Sonar Deep Research
Perplexity's exhaustive deep research model that autonomously searches hundreds of sources to deliver expert-level analysis and comprehensive reports.
Perplexity Autonomous multi-source research with cited reports
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.
What Sonar Deep Research supports
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.
Source Synthesis
Combines findings from hundreds of sources into a single coherent report, with citations included throughout the output.
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.
128K Context Window
Supports up to 128,000 tokens per session, enabling large volumes of text, citations, and research material to be processed together.
Cited Report Generation
Produces structured, long-form reports with inline citations linking back to the original web sources consulted during research.
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.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 68.9% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 47.1% |
| MATH-500 | Undergraduate and competition-level math problems | 81.7% |
| AIME 2024 | American math olympiad problems | 48.7% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 29.5% |
| HLE | Questions that challenge frontier models across many domains | 7.3% |
| SciCode | Scientific research coding and numerical methods | 22.9% |
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.
Parameters & options
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.
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