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It’s here: OpenAI’s o3-mini advanced reasoning model arrives to counter DeepSeek’s rise

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OpenAI has released a new proprietary AI model in time to counter the rapid rise of open source rival DeepSeek R1 — but will it be enough to blunt the latter’s success?

Today, after several days of rumors and increasing anticipation among AI users on social media, OpenAl is debuting o3-mini, the second model in its new family of “reasoners,” Al models that take slightly more time to “think,” analyze their own processes and reflect on their own “chains of thought” before responding to user queries and inputs with new outputs.

The result is a model that can perform at the level of a PhD student or even degree holder on answering hard questions in math, science, engineering and many other fields.

The o3-mini model is now available on ChatGPT, including the free tier, and OpenAI’s application programming interface (API), and it’s actually less expensive, faster, and more performant than the previous high-end model, OpenAI’s o1 and its faster, lower-parameter count sibling, o1-mini.

While inevitably it will be compared to DeepSeek R1, and the release date seen as a reaction, it’s important to remember that o3 and o3-mini were announced well prior to the January release of DeepSeek R1, in December 2024 — and that OpenAI CEO Sam Altman stated previously on X that due to feedback from developers and researchers, it would be coming to ChatGPT and the OpenAI API at the same time.

Unlike DeepSeek R1, o3-mini will not be made available as an open source model — meaning the code cannot be taken and downloaded for offline usage, nor customized to the same extent, which may limit its appeal compared to DeepSeek R1 for some applications.

OpenAI did not provide any further details about the (presumed) larger o3 model announced back in December alongside o3-mini. At that time, OpenAI’s opt-in dropdown form for testing o3 stated that it would undergo a “delay of multiple weeks” before third-parties could test it.

Performance and Features

Similar to o1, OpenAI o3-mini is optimized for reasoning in math, coding, and science.

Its performance is comparable to OpenAI o1 when using medium reasoning effort, but offers the following advantages:

  • 24% faster response times compared to o1-mini (OpenAI didn’t provide a specific number here, but looking at third-party evaluation group Artificial Analysis’s tests, o1-mini’s response time is 12.8 seconds to receive and output 100 tokens. So for o3-mini, a 24% speed bump would drop the response time down to 10.32 seconds.)
  • Improved accuracy, with external testers preferring o3-mini’s responses 56% of the time.
  • 39% fewer major errors on complex real-world questions.
  • Better performance in coding and STEM tasks, particularly when using high reasoning effort.
  • Three reasoning effort levels (low, medium, and high), allowing users and developers to balance accuracy and speed.

It also boasts impressive benchmarks, even outpacing o1 in some cases, according to the o3-mini System Card OpenAI released online (and which was published earlier than the official model availability announcement).

o3-mini’s context window — the number of combined tokens it can input/output in a single interaction — is 200,000, with a maximum of 100,000 in each output. That’s the same as the full o1 model and outperforms DeepSeek R1’s context window of around 128,000/130,000 tokens. But it is far below Google Gemini 2.0 Flash Thinking’s new context window of up to 1 million tokens.

While o3-mini focuses on reasoning capabilities, it doesn’t have vision capabilities yet. Developers and users looking to upload images and files should keep using o1 in the meantime.

The competition heats up

The arrival of o3-mini marks the first time OpenAI is making a reasoning model available to free ChatGPT users. The prior o1 model family was only available to paying subscribers of the ChatGPT Plus, Pro and other plans, as well as via OpenAI’s paid application programming interface.

As it did with large language model (LLM)-powered chatbots via the launch of ChatGPT in November 2022, OpenAI essentially created the entire category of reasoning models back in September 2024 when it first unveiled o1, a new class of models with a new training regime and architecture.

But OpenAI, in keeping with its recent history, did not make o1 open source, contrary to its name and original founding mission. Instead, it kept the model’s code proprietary.

And over the last two weeks, o1 has been overshadowed by Chinese AI startup DeepSeek, which launched R1, a rival, highly efficient, largely open-source reasoning model freely available to take, retrain, and customize by anyone around the world, as well as use for free on DeepSeek’s website and mobile app — a model reportedly trained at a fraction of the cost of o1 and other LLMs from top labs.

