Artificial intelligence has revolutionized the way we access information, interact with technology, and automate tasks. Two prominent AI-driven platforms, Bigseek and ChatGPT, have emerged as key players in this space, but they serve different purposes and operate on distinct technologies. While both use AI to enhance user experiences, they function differently based on their core design and intended applications.
It has been a whirlwind week for artificial intelligence, with DeepSeek shaking up the AI landscape by launching its R1 reasoning model. Not only did DeepSeek make the model freely available as a chatbot, but it also provided developers with highly cost-effective API access, significantly disrupting the market.
Shortly after, DeepSeek faced cyber attacks that briefly took it offline, though the platform now appears to be fully operational again. To round off the week, OpenAI responded by introducing its o3-mini and o3-mini-high reasoning models, making them accessible across all its subscription tiers, including Plus, Pro, and even the free version of ChatGPT.
For those using ChatGPT’s free tier, accessing o3-mini is straightforward. Simply update your ChatGPT mobile app, then tap the new “Reason” button next to the message box’s search bar. The same feature is available in the web browser version of ChatGPT, functioning seamlessly across both platforms.
Reasoning models excel at tackling complex coding challenges and solving intricate mathematical problems. However, for most users, chatbots serve a more practical purpose—providing quick, accurate responses to everyday questions. With the release of OpenAI’s o3-mini and DeepSeek-R1, both freely accessible, I was eager to see how they compare in real-world scenarios.
Curious about their reasoning capabilities, I wasted no time in putting them to the test. I crafted a series of challenging, thought-provoking questions—the kind that require more than just regurgitating information. Would o3-mini hold its own against DeepSeek-R1, or would one prove superior in handling complex queries? It was time to find out.
Pro Advice
The first test I gave o3-mini was a classic life decision—whether to start a career straight out of high school or pursue a university degree. This type of question requires nuanced reasoning, as it involves weighing multiple factors such as career aspirations, financial situation, personal interests, and long-term goals. I figured it would be a solid way to assess how well these two free reasoning models process complex, real-world dilemmas.
Both models provided decent answers, but what truly stood out was how they presented their reasoning—and the difference was striking.
ChatGPT o3-mini: Concise and Focused Reasoning
When I posed the question to o3-mini, it took a few seconds to process the dilemma and briefly shared insight into its thinking before delivering a balanced, well-structured response. It initially stated:
“I’m weighing the decision between starting a career now or pursuing further education. Need to gather more details, like goals and specific circumstances, before giving any advice.”
Then, it expanded:
“I’m evaluating fields’ requirements, considering interests, preferences, finances, career goals, and job market. Mentorship and research are pivotal. Personal context is crucial for an informed decision.”
After this brief preview of its reasoning, it provided a structured and balanced response outlining the pros and cons of both paths, emphasizing personal priorities and market conditions. The approach was efficient, logical, and easy to digest—ideal for users who want a quick yet thoughtful answer without unnecessary elaboration.
DeepSeek-R1: A Transparent, Almost Overwhelming Thought Process
DeepSeek-R1, on the other hand, took a completely different approach. Instead of just summarizing its reasoning before answering, it walked me through every step of its thought process in extreme detail—almost like an internal monologue.
For instance, it started questioning itself out loud:
“Wait, but how do I know what I want? Maybe I should list out the pros and cons.”
As it continued, it added:
“What about passion? Am I excited about a particular field of study, or am I more eager to get into the workforce? If I’m not sure what to study, maybe working for a while could help me figure that out before committing to a degree.”
This stream-of-consciousness style response was incredibly transparent, offering deep insight into how the AI weighs options and makes decisions. However, it bordered on overthinking, almost as if I were listening to someone verbalizing every single thought before making up their mind.
The Key Takeaway
- o3-mini delivers a concise yet structured reasoning process, giving just enough insight before presenting a clear, practical answer.
- DeepSeek-R1 exposes every step of its decision-making, offering a raw, sometimes overwhelming look into its thought process.
Both approaches have their merits. If you want a straightforward, efficient answer, o3-mini is the way to go. But if you’re curious about AI reasoning and enjoy seeing the full cognitive breakdown, DeepSeek-R1 provides a fascinating (and sometimes exhausting) experience.
Both models followed a structured approach in delivering their final answers, typically breaking down responses into bullet points and categorized sections before providing a summary. This style of response is characteristic of deep reasoning models, which aim to lay out their thought processes clearly, in contrast to models like ChatGPT-4o, which tend to give concise, natural-sounding answers without explicitly showing how they arrived at them.
Consistent Trends Across Multiple Queries
I didn’t stop at just one question—throughout my testing, the pattern remained the same across most queries.
- ChatGPT o3-mini streamlines its reasoning, offering a high-level overview of the thought process before jumping into the answer.
- DeepSeek-R1, on the other hand, exposes every step of its reasoning, expanding on multiple considerations before arriving at a conclusion.
For those who prefer a quick, efficient response, o3-mini is the clear winner. But if you really want to see how the model processes a question in intricate detail, DeepSeek-R1 feels like the “full-service” reasoning experience—providing every angle, every doubt, and every possibility before locking in an answer.
Performance in Different Domains
Interestingly, I’ve seen reports that o3-mini outperforms DeepSeek-R1 in physics simulations and complex geometric reasoning tasks, making it a stronger choice for scientific and technical applications. However, when it comes to simpler, everyday reasoning questions, I found myself leaning toward DeepSeek-R1—not because its answers were necessarily better, but because the sheer depth of its reasoning process felt more engaging.
At the end of the day, which model is “better” depends entirely on what you need—whether it’s concise efficiency or an AI that walks you through every single step of its thought process.
