Introducing research models with Basis for the Parallel Chat API

Starting today, the Parallel Chat API introduces three new research models, enhancing interactive AI applications with research-grade web intelligence and full Basis verification. It now offers two modes: a speed model for quick, low-latency responses and research models (Lite, Base, Core) that prioritize comprehensive, deeply verified outputs over speed. Both modes return OpenAI ChatCompletions-compatible streaming text and JSON. The research models, powered by the same processors as the Task API, utilize the Basis verification framework, providing citations, reasoning, excerpts, and calibrated confidence scores to ensure reliable outputs. These models are designed for tasks requiring extensive reasoning and verification from primary sources, offering high-confidence outputs with significantly lower error rates. Parallel Web Systems, which powers these APIs, provides critical web search infrastructure that transforms lengthy human tasks into efficient agentic tasks, used by Fortune 100 and 500 companies and AI-native businesses to automate business functions in sectors like insurance, finance, and retail.

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