DeepSeek R1 vs GPT-o1: The AI Showdown Businesses Need to See

What if I told you there's a new AI disruptor that's open source, 27 times cheaper, and just as powerful as the biggest players in the industry? Meet DeepSeek R1. It's shaking up the AI world, but how does it stack up compared to OpenAI's GPT-01? And more importantly, could it revolutionize how businesses like yours grow and innovate? Let's dive in and find out. Hi, I'm Polly, and welcome to the AI Accelerant, your go-to channel for exploring the latest AI technologies and how they can supercharge your business. Whether you're a founder, creator, or tech enthusiast, I'm here to show you how AI can help you automate your workflows, boost conversions, and unlock new opportunities for growth. Today, we're diving into a head-to-head comparison of 2 groundbreaking AI reasoning models. DeepSeek R1 and OpenAI's GPT-01. But stick around as I put DeepSeek R1 to the test in practical business and marketing use cases, so you can see exactly how this open-source disruptor could transform your business.

All right, let's talk about DeepSeek R1, the AI model that's taking the industry by storm.

Developed by a Chinese AI startup, This reasoning model is fully open source and completely free under an MIT license. That's huge for startups and SMBs looking to innovate without breaking the bank. But first, what exactly is an AI reasoning model? Think of it as a problem solver. Unlike general AI models that provide quick answers, reasoning models break down complex tasks into smaller logical steps. Analyze each part, and generate thoughtful, layered solutions. For example, they can optimize workflows, analyze datasets, or even plan logistics, handling tasks that require serious logical thinking and decision-making. Until recently, OpenAI's GPT-01 dominated the reasoning model landscape, with Claude 3.5 Sonnet as its closest competitor. But with the introduction of DeepSeek R1, the game has changed. This new model not only matches the performance of GPT-01, but also redefines accessibility through its open-source approach and groundbreaking affordability. Okay, so now let's get into the numbers, because this is where DeepSeek R1 really shines. It's not just hype, its performance speaks for itself. Okay, so when we look at this chart over here, we see that for mathematics on the AIME, which stands for American Invitational Mathematics Examination, DeepSeek R1 scored 79.8%, outperforming GPT-01 by more than half a percentage point and proving its ability to handle complex mathematical problems with precision. On Math 500, DeepSeek R1 achieved an impressive 97.3%, again beating GPT-01's 96.4%. And then when we look at the coding benchmarks for programming like Codeforces, DeepSeek R1 is in the 96.3rd percentile, which is close to the 01 model. Both DeepSeek and 01 beat the scores for expert human coders who averaged around the 90th percentile. And for the software engineering benchmark called SWE, DeepSeek R1 scores 49.2%, which outperforms O1's 48.9%. For the general knowledge GPQA benchmark, which stands for general purpose question answering, DeepSeek R1 scored 71.5%, which is below O1 but still at an impressive score compared to other models. This shows the model's competence in tasks requiring specialized knowledge and nuanced responses. On the MMLU, a test for multitask language understanding, DeepSeek R1 scores 90.8%, just shy of GPT-01's 91.8%. These results make DeepSeek R1 a powerhouse for businesses handling data-heavy or reasoning-intensive challenges. Now, let's move on to the cost advantage. Here's where DeepSeek R1 truly disrupts the market. Let's talk costs, because for startups and small businesses, this is a game-changer. OpenAI's GPT-01 costs $15 per million input tokens and $60 per million output tokens. Whereas DeepSeek R1 costs only $0.14 per million tokens— per million input tokens and $2.19 per million output tokens. That's over 95% cheaper and 27 times more cost-effective. But it doesn't stop there. OpenAI reportedly spent over $80 million developing GPT-01. In contrast, DeepSeek R1 was built in just 2 months with a budget of about $6 million. Let's hear what CNBC had to say about this incredible achievement.

What took Google and OpenAI years and hundreds of millions of dollars to build, DeepSeek says took it just 2 months and less than $6 million.

