DeepSeek R1 vs. GPT-4: Which AI Model Should You Choose?

8/10/2025

DeepSeek R1 vs. GPT-4: Is the Open-Source Underdog Actually a Better Choice?

What’s up, everyone. Let’s talk about something that’s been on my mind a lot lately: the AI power struggle. For the longest time, it’s felt like OpenAI’s GPT models have been the undisputed kings of the hill. GPT-4, in its various forms, has been the go-to for pretty much everything, from writing emails to coding complex applications. But here's the thing, the landscape is shifting, and it's shifting in a BIG way. There’s a new breed of open-source models cropping up, & they’re not just nipping at the heels of the giants; in some cases, they're straight-up outperforming them.

And the one that's really got my attention is DeepSeek R1.

Honestly, when I first heard about it, I was a little skeptical. Another open-source model claiming to be a GPT-4 killer? Heard that one before. But the more I dug into it, the more I realized this one is different. It’s not just a cheaper alternative; it's a powerhouse in its own right, with some serious architectural advantages & a focus on reasoning that makes it a compelling choice for a lot of people, especially developers & businesses looking for more control.

So, today, we're going to do a deep dive. We'll go beyond the headlines & the hype to really compare DeepSeek R1 & GPT-4. We'll look at the raw performance, the nitty-gritty of running it locally, & what this all means for the future of AI. This is the kind of insider knowledge I wish I had when I was first exploring this stuff, so I hope this is helpful for you.

So, What’s the Big Deal with DeepSeek R1 Anyway?

Before we get into the head-to-head, let's get to know our challenger. DeepSeek R1 isn't your average language model. It was created by DeepSeek AI, a company that's been making some serious waves. What makes R1 so special is its architecture. It's a "Mixture of Experts" or MoE model. Now, without getting too bogged down in the technical jargon, here’s what that means in plain English:

Imagine you have a team of specialists instead of one generalist. When a task comes in, you don't send it to the whole team; you send it to the experts who are best equipped to handle it. That's essentially what an MoE model does. DeepSeek R1 has a whopping 671 billion parameters (the building blocks of an AI model), but for any given task, it only uses about 37 billion of them. This makes it incredibly efficient. It's like having a V8 engine that only uses the fuel of a four-cylinder when you're cruising on the highway. Pretty cool, right?

This efficiency is a HUGE part of why DeepSeek R1 is so compelling. It translates to lower costs & the potential to run these powerful models on your own hardware, but more on that later.

The Head-to-Head: DeepSeek R1 vs. GPT-4 Performance

Alright, let's get to the main event. How does DeepSeek R1 actually stack up against the reigning champ, GPT-4? The answer, it turns out, is a little nuanced. It's not a simple "one is better than the other." It really depends on what you're using it for.

The Benchmark Battle

If we look at the raw numbers from a lot of the standard AI benchmarks, DeepSeek R1 is seriously impressive. On tests that measure multitask accuracy & math problem-solving, it actually outperforms GPT-4o. For instance, on the MMLU benchmark, which tests knowledge across 57 subjects, DeepSeek-R1 scores a 90.8% compared to GPT-4 Turbo's 85.4%. That's a significant lead.

When it comes to math & coding, DeepSeek R1 also holds its own, often slightly outperforming GPT-4o in these areas. This is because DeepSeek R1 was specifically designed with a focus on reasoning capabilities. It's been trained to think through problems step-by-step, which makes it a natural fit for logical & technical tasks.

However, where GPT-4o tends to have a slight edge is in general knowledge tasks. It also has the advantage of being multimodal, meaning it can handle not just text, but also images, audio, & video, something DeepSeek R1 doesn't currently do.

Beyond the Benchmarks: Real-World Feel

But let's be real, benchmarks don't tell the whole story. How do these models feel to use in the real world?

Creative & Conversational Tasks: This is where GPT-4 & its variants still shine. They have a knack for generating human-like, creative text & can handle long, nuanced conversations with ease. While DeepSeek R1 is no slouch, some users have noted that it can sometimes struggle with creative writing & maintaining context in very long conversations. So, if you're writing a novel or need a chatbot with a very strong personality, GPT-4 might still be your best bet.
Coding & Technical Tasks: This is where DeepSeek R1 really comes into its own. Its strong reasoning abilities make it a fantastic coding companion. It excels at generating code, debugging, & explaining complex technical concepts. In some direct comparisons, developers have found that DeepSeek R1 provides solutions that are just as good, if not sometimes more elegant, than GPT-4's. The key difference is often in the style of the solution. For example, in one coding challenge, GPT-4o used regular expressions while DeepSeek R1 used list comprehensions – both valid, but different approaches to the same problem.

So, the takeaway here is this: if your primary need is for a highly creative & versatile conversational AI, GPT-4 is a solid choice. But if you're focused on technical tasks, coding, & complex problem-solving, DeepSeek R1 is a VERY strong contender, and given its other advantages, might even be the better choice.

