M** rate instead of $3/M).
# Request for Help
\- Could anyone please provide some insight or clarification? Any advice or explanation would be greatly appreciated.
\- Is there any way to minimize cost from this model, such as how to instruct this model not to returning reasoning tokens?
Thank you so much everyone!
I was using the API for sonar deep research a couple weeks ago and it worked just as the deep research worked on the website with FULL reports that were very very long and in-depth. Now, however, the reports it gives are only a couple words and aren't like the full reports that were 1k+ words that it gave before and that it gives on the website.
And.. **it's still as expensive or even more expensive for requests...**
I've been deploying the new 'Sonar-Deep-Research' via the API and I've ran into a few issues.
1. The 'citations' key doesn't always return sources and is sometimes empty.
2. During response, sometimes the output will be something like this at the end of the response (it just says that it will continue or that it stopped responding cause things are too long):
*\[Continuing for remaining sections covering healthcare reforms (Medicaid expansion), education policies (teacher residency programs), cultural developments (Tulip Festival expansion), etc., maintaining similar depth across all topics while adhering strictly to citation protocols and structural requirements.\]*
Not sure if these are known issues or I'm doing something wrong.......
Sonar Deep Research supports a context window of 128,000 tokens, allowing large amounts of text, citations, and research content to be handled within a single session.
Rather than returning a single retrieved answer, Sonar Deep Research autonomously plans a research strategy, performs dozens of iterative web searches, evaluates sources, and refines its approach before producing a detailed, citation-backed report.
No. According to the model's documentation, customer queries and outputs are not used to train the model.
The model's training data has a cutoff of February 2025. However, because it performs live web searches at inference time, it can access and cite information published after that date.
Pricing details are available on Perplexity's official pricing page at https://docs.perplexity.ai/docs/getting-started/pricing.
It is designed for tasks that require thoroughness over speed, including academic research, market analysis, competitive intelligence, and due diligence investigations across domains like finance, technology, and healthcare.
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