For startups and small businesses, this affordability opens doors that were once locked behind high costs. Imagine what you could do with AI that's this powerful and budget-friendly. What really sets DeepSeek R1 apart isn't just the cost, it's how this model was trained. Unlike most traditional AI models, which depend heavily on pre-labeled datasets, DeepSeek R1 takes a completely different path. It uses reinforcement learning, Think of it like trial and error. The model doesn't rely on humans labeling every data point. Instead, it learns by doing, receiving feedback and improving as it goes. This approach unlocks some seriously advanced capabilities, such as self-verification. The model can double-check its work, refining its responses to ensure they're accurate. Step-by-step problem solving. It tackles complex tasks piece by piece, making it perfect for multi-step challenges and long chains of thought. DeepSeek R1 can follow a detailed reasoning process, handling intricate problems by generating detailed, logical explanations. And here's the best part: DeepSeek R1 is scalable, with versions ranging from 1.5 billion to up to 70 billion parameters, it can run efficiently on consumer-grade hardware like Apple's M2 Ultra. That's game-changing accessibility for startups and small businesses. Of course, it's not just about the technology. DeepSeek R1's origins in China raise valid concerns.

In other words, the adoption of a Chinese open-source model at scale, that could undermine U.S. leadership while embedding China more deeply into the fabric of global tech infrastructure.

One concern is government influence. AI models built in China are required to align with state policies, potentially leading to censorship of politically sensitive topics. Here's a clip from CNBC on this issue.

AI models built in China, they're forced to adhere to a certain set of rules set by the state. They must embody core socialist values. Studies have shown that models created by Tencent and Alibaba, they will censor historical events like Tiananmen Square, deny human rights abuse, and filter criticism of Chinese political leaders.

Another concern is data privacy. With strict oversight in China, businesses may hesitate to trust sensitive data or intellectual property to a model developed under these conditions. But here's the good news: DeepSeek R1 is fully open source. That means you can inspect the architecture, audit the training data, and ensure that it meets your standards. It puts the control back into your hands. The founder of Perplexity AI highlights this important distinction.

When LLaMA was being built and becoming more widely used, there was this question, should we trust Zuckerberg? But now the question is, should we trust China. That's a very big—

You should trust open source. That's, that's the— like, it's not about who— is it Zuckerberg or is it, um—

Does it matter then if it's Chinese if it's open source?

It—

look, it doesn't matter, uh, in the sense that you still have full control. Uh, it's— you run it as your own, like, like, um, set of weights on your own computer. You are in charge of the model.

This open-source transparency is a game changer. offering businesses the ability to mitigate risks while still leveraging DeepSeek R1's powerful capabilities. When should you choose a reasoning model like DeepSeek R1 versus a general-purpose model like GPT-4o? Here's the breakdown. In short, if your task requires logic, problem-solving, step-by-step reasoning, or workflow automation, a reasoning model like DeepSeek R1 is your go-to. But if you need a generalist for creative writing, marketing content, or conversational AI, then GPT-4o is hard to beat. Now that you know when to use a reasoning model, let's talk about the tasks it's best at, specifically the ones that can have the biggest impact on your business. Imagine analyzing large datasets or summarizing market research reports with minimal effort. Reasoning models deliver insights faster and with more accuracy than traditional methods. If you're in software development, DeepSeek R1 can write, debug, and explain code with near-human precision, helping your team save time and reduce errors. Automating multi-step workflows like lead qualification or logistics planning is where reasoning models thrive. They don't just execute tasks, they think through them step by step. From planning budgets to evaluating multiple what-if scenarios, reasoning models can crunch the numbers and deliver logical, actionable insights. And for handling complex customer inquiries, reasoning models excel at breaking down and responding logically. Whether it's troubleshooting technical issues or guiding users step by step through decision trees, DeepSeek R1 shines. These are just a few of the ways reasoning models can deliver massive value for businesses. Next, let's test DeepSeek R1 in action to see how it stacks up. I created this first test based on my personal experience with mobile apps. It's a great test for reasoning models because it involves multiple layers of calculations and requires step-by-step thinking. The name of the test is Calculating Profitability for an Ad Campaign. All right, so let's read through the question. I run a startup with a mobile app that's free to download on the App Store. To use the app, users must upgrade to a paid plan. There are 2 upgrade options: $10 a month with no free trial, $60 a year with a 7-day free trial. We are planning a new advertising campaign on Meta to attract new users. Here are the details and factors that we're reviewing.