The Local Advantage: Taking Back Control with DeepSeek R1

This, for me, is the most exciting part of the DeepSeek R1 story. Because it's open-source, you can run it on your own hardware. This is a game-changer for a few key reasons:

Privacy & Security: When you use a closed-source model like GPT-4, you're sending your data to a third-party server. For many businesses, especially those dealing with sensitive customer information or proprietary code, this is a non-starter. Running a model locally means your data never leaves your control.
Customization: Open-source models are incredibly flexible. You can fine-tune them on your own data to create a model that's perfectly tailored to your specific needs. Imagine a customer service chatbot that knows your product inside & out, or an internal knowledge base that can answer questions about your company's specific processes.
Cost: While there's an upfront hardware cost, running a model locally can be significantly cheaper in the long run than paying for API access, especially for high-volume use cases.

So, What Does it Take to Run DeepSeek R1 Locally?

This is where it gets interesting. You might be thinking you need a supercomputer to run a model of this caliber, & for the full 671B parameter version, you'd be right. One guide outlines a CPU-only build that costs around $6,000 & requires a whopping 768GB of RAM. That's definitely not for the average user.

But here's the good news: DeepSeek has also released "distilled" versions of R1, ranging from 1.5B to 70B parameters. These smaller models have been trained on the output of the full model, so they retain a lot of its power in a much more compact package. And these are VERY accessible.

Here's a rough breakdown of what you'd need for some of the distilled models:

1.5B model: This is the smallest version & can run on a computer with as little as 8GB of RAM & a CPU that's less than 10 years old.
7B/8B models: These are the most common for local use. You'll want a GPU with at least 6GB of VRAM for a smooth experience.
32B model: Now we're getting into more serious hardware. You'll need a GPU with 24GB of VRAM or more.
70B model: For the most ambitious home users, you'll be looking at a GPU with 48GB of VRAM.

A Quick & Dirty Guide to Getting Started with Ollama

The easiest way to get up & running with DeepSeek R1 locally is with a tool called Ollama. It's a fantastic piece of software that makes running open-source models on your own machine incredibly simple.

Here’s a super-simplified guide:

Download & Install Ollama: Head over to their website & grab the version for your operating system.
Open Your Terminal: This is the command-line interface for your computer.
Run the DeepSeek R1 Model: Type in the following command to download & run the 8B version of the model:
1ollama run deepseek-r1:8b
Start Chatting: That's it! You can now chat with the model directly in your terminal.

This is just the tip of the iceberg, of course. From here, you can integrate the model into your own applications, build custom chatbots, & so much more.

Speaking of custom chatbots, this is where a tool like Arsturn comes in. If you're a business looking to leverage the power of a local AI model for customer service, Arsturn can help you build a no-code AI chatbot trained on your own data. This allows you to create a highly personalized & efficient customer experience, providing instant support & answering questions 24/7. Imagine combining the power & privacy of a locally run DeepSeek model with the ease of use of a platform like Arsturn. That's a pretty powerful combination for any business.

The Future is Open (and Hybrid)

So, what does all of this mean for the future of AI?

For one, it's clear that the gap between open-source & closed-source models is closing, and it's closing FAST. We're moving beyond a world where one or two companies dominate the AI landscape. The rise of powerful open-source models like DeepSeek R1 is democratizing AI, making it more accessible, customizable, & affordable for everyone.

But this doesn't necessarily mean the end of closed-source models. What we're likely to see is a more hybrid future. Businesses might use a closed-source model like GPT-4 for its broad, general-purpose capabilities, but then turn to a fine-tuned, open-source model running locally for more specialized or sensitive tasks.

Think about it: a company could use GPT-4 to power a public-facing chatbot for general inquiries. But when a customer has a specific question about their account or needs to discuss a confidential issue, the conversation could be seamlessly handed off to a secure, locally hosted DeepSeek R1 model that has been trained on the company's internal data. This gives them the best of both worlds: the power of a large, general-purpose model & the privacy & customization of a local one.

This is where a platform like Arsturn can be so valuable. It helps businesses bridge that gap, allowing them to build meaningful connections with their audience through personalized chatbots, regardless of the underlying model. Whether you're using a closed-source API or a locally hosted open-source model, Arsturn can help you create a seamless & engaging conversational experience.

So, Who Wins?

At the end of the day, the "winner" in the DeepSeek R1 vs. GPT-4 debate is… you. The user.

The fact that we even have this debate is a testament to how far the open-source community has come. We now have a real, viable choice.

If you need a versatile, highly creative AI that can handle a wide range of tasks & you're not overly concerned about data privacy or cost, GPT-4 is still a fantastic option.

But if you're a developer, a business, or anyone who values privacy, control, & customization, DeepSeek R1 is an incredibly compelling alternative. Its strong reasoning abilities, cost-effectiveness, & the sheer fact that you can run it on your own terms make it a force to be reckoned with.

Honestly, I think we're just at the beginning of this open-source AI revolution. And I, for one, am incredibly excited to see what comes next.

Hope this was helpful. Let me know what you think in the comments below