DeepSeek vs. ChatGPT, Which One is Best for You?

In 2025, the battle for the best AI model is heating up, and while many models are in the race, DeepSeek is quickly standing out alongside ChatGPT as a top contender.

ChatGPT was one of the first AI models to transform how we interact with technology and reshape perceptions of artificial intelligence.

On the other hand, DeepSeek emerged from imposed limitations and a lack of resources. Yet it now challenges the core belief held by major AI companies: “that better GPUs automatically mean better AI.”

Just a few months ago, as small language models (SLMs) gained popularity, we realized that ‘better data beats better algorithms.’ Now, with DeepSeek’s R1, this idea has evolved into ‘better data with better algorithms will beat better GPUs.’

DeepSeek and its story of a $1 trillion market shakeup

Before talking about DeepSeek vs. ChatGPT, let’s first have a basic understanding of what is going on, why everyone is searching for DeepSeek news, and how it wiped off over $1 trillion in U.S. stocks.

DeepSeek is a Chinese AI company that was founded in July 2023. It focuses on developing open-source large language models (LLMs).

In AI labs, the GPUs used for number crunching and matrix multiplication to train these models remain idle most of the time. These GPUs operate at peak potential for only about a third of the time, while the rest is spent waiting for data to be transferred between memory caches or other GPUs.

Liang Wenfeng, a hedge fund owner and founder of DeepSeek used these GPUs to build the AI model, mostly because he did not have access to advanced and latest chips due to U.S.-imposed restrictions on China.

After DeepSeek launched its R1 model in January 2025, it changed the way people viewed AI development. It proved that anyone could access high-quality AI using affordable GPUs and open-source models.

This challenged the traditional approach, which relied on using expensive, high-end GPUs that not everyone could access. As a result, tech stocks in the U.S. plunged, with Nvidia’s stock dropping 17%, wiping out $560 billion in market capitalisation.

What’s the buzz around DeepSeek vs. ChatGPT

With everything changing so fast, active AI users are considering DeepSeek as a replacement for ChatGPT. Here’s why:

1. Control vs. convenience

DeepSeek’s R1 model is an open-source AI that performs on par with OpenAI’s paid o1 model in many tasks. Being open-source, its code and technical papers are publicly available, allowing anyone to use and examine it. But it has a much steeper learning curve.

Moreover, R1 displays its reasoning process to users, enhancing learning by revealing how the model approaches problems, a feature hidden in o1.

On the other hand, ChatGPT (GPT-4o), developed by OpenAI, operates as a closed-source proprietary model, meaning its underlying code and technical details are not publicly available. It follows a freemium model, offering limited features for free while charging $20-$200 per month for access to advanced models like o1.

However, it is widely accessible via API, allowing businesses and developers to integrate its powerful conversational AI into applications without handling complex infrastructure.

2. Local hosting vs. cloud hosting

DeepSeek offers models ranging from the 1.5-billion-parameter to larger models having 671B parameters. To run these models locally, it largely depends on the GPU’s Video Random Access Memory (VRAM) capability.

Each model variant has specific VRAM needs. For instance:

• DeepSeek-R1-Distill-Qwen-1.5B: Requires approximately 0.7 GB of VRAM.
• DeepSeek-R1-Distill-Qwen-7B: Requires around 3.3 GB of VRAM.
• DeepSeek-R1-Distill-Qwen-14B: Needs about 6.5 GB of VRAM.
• DeepSeek-R1-Distill-Qwen-32B: Demands approximately 14.9 GB of VRAM.

ChatGPT (GPT-4/GPT-4o) however, is a closed-source, cloud-based model that can’t be run locally. It’s designed for ease of use and scale, accessible via API or browser—ideal for businesses and users who prefer a managed solution without the hardware requirements or setup.

In short:

• DeepSeek is best for those who want to experiment, fine-tune, or host privately.
• ChatGPT is better suited for plug-and-play convenience, scalability, and top-tier performance, especially since many open-source alternatives like DeepSeek, while powerful, are still catching up in terms of polish and user experience.

3. Precision vs. memory optimization

ChatGPT prioritizes maximum precision by default, using 32-bit numbers to maintain high accuracy. While effective, this can lead to higher memory consumption.

DeepSeek lowers the precision by using around 8 decimal places, significantly reducing memory usage up to 75%—without sacrificing accuracy in most practical tasks.

DeepSeek’s approach provides a valuable efficiency boost by prioritizing resource optimization, while ChatGPT prioritizes maximum precision, regardless of memory usage.

4. Speed vs. accuracy in token processing

ChatGPT processes text sequentially, i.e. reads sentences word by word, sometimes breaking big words into smaller tokens. This method ensures high accuracy but can be slower in large-scale operations.

DeepSeek, however, processes entire phrases at once, making it twice as fast while maintaining 90% of ChatGPT’s accuracy. When applied to billions of words or large data sets, it makes R1 incredibly fast.

It’s also important to understand that one word is not always equal to one token. A single word can be broken into multiple tokens depending on its complexity, and the ratio between words and tokens varies across models.

