Perplexity: Sonar Deep Research
By Perplexity
Released: 2025-03-07
Perplexity Sonar Deep Research, developed by Perplexity AI, features 128k-token context window. 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 Premium pricing at $2.0/1M tokens reflects its advanced capabilities.
Visit Perplexity: Sonar Deep ResearchAI-Powered
Leverages advanced AI technology to deliver cutting-edge capabilities and results.
Fast & Efficient
Optimized performance ensures quick results without compromising on quality.
Purpose-Built
Specifically designed for llms tasks and workflows.
Perplexity Model Timeline
200k tokens context
128k tokens context
200k tokens context
128k tokens context
127k tokens context
Specifications
AI Evaluation
Combines language understanding with search capabilities. Excels at finding and synthesizing information from various sources.
Pros
- 128k token context window
- Advanced logical reasoning
- Built-in search capabilities
- Optimized for RAG workflows
Cons
- Moderate API costs
- API integration required
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