Perplexity: Sonar Reasoning Pro

Implements DeepSeek R1-powered chain-of-thought reasoning that interleaves web search queries throughout the reasoning process rather than reasoning in isolation. The model generates explicit reasoning traces while dynamically deciding when to invoke Perplexity's search API to ground reasoning in current information, enabling multi-step problem decomposition with real-time fact verification.

Executes live web searches through Perplexity's proprietary search infrastructure, returning ranked results based on semantic relevance to the query rather than link popularity. Results are integrated into reasoning context with source attribution, enabling the model to cite specific URLs and passages when answering questions.

Maintains conversation state across multiple turns, allowing the model to reference previous reasoning steps, search results, and conclusions without re-executing searches or re-reasoning from scratch. The model can build on prior context to refine answers or explore tangential questions while preserving the reasoning chain.

Extracts structured data (JSON, tables, key-value pairs) from unstructured text or search results while using chain-of-thought reasoning to validate the extraction logic. The model explicitly reasons about which fields are present, how to handle missing data, and whether the extraction is complete before returning structured output.

Evaluates claims by searching for supporting or contradicting evidence, then reasoning about the credibility of sources and the strength of evidence. The model generates explicit reasoning about source reliability, potential biases, and the confidence level of its fact-check conclusion, with full citation trails.

Searches for information about multiple entities or concepts simultaneously, then reasons about similarities, differences, and trade-offs by synthesizing evidence from multiple sources. The model generates explicit comparisons with source attribution for each claim, enabling transparent side-by-side analysis.

Analyzes code snippets or error messages, searches for relevant documentation and Stack Overflow discussions, then generates explanations or debugging suggestions grounded in current best practices and community solutions. The model reasons about the root cause while citing relevant external resources.

Searches for academic papers, articles, and reports on a topic, then synthesizes findings into a coherent narrative while maintaining explicit citation trails for each claim. The model reasons about the strength of evidence, identifies consensus vs. disagreement in sources, and flags areas of uncertainty.

Helps evaluate complex decisions by searching for relevant information about each option, reasoning about multiple factors (cost, risk, timeline, etc.), and synthesizing trade-offs into a structured analysis. The model generates explicit reasoning about decision criteria and their relative importance.

Generates technical documentation by searching for current API specifications, code examples, and best practices, then synthesizing them into coherent documentation with proper citations. The model reasons about completeness and accuracy while generating examples grounded in current library versions.