Sonar is Perplexity's lightweight search model. It combines language generation with built-in web search to deliver citation-backed answers at low cost within a context window of 127K tokens.
import { streamText } from 'ai'
const result = streamText({ model: 'perplexity/sonar', prompt: 'Why is the sky blue?'})Sonar is the base tier of Perplexity's Sonar API family. It's built for developers who need web-grounded answers without building a retrieval pipeline. Unlike conventional language models that generate from training data alone, Sonar models run a live web search on every inference call and synthesize results into a cited response.
This built-in search is Perplexity's core differentiator. You don't need to configure a separate search API, build a RAG pipeline, or manage document indexing. The model handles query formulation, web retrieval, source evaluation, and answer synthesis in one request. Responses include inline citations pointing to sources, giving your application verifiable provenance for each claim.
Sonar targets high-volume use cases where cited answers matter but deep reasoning isn't required. It has the lowest per-token cost in the Sonar family. That makes it practical for customer-facing search, chatbots that need factual accuracy, and automated research assistants processing many queries. The context window of 127K tokens supports multi-turn conversations that accumulate search context across exchanges.