OpenAI Reveals ChatGPT's Multi-Model Deep Research Process, Highlighting Complexity Behind the Scenes

The introduction of the OpenAI API for Deep Research has provided unprecedented insights into the inner workings of ChatGPT, revealing a complex and layered process behind its user-friendly interface. According to the API documentation, a significant amount of activity takes place behind the scenes, highlighting the need for caution in enterprise implementations where the underlying functionality of an API remains largely opaque. Numerous studies have shown that AI models behind commercial APIs can change and drift over time, leaving users at the mercy of the model providers. This transparency from OpenAI is a valuable step, as it allows developers and researchers to better understand and manage these dynamics. The sequence for a Deep Research query on ChatGPT, as detailed in the publication, demonstrates the intricate process involving multiple model calls. Contrary to its simple and intuitive GUI, which aims to abstract away complexity, ChatGPT employs a sophisticated workflow: Helper Model: Before initiating the research, a lightweight model (such as GPT-4.1) clarifies the user’s intent, collecting preferences or goals. This helps customize web searches to yield more relevant results. Prompt Rewriter: Another lightweight model (like GPT-4.1) expands or specifies user queries, ensuring they are optimized for the research model. Deep Research Model: Finally, the main research model processes the refined queries to generate detailed responses. This multi-model orchestration is designed to enhance the accuracy and relevance of ChatGPT’s outputs. However, this complexity is hidden from the end user, emphasizing an important principle in AI design: complexity must reside somewhere—either visible to the user or managed behind the scenes. By handling this complexity internally, OpenAI offloads the burden of orchestrating the user experience to the platform itself, rather than to individual users or developers. While the Deep Research API does not include the initial helper and prompt rewriter steps, developers can still choose to implement their own versions of these functionalities. This flexibility allows users to customize the workflow to better suit their specific needs. Understanding the multi-model approach of ChatGPT is crucial for advancing AI practices. It suggests that a single, monolithic model is often not the best solution, and that leveraging smaller, specialized models can enhance performance. For instance, NVIDIA has demonstrated a method where a language model is trained to accurately identify the appropriate tool for each step or sub-step in a task, further exemplifying the benefits of modular AI systems. In summary, the OpenAI API for Deep Research provides a window into the sophisticated machinery driving ChatGPT, underscoring the importance of transparent and customizable AI solutions in both enterprise and research settings. This approach not only improves the user experience but also offers valuable lessons for developers looking to build more effective and flexible AI systems.