Sonar Huge
Sonar Huge is Perplexity's most capable search-augmented model, combining a large underlying language model with real-time web retrieval to answer questions with current, cited information. As the top tier of the Sonar family, it applies greater reasoning depth and more comprehensive source synthesis than the lighter Sonar variants, making it appropriate for complex research queries that benefit from broad web coverage.
The model is designed to minimize hallucination on factual questions by grounding responses in retrieved sources, with inline citations included in output. It excels at multi-hop research tasks, competitor and market analysis, and any question where information currency matters. Organizations that need reliable, attributed answers to knowledge-intensive queries will find it among the most capable retrieval-augmented options available.
Key Features
Real-time web retrieval integrated directly into response generation
Inline source citations for factual claims and data points
Largest context and reasoning capacity within the Perplexity Sonar family
Strong multi-hop research synthesis across multiple retrieved sources
Minimizes hallucination on current-events and factual questions
Suitable for competitor intelligence, market research, and news analysis
Ideal Use Cases
Deep research queries requiring synthesis across multiple current sources
Market and competitor analysis needing up-to-date information
Fact-checking and claim verification with attributed sourcing
News monitoring and trend analysis requiring real-time grounding
Academic or business research requiring cited, traceable answers
Example Prompts for Sonar Huge
Technical Specifications
| Provider | Perplexity |
| Category | Search |
| Modality | Text -> Text (web-grounded) |
Frequently Asked Questions
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