Perplexity

Sonar Deep Research

PerplexitySpecialized
Thinking

About this model

Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. It autonomously searches, reads, and evaluates sources, refining its approach as it gathers information. This enables comprehensive report generation across domains like finance, technology, health, and current events. Notes on Pricing ([Source](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-deep-research)) - Input tokens comprise of Prompt tokens (user prompt) + Citation tokens (these are processed tokens from running searches) - Deep Research runs multiple searches to conduct exhaustive research. Searches are priced at $5/1000 searches. A request that does 30 searches will cost $0.15 in this step. - Reasoning is a distinct step in Deep Research since it does extensive automated reasoning through all the material it gathers during its research phase. Reasoning tokens here are a bit different than the CoTs in the answer - these are tokens that we use to reason through the research material prior to generating the outputs via the CoTs. Reasoning tokens are priced at $3/1M tokens

Performance Tier

Specialized

Sonar Deep Research is a specialized model from Perplexity : built for a specific domain.

Domain-specific model. Optimized for a particular task such as code generation, image creation, or web search.

Pricing

This model is included in Elosia plans
Typeper 1M tokens
Input (prompt)$2.00
Output (completion)$8.00
Internal reasoning$3.00

Capabilities

Context Length128K
Max Output Tokens
TokenizerOther
Inputtext
Outputtext
Release DateMarch 7, 2025

Recommended Use Cases

ResearchAnalysisData Extraction

Strengths

  • Multi-step autonomous research with web search
  • Synthesizes information from dozens of sources
  • Produces comprehensive, well-cited research reports
  • Ideal for literature reviews and competitive analysis

Limitations

  • Slower than standard Sonar due to multi-step process
  • Higher cost reflecting the extended search and synthesis
  • Results depend on availability and quality of web sources

Resources

This model may use your data for training

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