DeepSeek R1’s permissive MIT Licensing terms, free app/website for consumers, and decision to make R1’s codebase freely available to take and modify has led it to a veritable explosion of usage both in the consumer and enterprise markets — even OpenAI investor Microsoft and Anthropic backer Amazon rushing to add variants of it to their cloud marketplaces. Perplexity, the AI search company, also quickly added a variant of it for users.

DeepSeek also dethroned the ChatGPT iOS app for the number one place in the U.S. Apple App Store, and is notable for outpacing OpenAI by connecting its R1 model to web search in its app and on the web, something that OpenAI has not yet done for o1, leading to further techno anxiety among tech workers and others online that China is catching up or has outpaced the U.S. in AI innovation — even technology more generally.

Many AI researchers and scientists and top VCs such as Marc Andreessen, however, have welcomed the rise of DeepSeek and its open sourcing in particular as a tide that lifts all boats in the AI field, increasing the intelligence available to everyone while reducing costs.

Availability in ChatGPT

The model is now rolling out globally to Free, Plus, Team, and Pro users, with Enterprise and Education access coming next week.

  • Free users can try o3-mini for the first time by selecting the “Reason” button in the chat bar or regenerating a response.
  • Message limits have increased 3X for Plus and Team users, up from 50 to 150 messages per day.
  • Pro users get unlimited access to both o3-mini and a new, even higher-reasoning variant, o3-mini-high.

Additionally, o3-mini now supports search integration within ChatGPT, providing responses with relevant web links. This feature is still in its early stages as OpenAI refines search capabilities across its reasoning models.

API Integration and Pricing

For developers, o3-mini is available via the Chat Completions API, Assistants API, and Batch API. The model supports function calling, Structured Outputs, and developer messages, making it easy to integrate into real-world applications.

One of o3-mini’s most notable advantages is its cost efficiency: It’s 63% cheaper than OpenAI o1-mini and 93% cheaper than the full o1 model, priced at $1.10/$4.40 per million tokens in/out (with a 50% cache discount).

Yet it still pales in comparison to the affordability of the official DeepSeek API‘s offering of R1 at $0.14/$0.55 per million tokens in/out. But given DeepSeek is based in China and comes with attendant geopolitical awareness and security concerns about the user/enterprise’s data flowing into and out of the model, it’s likely that OpenAI will remain the preferred API for some security-focused customers and enterprises in the U.S. and Europe.

Developers can also adjust the reasoning effort level (low, medium, high) based on their application needs, allowing for more control over latency and accuracy trade-offs.

On safety, OpenAI says it used something called “deliberative alignment” with o3-mini. This means the model was asked to reason about the human-authored safety guidelines it was given, understand more of their intent and the harms they are designed to prevent, and come up with its own ways of ensuring those harms are prevented. OpenAI says it allows the model to be less censorious when discussing sensitive topics while also preserving safety.

OpenAI says the model outperforms GPT-4o in handling safety and jailbreak challenges, and that it conducted extensive external safety testing prior to release today.

A recent report covered in Wired (where my wife works) showed that DeepSeek succumbed to every jailbreak prompt and attempt out of 50 tested by security researchers, which may give OpenAI o3-mini the edge over DeepSeek R1 in cases where security and safety are paramount.

What’s next?

The launch of o3-mini represents OpenAI’s broader effort to make advanced reasoning AI more accessible and cost-effective in the face of more intense competition than ever before from DeepSeek’s R1 and others, such as Google, which recently released a free version of its own rival reasoning model Gemini 2 Flash Thinking with an expanded input context of up to 1 million tokens.

With its focus on STEM reasoning and affordability, OpenAI aims to expand the reach of AI-driven problem-solving in both consumer and developer applications.

But as the company becomes more ambitious than ever in its aims — recently announcing a $500 billion data center infrastructure project called Stargate with backing from Softbank — the question remains whether or not its strategy will pay off well enough to justify the multibillions sunken into it by deep-pocketed investors such as Microsoft and other VCs.

As open source models increasingly close the gap with OpenAI in performance and outmatch it in cost, will its reportedly superior safety measures, powerful capabilities, easy-to-use API and user-friendly interfaces be enough to maintain customers — especially in the enterprise — who may prioritize cost and efficiency over these attributes? We’ll be reporting on the developments as they unfold.

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Q1 Executive Roundtable Recap

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

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Executive Roundtable: The AI Infrastructure Credibility Test

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Superconducting the AI Era: Rethinking Power Delivery for Gigawatt Data Centers

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DCF Poll: AI Data Center Assumptions

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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