AI-based knowledge work: Deep Research with Chatgpt from Openaai: Where are the advantages and limits?

Depth research: Openai opens access and changes the landscape of knowledge work

With the gradual opening of his “Deep Research” feature, Openaai has made a remarkable step that has the potential to fundamentally change the way we know knowledge. What was once reserved for an exclusive group of pro-users is now available to a wider audience, including subscribers to Chatgpt Plus, team, education and enterprise plans. This expansion of access, albeit with monthly usage limits, signal not only the increasing maturity of this technology, but also Openai's strategic ambition, to play a leading role in the highly competitive field of AI-based information systems. The step takes place at a time when competition with companies such as Perplexity, Google, Xai and Microsoft is intensified, all of which strive to develop the next generation of tools for knowledge work.

Background and functionality of Deep Research

Genesis and core functionality

Deep Research emerged from the need to overcome the limits of conventional search methods and to initiate a new era of gaining knowledge. It was designed as a kind of “AI agent” that is able to autonomously carry out complex, multi-stage research. In essence, it is about not only finding information, but also to understand it, analyze and present it in a structured form. Deep Research uses a highly developed version of the O3 model from Openai, which has been specially optimized for the demanding tasks of web browsing and data analysis.

In contrast to the traditional chat bot modes, such as those used in GPT-4O, Deep Research is designed to operate over a longer period of time-typically between five and thirty minutes per request. During this time, it systematically searches hundreds of online sources, extracted relevant information, interprets its importance in the context of the question asked and synthesizes the results into a coherent report. This process goes far beyond the simple access of search results; It includes an active examination of the material found, the identification of patterns, contradictions and relevant connections.

Technological foundations

The performance of Deep Research is based on a combination of different advanced AI technologies. A central aspect is the "Reasoning", that is, the ability to draw logical conclusions and to understand complex facts. This enables the system to develop and adapt search strategies independently, to critically assess sources and to assess the relevance of information in the context of the respective question.

In addition, Deep Research is able to carry out Python code, which opens the door for direct data analysis. This ability is particularly valuable when it comes to processing large data records, carrying out statistical analyzes or making complex calculations. Another important building block is the ability to process custom files. Users can provide the system documents, tables or other file formats that can then be included in the research. This enables, for example, to integrate internal reports, research data or specific documentation into the analysis and thus to expand the context of research.

A decisive difference to previous models is in the training approach. Deep Research was trained by "Reinforcement Learning", whereby the focus was on real tasks that require browser and tool use. This approach differs fundamentally from the purely text -based training method, which was common in many previous language models. Through the training of real research tasks, Deep Research has learned to deal effectively with the dynamic and often unstructured information space of the Internet.

Extended access and terms of use

New user groups and chipping limits

The expansion of access to Deep Research to broader user groups marks a significant step in the democratization of this technology. Originally available exclusively for PRO users with a monthly subscription of $ 200, access was expanded to the following user groups on February 25, 2025:

Plus users ($ 20/month)

10 deep review queries per month. This enables a broad circle of users to experience the basic advantages of depth research without having to bear the high costs of a pro subscription.

Team/Enterprise/Education

10 queries per user and month. This regulation aims to provide organizations and educational institutions access and to promote the collaborative use of deep research in teams.

Pro user

Increasing the monthly deflection of 100 to 120 queries. For power users who regularly carry out extensive research, this is a welcome increase in capacity.

Resource -intensive processing: the balance between precision and efficiency

These staggered usage limits reflect the resource intensity of Deep Research. Each query is associated with considerable computing expenses, since the model works autonomously for up to 30 minutes, develops search strategies, evaluates sources and trianents results. The limiting of the queries thus serves to efficiently manage system resources and to ensure consistently high service quality for all users.

Technical improvements in the course of the expansion

In parallel to the expansion of the user group, technical improvements were also implemented, which further increase the functionality and user -friendliness of Deep Research:

1. Embedded images with quotations

Visual content from web sources is now integrated directly into the reports and provided with the corresponding sources. This enriches the reports for visual information and facilitates the understanding of complex facts, especially in areas such as science, technology or design.

2. Improved document analysis

Deep Research now has an even better understanding of uploaded files, especially PDFs and tables. This is particularly advantageous in subject -specific contexts in which users often work with specialized documents. The improved analysis ability makes it possible to extract information from these documents more precisely and to integrate into the research results.

