What is the typical length or detail of a report generated by DeepResearch, and can this be adjusted or controlled?

DeepResearch generates reports that typically range from 1,000 to 1,500 words, structured to balance depth with clarity. These reports often include sections like an introduction, methodology, key findings, and actionable recommendations. For example, a report analyzing software performance might break down metrics like latency, error rates, and resource usage, accompanied by code snippets or configuration examples. The default structure aims to provide enough detail for developers to understand technical trade-offs without overwhelming them with unnecessary information. However, this baseline can vary slightly depending on the complexity of the query or dataset provided.

The length and detail of reports can be adjusted using parameters or settings in the tool’s interface or API. Developers can specify constraints like max_length to cap the word count or detail_level (e.g., “brief,” “standard,” or “comprehensive”) to control analytical depth. For instance, a “brief” report might exclude methodology details and focus on high-level conclusions, while a “comprehensive” report could include raw data tables, extended code analysis, or comparisons between multiple frameworks. Users can also define sections to include or exclude—like omitting an executive summary for internal technical reviews—or request appendices with additional datasets or debugging steps. These controls are often exposed as JSON or YAML configurations in API requests, allowing integration into automated workflows.

Adjustments are particularly useful for tailoring outputs to specific use cases. A developer troubleshooting a production issue might request a 500-word report focused solely on error root causes and mitigation steps, skipping broader context. Conversely, a team evaluating architecture choices might need a 2,000-word analysis comparing cloud services, including cost-benefit breakdowns and scalability tests. The system’s flexibility ensures that outputs align with priorities like time constraints or audience expertise. For example, adding include_code_samples: true could append relevant code snippets to illustrate optimization techniques, while technical_depth: advanced might trigger deeper discussions of algorithms or protocol-level interactions. This adaptability makes the tool practical for both quick audits and in-depth technical reviews.