Search API: Better extraction, dynamic benchmarks

Perplexity has focused recent Search API development on improving snippet relevance and size through extraction, span-level labeling, and evaluation systems that identify essential, irrelevant, and duplicate document segments for each query. These changes enable shorter, more useful snippets, lower response latency and token costs, and improved downstream LLM accuracy, while expanding support for structured formats such as tables, nested lists, and dynamically rendered content. The company also added the time-sensitive SEAL benchmark to its open-source search_evals framework, reporting improved performance on SEAL-Hard compared with declining results from other providers in tests using Claude Sonnet 4.5. Additional API updates include support for up to five queries per request, language and regional filtering alongside existing domain and recency filters, and native Search API support in the Python SDK.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.