OpenAI expands Deep Research access to Plus users, heating up AI agent wars with DeepSeek and Claude

Openai Announced today to be launching its powerful In-depth research The ability of everyone Chatgpt Plus,,,,, team,,,,, educate and enterprise Users, greatly expanding access to the company’s most transformative AI agents that many experts believe have been the company since the original CHATGPT.

According to Openai’s announcement Official X AccountAdditionally, team, education and enterprise users will initially receive 10 in-depth research queries per month, while professional subscribers will access 120 queries per month.

In-depth research, powered by Openai’s upcoming professional version O3 modelrepresents a significant shift in how AI can assist complex research tasks. Unlike traditional chatbots that provide instant response, dig deep into independent searches for hundreds of online resources, analyze text, images and PDFs, and synthesize comprehensive reports that are comparable to comprehensive reports produced by professional analysts.

In-depth research is being launched to all Chatgpt Plus, Team, Edu and Enterprise users?

– Openai (@openai) February 25, 2025

AI Research Weapons Competition: DeepSeek’s Open Challenge complies with Openai’s advanced game

There is little chance that the Openai expansion will be launched. The generated AI landscape has changed dramatically in recent weeks, with the Chinese landscape DeepSeek Become an unexpected destroyer. By open source DeepSeek-R1 model In a MIT Licensethe company fundamentally challenged the closed subscription-based business model that has been defined by Western AI development.

What makes this competition particularly interesting is the different philosophies that work. Although Openai continues to master its most powerful features behind the increasing complexity Subscription layerDeepSeek chose a fundamentally different approach: abandoning technology and leaving a thousand applications open.

Chinese artificial intelligence company DeepSeek recently announced that R1 is an open source inference model that claims to have comparable performance to OpenAI’s O1, at a very small cost.

But for those who follow AI development, DeepSeek and R1 are not… pic.twitter.com/fuahyp0hhz

– Y Combinator (@ycombinator) February 5, 2025

This strategy echoes an earlier era of technology adoption, in which open platforms eventually create more value than closed systems. Linux’s dominance in server infrastructure provides a compelling historical parallel. For business decision makers, the question is whether to invest in proprietary solutions that may provide competitive advantages, or adopt open alternatives that can promote wider innovation in their organizations.

Puzzled Recent integration DeepSeek-R1 enters its own research tool (in a small part of the Openai price point) to prove how fast this open approach can produce competitive products. At the same time, humans Claude 3.7 sonnet Another approach was taken, focusing on “visible extended thinking” to be transparent in its reasoning process.

DeepSeek’s R1 is an impressive model, especially around how they can deliver at a price.

Obviously, we will provide better models and have the legitimacy of new competitors! We will extract some versions.

– Sam Altman (@sama) January 28, 2025

The result is a fragmented market, with each major player now offering a unique approach to AI-driven research. This means a bigger choice for businesses, but also adds complexity when determining which platforms best align with their specific needs and values.

From walled gardens to public squares: Openai’s democratic hub for computing

When Sam Altman writes in-depth researchFor some users, it may be worth $1,000 per month,” he reveals more than just price elasticity — he acknowledges extraordinary value differences among potential users. This admission cuts the core of Openai’s ongoing strategic balancing behavior.

The company faces fundamental tensions: maintaining premium exclusivity that fundes its development while fulfilling its mission to ensure that “artificial universal intelligence is beneficial to all humanity.” Today’s announcement represents a cautious step towards greater accessibility without undermining its revenue model.

I think we will initially offer 10 uses for Chatgpt Plus every month, and 2 free tiers per month with the intention to expand those uses over time.

For some users it may be worth $1000 per month, but I’m glad to see what everyone does about it! https://t.co/ybicvzodpf

– Sam Altman (@sama) February 12, 2025

By limiting the free tier users to two queries per month, OpenAI essentially provides trailers that prove the capabilities of the technology without swallowing up its premium products. This approach follows a classic “free value added” script that defines most of the digital economy but has exceptionally tight constraints that reflect the large amount of computing resources required for each in-depth study query.

distribute Add 10 monthly query from users ($20 per month), while Pro users create a clear demarcation for $120 per month ($200 per month) that retains the premium value proposition. This stratified rollout strategy shows that OpenAI recognizes that democratizing advanced AI capabilities requires not only reducing price barriers, but also rethinking how these features are packaged and delivered.

Beyond the surface: Hidden advantages and surprising vulnerabilities of deep research

Title diagram – “26.6% accuracy”The final exam for humans” – Telling only part of the story. This benchmark, which is a challenging one even for human experts, represents a quantum leap beyond previous AI capabilities. In context, a year ago, even reaching 10% in this test.

The most important thing is not only the original performance, but also the nature of the test itself, which requires comprehensive information across different fields and the application of subtle reasoning that goes far beyond pattern matching. Deep Research’s approach combines several technological breakthroughs: multi-stage planning, adaptive information retrieval, and perhaps most critical is a form of computational self-correction that enables it to identify and correct its own limitations during the research process.

However, these features carry famous blind spots. The system is still susceptible to what is called “Consensus bias” – A tendency toward a uniquely widely accepted perspective while potentially ignoring the counter-trend view of challenging thinking. This bias may be especially problematic in areas where challenging traditional wisdom often emerges in innovation.

Furthermore, the system’s reliance on existing web content means that it inherits the biases and limitations of its original material. In the fast-growing field or niche specialization of limited online documentation, in-depth research can be difficult to provide a truly comprehensive analysis. And, without access to proprietary databases or subscription-based academic journals, its insight into certain areas of expertise may remain superficial despite its complex reasoning capabilities.

The Dilemma of Executives: Deeply Studying How to Rewrite the Rules of Knowledge Work

For C-Suite leaders, in-depth research raises a paradox: it is a tool that is sufficient to redefine the role of an entire organization, but is still too limited to deploy without careful human supervision. Direct productivity gains are undeniable – tasks that once took several days of analyst time can now be completed in minutes. But this efficiency has complex strategic significance.

Effectively integrating in-depth research organizations may need to completely reimagine their information workflow. Not only does this technology replace junior analysts, it can also create new hybrid roles where human expertise focuses on framework issues, evaluates sources and conducts rigorous evaluation of AI-generated insights. The most successful implementation may see in-depth research as a replacement for human judgment, but an amplifier of human capabilities.

In-depth research by Chatgpt Plus users!

One of my favorite things.

– Sam Altman (@sama) February 25, 2025

Pricing structures create their own strategic considerations. For professional users with 120 queries, $200 per month, the valid price per query is about $1.67, a trivial fee compared to labor costs. However, limited amounts create artificial scarcity, forcing organizations to prioritize what issues are truly worthy of in-depth research. Ironically, this constraint may lead to the application of technology to technology than purely infinite models will encourage.

The long-term meaning is more profound. As research capabilities once limited to elite organizations become widely accessible, competitive advantages will increasingly be gained from access to information, but rather from how organizations structure problems and integrate AI-generated insights into their decision-making processes. Strategic value shifts from understanding to understanding – from information gathering to insight.

For technology leaders, the message is clear: the AI ​​research revolution is no longer here – right here. The question is not whether to adapt or not, but the processes, skills and cultural thinking needed by organizations to thrive in-depth research that is fundamentally democratized.