Sonar Deep Research is designed as an exhaustive research and reasoning model that browses hundreds of sources to assemble expert-level reports rather than act as a general chat assistant. Its primary value lies in chaining together deep search queries, citation handling, and step-by-step reasoning so the final output reads as a synthesized analysis with sourced detail. Practical use cases highlighted in the documentation include academic research, market analysis, and any workflow where a well-cited, long-form summary saves hours of manual reading. Because it treats reasoning as a first-class capability, the model is best suited for users who can frame an open-ended question and then refine prompts based on the report it returns.

The interface follows a chat-completions style endpoint that delivers a detailed report in a single response, with token accounting split across input, output, citation, search, and reasoning components so heavy research runs can be costed in advance. Pricing tracks each of those components separately per million tokens or per thousand search requests, which means deep-research sessions are more expensive than simple chat calls but remain predictable. Underlying routing lives on the Sonar platform, which the docs note is migrating toward an Agent API while keeping Sonar access available through a stated sunset date. For teams evaluating fit, the model pairs naturally with retrieval-heavy products, briefing pipelines, and analyst tooling where exhaustive sourcing matters more than conversational latency.