Cost per click, $0.50.

Download rate, 25%. Paid upgrade rate, 5%. And the plan split, 50% of upgraders choose the $60 annual plan, including the 7-day free trial. Of those annual users, 50% cancel during the free trial and don't pay. The remaining 50% continue and pay for the $60 plan. And then 50% of upgraders choose the $10 monthly plan. with no free trial and payment begins immediately. I plan to spend $10,000 on this ad campaign today. Here are my questions. 1, at the current paid upgrade rate of 5%, what will be my profit at the end of the month? 2, what paid upgrade rate would I need to break even at the end of the month? And 3, what paid upgrade rate would I need to achieve $10,000 in profit for the month. So the reason why this is a really good question is because it involves multiple layers of analysis, multiple variables and questions.

So let's see how GPT-01 versus DeepSeek R1 performs.

I actually did the math in Excel so that I could double-check the answers for both models.

Okay, so make sure to select DeepThink R1 and select GPT-01 to use the reasoning models. And then here I'm just going to paste in the first question to both models so that we can run them side by side and do a comparison for each of the answers. Okay, so I'm just running it right now. Okay, so it looks like that's— they're both just thinking right now.

They're thinking through the problem.

For DeepSeekR1, you can actually see the thoughts that it's going through line by line, but with GPT-01, it's just summarized for you, so it's a little bit cleaner. Okay, wow, it looks like GPT-01 is already done. DeepSeek is still going through it. And I can actually already see that from my Excel sheet that some of these, these answers are actually accurate. Wow, it's incredible to see how much thinking DeepSeek is doing. It's like thinking like a person. It's pretty interesting. Okay, so I'm just doing a quick comparison to my Excel sheet. It looks like 01 calculated all the initial variables correctly.

It broke down All of the math.

It looks like DeepSeek is already done with the answer, so it's just presenting it right now.

I really like the formatting that the answer is presented in from DeepSeek.

It's a little bit easier to read.

Okay.

So let's take a look at the, each question that we asked. So for question 1, profit at the current 5% upgrade rate, it looks like all the variables are matching up on the DeepSeek end and same with on the 01 end. $5,000 in revenue, right? So the profit, it's actually a loss of $800, which is exactly right. That's what's in my Excel sheet. So that's what the 5% upgrade rate would bring for this particular example. So they both got that correct. Perfect. So let's move on to the second question, which is what would the paid upgrade rate need to be to break even? So they're both saying 5.8%. So let's test that in my Excel sheet. So I put 5.8% in and I got Break even. So that's perfect. And then here they're both saying 15.8% for $10,000 in profit, and I'm seeing the same thing in my Excel sheet. So it looks like they're both spot on and matching up when it comes to their answers. Okay, so I just wanted to test question 1 with GPT-4o, which is more like a general purpose model, just outta curiosity to see if it could actually answer a question like this. So I just put in the question and it's doing some sort of analysis right now. Really curious to see if it gets it right. Okay, so it's saying— it's actually, the answer is very clean and it was very fast. So something tells me it might not be right.

So it's saying number 1, profit at the current paid upgrade rate of 5% is $1,000.

And that is actually wrong because it's a loss of $800. It says that the paid upgrade rate needed to break even is 4%, but that's actually—

it should be 5.8%.

And then it says the paid upgrade rate needed to achieve $10,000 in profit is 14%, but it should really be 15.8%. So it got all of the questions wrong for this test. And actually, I'm, I'm not surprised because I think For multi-layered problems like this, you know, general purpose models like GPT-4o usually struggle. And this is a clear example of why these types of LLMs are not ideal for complex reasoning tasks. I think whenever it comes to like multi-step or multi-layer questions like this, you should definitely use DeepSeek R1 or GPT-01. Otherwise, you're more likely to get incorrect answers from, from GPT-4o.

I created the second test based on my personal experience with ad campaigns on social media. The reason why it's a good test for an AI reasoning model is because it involves, again, multiple steps and layers, analyzing performance metrics, calculating costs, and making strategic decisions based on those insights. So test number 2. Optimize ad budgets for maximum ROI.

