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Perplexity Brings Deep Research into Computer with Search as Code Architecture

Perplexity integrated Deep Research into its Computer platform, routing research subtasks across 20+ frontier models through a new Search as Code architecture that runs thousands of parallel retrieval steps per query.

Perplexity Deep Research benchmark comparing Legacy vs Computer across three tests
Credit: Perplexity

Perplexity released Deep Research inside its Computer platform on June 11, combining iterative multi-model search with computer-side task execution so that research and output production happen in a single workflow rather than across two separate sessions.

The change addresses a workflow friction the company says its data confirms matters: research and analysis account for 26% of Computer tasks, the single largest category ahead of coding and asset creation, per Perplexity. Previously, users had to run a Deep Research query in one thread, then open a separate Computer thread to turn findings into a document, slide deck, or dashboard. The integration removes that handoff: a single query now routes through 20 or more frontier models, breaks into parallel subtasks, and produces work-ready output in one pass.

The technical piece behind the integration is what Perplexity calls Search as Code. Instead of issuing one query at a time, Computer writes and executes code that designs a custom search program for each question, running potentially thousands of retrieval steps in parallel, evaluating source quality mid-run, and adjusting the search plan when early results fall short. The code runs in a secure sandbox alongside the model, so the model can see arriving results and change course before synthesis. Perplexity says the Perplexity Computer architecture scores higher on Humanity's Last Exam, BrowseComp, and DeepSearchQA compared to the prior separate-tool version, with improvements across factual accuracy, depth of analysis, and citation quality.

On the source side, Deep Research in Computer can pull from authorized internal connectors including files, apps, and connected data sources, alongside the live web. Premium data sources such as Statista, PitchBook, and CB Insights are automatically invoked when the query warrants it. Every claim in the Perplexity output carries an inline citation linked to a live source URL.

The practical implication for enterprise teams is that a complex multi-part research task runs through a single Computer session rather than requiring a separate research step followed by a drafting step. Perplexity's example in the announcement traces a buy-side diligence brief through parallel analyses of four separate jurisdictions, each producing a risk assessment reconciled into a single memo. How much of the benchmark improvement is attributable to the Search as Code architecture versus the simpler workflow consolidation will become clearer as third-party evaluations accumulate.

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Julian Beaumont

Julian Beaumont covers artificial intelligence and large language models for techshooked, following the path from research paper to deployed feature. His standard is anti-hype: ask what a model actually does, what trained it, how it fails, and whether a benchmark measures what the announcement claims.