3. increased transparency

Each report created by Deep Research contains detailed sources of source and a summary of the research steps carried out. This increases the comprehensibility of the research process and enables users to better assess the credibility of the results. Transparency is an important aspect to strengthen trust in AI-supported knowledge work and to promote responsible use of this technology.

Performance and applications in practice

Benchmark results and performance comparisons

The performance of Deep Research was demonstrated in various internal and external tests. In direct comparisons with other models, including GPT-4O and Claude 3.5, Deep Research clearly exceeded them in various benchmarks:

Humanity's Last Exam (Cais/Scale Ai)

In this demanding benchmark, which tests the general knowledge and problem-solving skills of AI systems, Deep Research achieved an accuracy of 26.6 %. For comparison: GPT-4O and Claude 3.5 only achieved 9 %. This result underlines Deep Research's superior ability to understand complex questions and provide precise answers.

Gaia benchmark

In the Gaia Benchmark, which tests the ability of AI systems to answer questions in various areas of knowledge, Deep Research took the lead in 43 out of 50 task categories. This demonstrates the broad applicability and high performance of Deep Research in different domains.

Reprogramming research

In a specific application in the field of biomedical research, Deep Research was successfully used to analyze over 200 studies on cell reprogramming in less than 30 minutes. This task, which traditionally used days or even weeks, could be mastered in the shortest possible time by using Deep Research. This illustrates the enormous potential of technology to accelerate research processes.

Competition landscape and strategic positioning

Competing solutions and unique selling points

Openai deliberately positions Deep Research in response to the growing competition in the field of AI-based knowledge work. There are various alternative solutions on the market that offer similar functionalities, but differ in certain aspects:

Google Deep Research

Integrated in Gemini Advanced (also available for $ 20/month). With Gemini Advanced, Google offers a comparable solution that also relies on Deep Research functionalities. The competition between Openaai and Google is driving innovation in this area and leads to a steady improvement in the available technologies.

Xai DeepSearch

Exclusively for GROK users (from $ 8/month). Xai, the company of Elon Musk, offers a further alternative with DeepSearch, which is bound to the GROK subscription. This shows that various actors in the AI ​​market pursue different strategies to position and market their technologies.

Microsoft Think Deeper

Available for free, but without webbrowsing functionality. With Think Deeper, Microsoft offers a free solution, which is limited in its functionality because it cannot access the Internet. This makes it clear that the ability to webbrowsing is a decisive distinction feature for deep research tools.

A significant difference between the different solutions lies in the "agent ability". While Microsoft's Think Deeper is limited to static data records, the systems of Openai and Google are able to research independently on the web and dynamically access new information. This ability to create autonomous information and processing is a central advantage of Deep Research and distinguishes it from simpler search tools.

Perplexity Deep Research

Perplexity Deep Research presents itself as a free, AI-based research platform, which enables users to quickly and interactive access to extensive, current sources of information. In contrast to conventional search tools, perplexity attaches particular importance to the transparent presentation of sources and the ability to answer complex questions in a context -related manner. By using advanced algorithms, the platform manages to extract dynamically relevant data from the web and to cover the user's information needs in real time. This combination of autonomous web research and precise processing of results makes Perplexity Deep Research an attractive instrument - especially for users who also appreciate well -founded and comprehensible information. In addition, the interactive nature of the platform enables follow -up questions to be clarified directly in the dialog and thus support an iterative research process.

Economic implications and market strategy

The price differentiation of Openai, with a plus subscription for $ 20 and a pro subscription for $ 200, is a strategic move to address both wide user groups and to bind high-performance users. The more affordable plus option enables a large audience to get to know and use the advantages of Deep Research, while the pro subscription is tailored to professional users who need extensive research and need extended functionalities.

Analysts like Paul Schell from Abi Research see this development a clear trend towards "democratizing agent -based AI". The broader availability of deep research and similar technologies has the potential to fundamentally change knowledge work and to open up new opportunities for companies and individuals. At the same time, this development also contains disruptive effects for traditional knowledge workers, whose tasks could increasingly be taken over by AI systems. The ability to effectively work with AI-supported tools and critically evaluate their results will be a key competence for knowledge workers in the future.

Security and risk management

Hallucination rates and susceptibility to errors

Despite Deep Research's impressive performance, it is important to take into account the limits and potential risks of this technology. Openai herself admits that Deep Research can draw incorrect conclusions in 3–5 % of cases or not correctly evaluate sources of authority. These "hallucinations" or errors can have different causes, for example inadequacies in the training data set, algorithmic weaknesses or the inherent complexity of the information to be processed.