Hi, I'm a startup with a monthly marketing budget of $25,000 split across 3 channels. Google Ads, $10,000 budget, average cost per click $1.50, click-through rate 4%, and conversion rate 10%. Facebook Ads, $10,000 budget, average cost per click $1, click-through rate 5%, Conversion rate 8%. And LinkedIn Ads, $5,000 budget, average CPC $2, CTR 3%, and conversion rate 15%. Right now we're generating about 2,000 monthly website visits from these ads combined, and our product sells at an average price of $50 per conversion. I'd like you to, one, analyze these metrics, calculate the cost per conversion for each channel. Suggest a revised budget allocation that maximizes total conversions within the same $25,000 limit. Provide a decision tree explaining how you'd allocate budget if we: A, increase the total budget to $40,000, or B, decrease it to $15K.

Explain the reasoning behind each budget shift and recalculate our projected ROI under these scenarios. Please break down your analysis step by step, discuss any assumptions, and highlight which channels you'd scale up or scale back first and why.

So for this next test, I'm just going to ask, ask each model the same questions side by side so that we can compare their responses and performance. Okay, so now each of the models are thinking through the problem. So for this question, we're looking to see that the model recommends the highest budget allocation towards the channel that has the lowest cost per conversion. So for the first question, it's calculating the cost per conversion for each of the channels, and it looks like they're all matching up. With $15 for Google Ads, $12.50 for Facebook Ads, and $13.33 for LinkedIn Ads, making Facebook the most efficient and Google the least efficient in terms of cost. Okay, so for this next section, it looks like they are both matching up with their recommendations.

They're recommending to prioritize Facebook because it has the lowest cost per conversion.

And since the budget is $25,000, they're recommending to put all of it into Facebook, which I think makes total sense. So for the next section, for both the $40,000 budget and the $15,000 budget, they're both pretty much recommending the same thing, to prioritize Facebook and Secondarily LinkedIn if we have a little bit of budget that we want to put there. But they're pretty much saying to go all in on Facebook, so this makes total sense. And then finally, it looks like the reasoning that they used pretty much lines up. It looks like You know, both DeepSeek R1 and GPT-01 are very much aligned in, in this question. Okay, so I'm just going to sum up the results from both of the tests. It looks like both GPT-01 and DeepSeek R1 were 100% accurate in their answers. Not only that, but they both broke down everything in a very similar way, following a clear, logical thought process. The one difference I noticed was that DeepSeek R1 did take a little bit more time to process the problem, but even then it's still impressively fast. So I really see very similar results, and I'm actually considering how spot on the results were with DeepSeek R1, and considering how much cheaper it is compared to GPT-01, I think it really does make it an excellent and cost-effective choice for businesses.

As a startup co-founder, I've been keeping a close eye on how AI models have been evolving, and I have to say, DeepSeek R1 has really caught my attention. Its performance, affordability, and open-source accessibility aren't just impressive, they're a total game-changer. It's the kind of breakthrough that gives startups, SMBs, and creators like us a real shot at competing with the big players. In the last few months, my team and I have been working hard at building Surfin AI, a platform that goes beyond basic RAG-powered chatbots. You know, the ones that answer questions based on your data? Well, many of them are limited in terms of functionality and customization, but with Surfin AI, we're looking to take things to the next level. Our mission with Surfin is to help businesses automate workflows, boost conversions, and grow faster. Whether it's qualifying leads, managing customer support, or diving deep into document research, we're building Surfin AI to help businesses do more with less effort. We're excited to integrate reasoning models like DeepSeek R1 and GPT-01 into our platform. When you combine those with top-tier models like GPT-4o, Claude Sonnet, and Gemini, And you've got something truly powerful. If you're curious to see what we're building, head over to surfn.ai and try it for free. I'd also love to hear your thoughts. And while you're at it, let me know, how would you use reasoning models like DeepSeek R1 in your business? I'd love to hear your ideas in the comments below. If you found this video helpful, hit that like button and subscribe to the AI Accelerant for more insights, hands-on demos, and the latest in AI innovation. Thanks for watching. See you guys next time.