5. Smarter resource use vs. peak resource utilization (MoE)

AI models have settings called parameters, which are like building blocks of an AI model that help them learn and make decisions.

DeepSeek has 671 billion parameters, but instead of using all of them at once, it only activates 37 billion at a time using a smart system called Mixture of Experts (MoE). This means only the most relevant parts of the model are used for each task, making it much more efficient.

In contrast, OpenAI’s models activate all available parameters at once (i.e., 1.8 trillion parameters), which is a massive amount. It’s like reading every book in a library instead of just picking the one that answers your question. This makes them far more resource-intensive compared to models that use a selective approach.

While MoE isn’t a new concept, it was challenging to train these models efficiently due to instability and difficulty in management. However, new advanced GPU utilization techniques have helped improve stability and GPU usage.

6. Distilled Performance vs. Full-Scale Accuracy

DeepSeek uses a process called knowledge distillation, where a smaller model (the “student”) learns to mimic the behavior of a larger, more complex model (the “teacher”). As a result, these student models can retain up to 95% of the original model’s accuracy while using significantly less memory and computing power.

ChatGPT relies on large, complex models with billions to trillions of parameters to deliver strong performance across a wide range of tasks. This ensures high accuracy and flexibility but requires more memory, longer processing times, and higher infrastructure costs.

7. Deep reasoning vs. supervised learning

ChatGPT is trained primarily using supervised learning, where it learns from labelled data, picking up patterns and improving over time. This method helps the model generate accurate responses but limits their ability to think deeply or reason through complex problems.

DeepSeek-R1, on the other hand, was built on top of the V3 model, which was trained using reinforcement learning. A process where the AI learns through trial and error, adjusting its approach based on feedback to maximize rewards.

DeepSeek used the Group Relative Policy Optimization (GRPO) process to train its model, enabling it to develop an extended chain of thought, much like how humans solve difficult problems.

During training, the AI even recognized its own mistakes and backtracked to correct them, improving its accuracy over time. This makes DeepSeek-R1 a highly advanced, precise, and efficient model, particularly useful for mathematical tasks and complex computations.

8. Government Access vs. GDPR Protections

It is important to understand that anything you share with DeepSeek will be stored on servers located in China, where the government can access it under strict laws. That might be a red flag, especially for those worried about data security or censorship, like how DeepSeek avoids answering sensitive questions about China.

On the other hand, ChatGPT, from OpenAI, follows stricter Western privacy rules like GDPR and promises not to store free-tier chats long-term, making it safer for users.

9. Affordable access vs. premium pricing

The flagship model, R1, is available as an open-source solution for local hosting, allowing deployment without direct costs. The pricing for the hosted solution is highly competitive compared to its competitors.

DeepSeek-R1 costs $0.55 per million input tokens and $2.19 per million output tokens. Additionally, the DeepSeek-V3 model (deepseek-chat) offers input costs as low as:

• $0.07 per million tokens (cache hit)
• $0.27 per million tokens (cache miss)
• Output pricing at $1.10 per million tokens

OpenAI’s GPT o1 model charges $15 per million input tokens and $60 per million output tokens, making DeepSeek approximately 27 times more affordable for input and output processing.

While ChatGPT is more expensive, the higher price comes with added value. ChatGPT also includes features like image generation, file uploads and analysis, web browsing, and code interpretation, all in one platform. These advanced tools aren’t yet available with DeepSeek.

Let’s make it easy, quickly choose between DeepSeek and ChatGPT

Both ChatGPT and DeepSeek are powerful AI models, but they serve different needs.

ChatGPT is great for conversations, content creation, and business applications, offering a smooth and user-friendly experience with features like video interactions, image generation, and various integrated tools.

It’s highly versatile, helping with everything from writing blog posts and brainstorming marketing ideas to coding support and problem-solving. Its ease of use makes it accessible to beginners and professionals alike.

On the other hand, DeepSeek is built for deep reasoning, technical tasks, and AI development, making it a strong choice for researchers and businesses that require advanced data retrieval, market analysis, or niche industry insights.

With its ability to filter results more effectively than traditional search engines, it’s ideal for those who need to go beyond surface-level information. The model’s open-source nature provides transparency and cost-effective AI solutions, especially for enterprises looking to fine-tune their AI for specialized tasks.

A key difference between them is their focus—ChatGPT is designed for conversational AI and excels in engagement, while DeepSeek aims for Artificial General Intelligence (AGI) and is more focused on advanced AI development. DeepSeek-R1 performs at a level comparable to OpenAI’s o1 model, offering an alternative for those seeking open-source AI capabilities.

With 33.7 million monthly active users worldwide, DeepSeek is gaining traction, especially in markets like China, India, and Indonesia, which account for over 51% of its user base.

As AI continues to evolve and adoption grows, this is an exciting time for businesses and researchers to explore new possibilities. Whether you’re looking to integrate AI for content creation, deep research, or automation, the options are expanding rapidly.

And the right tools can make all the difference in leveraging AI for growth. Platforms like Thinkstack make it easy for anyone to build and integrate AI chatbots, transforming the way businesses interact with their customers.

It’s the best time to build, innovate, and take advantage of AI-driven solutions.

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