What factors influence how long DeepResearch takes to complete a research query?

The time DeepResearch takes to complete a query depends on three main factors: the complexity of the query, the processing steps required, and the infrastructure handling the workload. Each factor introduces variables that can either speed up or slow down the system’s response, depending on how they’re configured or optimized.

First, the complexity and scope of the query directly impact execution time. For example, a query asking for a simple fact, like “population of Canada,” requires minimal data retrieval and cross-referencing. In contrast, a query like “compare the economic impact of AI regulation in the EU and U.S. over the past five years” demands aggregating data from multiple sources, filtering irrelevant information, and synthesizing results. Additionally, if the query involves real-time data (e.g., stock prices) versus static datasets (e.g., historical census data), the need to fetch live updates introduces delays. The number of data sources and their response times—such as slow APIs or rate-limited databases—can create bottlenecks, especially if the system waits for external services to reply.

Second, processing steps like data transformation, analysis, and formatting affect duration. For instance, queries requiring natural language processing (NLP) to interpret ambiguous terms or sentiment analysis to gauge public opinion add computational overhead. Machine learning models used for tasks like summarization or trend detection also vary in speed; a lightweight model might process data in milliseconds, while a larger, more accurate model could take seconds. Data volume plays a role too: parsing 10,000 documents takes longer than 100. Developers can optimize this by caching intermediate results or pre-indexing frequently accessed data, but these optimizations depend on the use case.

Finally, infrastructure and resource allocation determine baseline performance. A query running on a single server with limited RAM will process slower than one distributed across a cluster. Network latency between services (e.g., database calls, API gateways) adds overhead, especially in cloud environments. Concurrent queries competing for resources—like CPU time or database connections—can queue up, increasing wait times. For example, a system handling 100 simultaneous queries might prioritize them using a scheduler, but if the hardware is undersized, delays compound. Scaling horizontally (adding more servers) or vertically (upgrading hardware) can mitigate this, but requires balancing cost and performance.