Perplexity: Sonar

Sonar integrates live web search capabilities that retrieve current information from the internet and return results with explicit source citations. The model performs semantic ranking of search results before synthesis, ensuring cited sources are directly relevant to the query. This architecture allows the model to answer questions about recent events, current prices, and breaking news that would be outside its training data cutoff.

Sonar allows developers to specify which domains, content types, or source categories the model should prioritize or exclude when performing web searches. This filtering is applied at the search orchestration layer before synthesis, enabling domain-specific Q&A systems that respect source hierarchies (e.g., prioritizing academic papers over blogs, or excluding certain news outlets). The filtering logic operates on URL patterns and metadata tags rather than post-hoc content filtering.

Sonar is architected as a smaller, distilled model optimized for latency and cost efficiency compared to larger flagship models. It uses quantization and architectural pruning to reduce parameter count while maintaining reasoning capability for Q&A tasks. The model is designed to run inference quickly on Perplexity's infrastructure, with pricing structured to incentivize high-volume, low-cost queries suitable for production applications.

Sonar supports streaming responses where the synthesized answer is delivered token-by-token as it is generated, with citations appearing inline or in a separate metadata stream. This allows client applications to display answers progressively to users without waiting for the full response to complete. The streaming architecture maintains citation fidelity by buffering source metadata until relevant tokens are emitted.

Sonar supports multi-turn conversations where previous messages and their citations are retained in context for subsequent queries. The model uses conversation history to disambiguate follow-up questions and maintain coherence across turns. The architecture preserves source citations from previous turns, allowing users to reference earlier cited sources without re-searching.

Sonar is accessible through OpenRouter's unified API abstraction layer, which provides a standardized interface for calling Perplexity models alongside other LLM providers (OpenAI, Anthropic, etc.). OpenRouter handles authentication, rate limiting, and provider failover, allowing developers to swap between models without changing client code. The integration uses OpenRouter's standard message format and streaming protocol.

Sonar synthesizes answers from web search results and includes source citations that can be verified by following the provided URLs. The model performs implicit source credibility assessment during synthesis, prioritizing information from authoritative sources. The architecture includes mechanisms to detect and downweight contradictory sources, reducing the likelihood of returning conflicting information.