An internal white paper from Openai particularly warns of the following potential sources of error:

Misinterpretation of regulatory guidelines

Deep Research may have difficulty interpreting and applying complex laws, regulations or compliance guidelines. This can be particularly problematic in highly regulated industries such as finance or healthcare.

Inadequate distinction between facts and rumors

In the dynamic information room of the Internet, it is often difficult to distinguish between secure facts and unconfirmed rumors or expressions of opinion. In some cases, Deep Research may have difficulty making this distinction reliably and possibly incorrect or misleading information in his reports.

Limits in uncertainty communication

AI systems often have difficulty communicating uncertainties and probabilities in their statements. In some cases, Deep Research could give the impression that its results are absolutely safe and flawless, although this is not always the case in reality.

Security measures and quality assurance

In order to minimize the risks and to ensure the security of Deep Research, Openai has taken various measures:

1. Red teaming campaigns

External security experts and "red teams" were commissioned to search for weaknesses and potential abuse in deep research. These tests included 12 different risk categories, including data protection, distribution of dangerous advice, discrimination and manipulation. The results of these campaigns helped Openai to identify vulnerabilities and to improve the safety precautions.

2. Automated evaluations

Openai relies on automated evaluation systems in order to continuously monitor the quality and safety of Deep Research. According to their own information, these systems achieve an accuracy of 93 % in the detection of unwanted content, such as hate speeches, propaganda or harmful information.

3. Sandboxing

Python code within Deep Research is carried out in isolated "sandbox" environments. This prevents potentially harmful code access to the overall system or causes unwanted side effects. Sandboxing is a common safety technique to minimize the risk of malware or system compromising.

Future developments and open questions

Planned functions and extensions

Openaai has already announced that Deep Research will be further developed in the coming months and expanded to include new functions. The following extensions are planned for the second quarter of 2025:

Multimodal reports

The integration of data visualizations and generated images into the reports from Deep Research. This is intended to further increase the intelligibility and meaningfulness of the reports and enable users to record complex information at a glance.

API access

The provision of a programming interface (API) for selected enterprise partners. This would enable companies to integrate Deep Research directly into their own systems and applications and to adapt the technology for specific applications. However, Openai emphasizes that the API approval will only take place as soon as the "persuasion risks" have been sufficiently clarified. This indicates that Openai takes the potential risks of deep research, especially with regard to manipulation and disinformation, very seriously.

Dynamic deflagen limits

The introduction of usage -dependent scaling for teams. This could mean that teams that Deep Research use intensively can receive more flexible deflagen simits or add additional capacities. A dynamic adaptation of the usage limits would make it easier to integrate Deep Research into their work processes.

Unexplained challenges and research needs

Despite the impressive progress, there are still open questions and challenges related to Deep Research and AI-based knowledge work in general. Critics, for example, question whether the current citation mechanisms meet scientific standards. A case study from the scientific literature analysis shows that Deep Research correctly cited relevant studies in the analysis of OCT4 protein modifications in 87 % of cases, but incurred outdated or irrelevant sources in 13 % of cases. This example makes it clear that quality assurance and the critical evaluation of the results of AI systems must continue to play an important role.

The question also remains open how the broader availability of deep research will affect the world of work and the role of knowledge workers. Will Deep Research actually transform "weekly work in minutes", as Kevin because it predicts? Or will it prove to be another AI tool with limited practical benefits? The answer to these questions will significantly depend on how companies and individuals adapt this technology and integrate them into their work processes. However, it is certain that the era of agent -based research has started and the way we know knowledge will fundamentally change.

A turning point in AI-based knowledge work

The opening of Deep Research for a wider audience marks a turning point in AI-based knowledge work. The tool offers researchers, analysts and knowledge workers in various areas of unprecedented efficiency gains and new opportunities for gaining knowledge. At the same time, important questions about quality assurance, ethical responsibility and the effects on the world of work remain. The decision of Openaai, Deep Research initially not to offer via an API, underlines the carefully handled of the company with potential abuse risks and the need to develop the technology responsibly. For organizations, the integration of such tools is increasingly becoming a competitive factor, provided that they develop the necessary skills for the critical evaluation of the results and to use this technology. The next few months and years will show whether Deep Research actually has the potential to fundamentally transform knowledge work and to initiate a new era of AI-based knowledge acquisition.

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