Perplexity

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.

Mar 07, 2025 128,000 context 8,000 tokens output
Multi-Step Web Search Source Synthesis Deep Reasoning 128K Context Window Cited Report Generation Autonomous Research Planning

Model Overview

High-signal model metadata in a structured two-column overview table.

Provider

The entity that provides this model.

Perplexity

Model ID

The routed model identifier exposed by upstream providers.

perplexity/sonar-deep-research

Input Context Window

The number of tokens supported by the input context window.

128,000 tokens

Maximum Output Tokens

The number of tokens that can be generated by the model in a single request.

8,000 tokens tokens

Open Source

Whether the model's code is available for public use.

No

Release Date

When the model was first released.

Mar 07, 2025 1 year ago

Knowledge Cut-off Date

When the model's knowledge was last updated.

February 2025

API Providers

The providers that offer this model. This is not an exhaustive list.

Perplexity

Modalities

Types of data this model can process.

Text

What is Sonar Deep Research

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.

Capabilities

What Sonar Deep Research supports

AI

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.

AI

Source Synthesis

Combines findings from hundreds of sources into a single coherent report, with citations included throughout the output.

RN

Deep Reasoning

Applies a dedicated reasoning phase before generating a final response, allowing the model to evaluate gathered material and produce more accurate, nuanced outputs.

CTX

128K Context Window

Supports up to 128,000 tokens per session, enabling large volumes of text, citations, and research material to be processed together.

AI

Cited Report Generation

Produces structured, long-form reports with inline citations linking back to the original web sources consulted during research.

AI

Autonomous Research Planning

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.

Pricing for Sonar Deep Research

Primary API pricing shown in the same “quick compare” spirit as the reference page.

Price Comparison

Additional usage-cost dimensions synced into the project for this model.

Web search $5000.00
Reasoning $3.00
maxTemperature 1.9
maxResponseSize 8,000 tokens

API Access & Providers

Places where this model is available, based on the synced detail-page metadata.

Perplexity

Provider Endpoints

Endpoint-level provider data currently available for this model.

Perplexity

1d uptime: 99.8% Supported params: 9 Implicit caching: No

Configuration & Parameters

The configurable options currently documented for this model.

Return Citations

Select

Determines whether or not a request to an online model should return citations.

Default: false
No Yes

Return Images

Select

Determines whether or not a request to an online model should return images.

Default: false
No Yes

Supported Request Parameters

Parameters currently listed by OpenRouter or the local catalog for this model.

Return Citations Return Images

Model Performance

Benchmark scores synced from the current model source and normalized into the local catalog.

Benchmark Score
AIME 2024
American math olympiad problems
48.7%
GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
47.1%
HLE
Questions that challenge frontier models across many domains
7.3%
LiveCodeBench
Real-world coding tasks from recent competitions
29.5%
MATH-500
Undergraduate and competition-level math problems
81.7%
MMLU-Pro
Expert knowledge across 14 academic disciplines
68.9%
SciCode
Scientific research coding and numerical methods
22.9%

Resources & Documentation

Official model cards, release notes, docs, and other references synced from the source page.

Community discussion

What people think about Sonar Deep Research

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.

r/openrouter 3 upvotes 6 comments May 17, 2025
Why is Perplexity's Sonar Deep Research so expensive on OpenRouter?

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!

Open Reddit thread
r/perplexity_ai 2 upvotes 2 comments March 19, 2025
API for Sonar Deep Research is not as good??!?!

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...**

Open Reddit thread
r/perplexity_ai 3 upvotes 1 comments February 26, 2025
API Issues with 'Sonar-Deep-Research'

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.......

Open Reddit thread
View more discussions →
FAQ

Common questions about Sonar Deep Research

What is the context window for Sonar Deep Research?

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.

How is Sonar Deep Research different from a standard search or retrieval model?

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.

Does Perplexity use my queries or outputs to train Sonar Deep Research?

No. According to the model's documentation, customer queries and outputs are not used to train the model.

What is the knowledge cutoff or training date for Sonar Deep Research?

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.

Where can I find pricing information for Sonar Deep Research?

Pricing details are available on Perplexity's official pricing page at https://docs.perplexity.ai/docs/getting-started/pricing.

What types of tasks is Sonar Deep Research best suited for?

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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