Introducing Fast mode for Parallel Search

Parallel introduced Fast mode for its AI-focused web search service, positioning it as a lower-cost option for agent workflows, customer support, factual question answering, and general search. The company states that Fast mode costs $1 per 1,000 results, delivers roughly 700 ms average latency, and achieved a score of 73 on Artificial Analysis’s Search Index, compared with 75 for its higher-quality Advanced mode; it also says the same model scored 33 without search. The announcement argues that rapidly falling model costs, including an asserted 80% reduction in GPT 5.6 Luna pricing, have made search a larger share of end-to-end AI agent expenses, making low-cost search increasingly important. Parallel claims Fast can reduce total agent costs substantially compared with Brave, Exa, and Tavily search offerings, while its Turbo mode targets lowest latency and Advanced mode targets maximum quality. Developers can enable Fast through a single API parameter change by setting the search mode to “fast.”

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