DeepSeek vs ChatGPT: Which AI Model Is Better

Why DeepSeek vs ChatGPT Highlights AI Impact

The AI landscape is heating up. DeepSeek, as the latest kid on the block, is challenging ChatGPT’s dominance. It’s an interesting battle that highlights how much impact AI has today.

But it leaves us with a dilemma. Which option is better? Should you go for the premium customizable features of ChatGPT or the more cost-effective DeepSeek?

In this post, we’ll take a deep dive into this issue. After reading this, you’ll be able to take a firm stand in the DeepSeek vs ChatGPT debate.

Who should read this?

• AI/ML teams evaluating fine-tuning options

• Businesses looking for AI chatbots, automation, or LLM integration

• General users curious about which AI performs best

What’s covered: Performance, pricing, customization, and LLM fine-tuning capabilities.

What is DeepSeek AI?

This Chinese AI startup has been disrupting the market with open-source LLMs. It’s the hot new ticket in 2025, having experienced some downtime because of its surge in popularity.

You have two basic options:

DeepSeek-V3

V3 is a large language model comparable to GPT4.o. It generates text responses quickly. You would use this to draft an email.

DeepSeek-R1

Like ChatGPT’s o1 and o3-mini, R1 is a reasoning model. It uses chain-of-thought reasoning, making it capable of more complex problems. You would use this for coding or solving a math equation.

It’s based on a Mixture of Experts LLM architecture with 671B parameters. However, it only activates 37B per query, making it more economical to run. It’s a highly efficient model because the system only activates the components that it needs for the query.

The company boasted that they trained their R1 model on 2,048 Nvidia H800 GPUs in just 55 days using a reinforcement machine learning algorithm. The cost came in at $5.5 million, which is a drop in the bucket compared to ChatGPT’s estimated $100 million development cost.

Core Strengths

• Mixture-of-Experts (MoE) architecture for efficient processing

• Lower training costs than traditional models

• High performance in logic, math, and coding

Who Uses It?

AI developers, researchers, businesses looking for open-source AI.

DeepSeek has better real-time pattern recognition capabilities for identifying advanced threats, while ChatGPT can analyze cybersecurity reports. This means that DeepSeek can quickly identify and respond to security threats, whereas ChatGPT is better suited for analyzing existing data and providing insights.

Kevin Baragona Founder at Deep AI

What is ChatGPT?

Released in 2022, ChatGPT was OpenAI’s flagship chatbot. It’s since morphed into a robust multimodal AI model. There are several iterations, with the latest, GPT-4.5 having been released in March 2025.

Is this a response to DeepSeek’s impressive performance in math and coding? It could be, but it still doesn’t quite measure up.

That said, there’s a lot to like about OpenAI’s multi-modal model:

• Best-in-class natural language processing (NLP)

• Supports text, image, and voice-based AI interactions

• Enterprise integrations and user-friendly APIs

ChatGPT is a dense model with 1.8T parameters, far more than DeepSeek. ChatGPT uses supervised fine-tuning to train its models, which is far more expensive than DeepSeek’s reinforcement training.

Who Uses It?

Businesses, developers, and general users for chat, content creation, and automation.

I will always choose ChatGPT over DeepSeek. When I used DeepSeek, it felt very similar to ChatGPT, but not as refined. I use ChatGPT Pro to research campaigns, get feedback on writing, and help organize my meeting notes. In my experience, ChatGPT was the more polished and effective option.

Devan Leos co-founder & CCO at Undetectable AI

DeepSeek vs ChatGPT: Key Differences

So, how do the two large language models compete on a one-on-one basis?

DeepSeek often excels in technical domains and code generation, while ChatGPT may offer superior performance in content creation and conversational applications.

Brian Tham Applied Artificial Intelligence Undergraduate

Accuracy and Response Quality

Interface and Accessibility

DeepSeek is more technical in nature, so it takes a little playing around to get used to it. It’s not as polished as ChatGPT. At the moment it’s only text-based and won’t generate images.

ChatGPT is the more user-friendly of the two. You can also use text or voice prompts and generate images through DALL-E.

Customization and Integration

DeepSeek is more customizable than ChatGPT, but the latter has better integration with Google Workspace and AutoGPT. The latter can work alongside ChatGPT for automating complex workflows.

Which AI is better for general users? ChatGPT is more versatile, while DeepSeek is better for structured responses.

Which AI is better for research? ChatGPT excels at contextual reasoning, DeepSeek is faster in logic-based tasks.

Pricing & Subscription Plans

Comparing DeepSeek cost vs ChatGPT pricing is like comparing data annotation services solely on data annotation pricing. You have to go deeper and look at what you’re getting for your money.

Which AI is more cost-effective?

DeepSeek is better for API use and local deployment. ChatGPT is easier to scale, but costs more.

Best AI Model for Different Users

It’s important to look at the context when deciding ChatGPT vs DeepSeek.

DeepSeek might be better for creating different types of LLMs, while ChatGPT might work better for image recognition tasks.

For Everyday Users: Conversations

ChatGPT is better at creating more natural, engaging, and creative content. DeepSeek tends to be more structured and fact-driven.

For Businesses: Productivity and Automation

• ChatGPT: Best for customer service, content generation, and automation

• DeepSeek: Best for cost-efficient AI chatbots and internal automation

For Developers and AI/ML Teams : Coding and Technical Work

• DeepSeek: Faster code generation and logic-heavy applications

• ChatGPT: Better at explaining code, debugging, and ML model development

One emerging use case is ChatGPT data annotation, where businesses leverage its NLP capabilities to pre-label datasets, reducing manual workload and speeding up AI training cycles.

DeepSeek vs. ChatGPT for LLM Fine-Tuning

How industry-specific does your model need to be? In the DeepSeek AI vs ChatGPT debate, the answer may be down to how much you need to customize your application.

Fine-Tuning and Customization Capabilities

Which AI is better for training industry-specific models?

So, which option would a data annotation company, for example, choose? This would depend on what type of data annotation they perform and how much control they need.

• DeepSeek: Offers full control over fine-tuning

• ChatGPT: More stable enterprise API but limited in customization

Performance Benchmarks After Fine-Tuning

Both models offer accuracy improvements before and after LLM fine tuning.

DeepSeek tends to perform better on specialized datasets because it was designed with an eye on fine-tuning for different domains. It’s better for niche applications that require high accuracy, like finance.

It’s especially good with lightweight processes like LoRa, requiring less GPU usage overall.

ChatGPT is more general, meaning it can adapt to a broader range of topics. This AI model is cost-effective with the first round of training, but retraining becomes expensive. At the moment, you need to be an enterprise member to access GPT fine-tuning.

Security, Bias and Ethical Considerations

Which model performs better in these areas, DeepSeek vs. ChatGPT?

Data Privacy and Compliance

DeepSeek is based in China and doesn’t necessarily follow the strict compliance measures that Western companies do. You may not have as much control over sharing.

ChatGPT follows GDPR and enterprise security protocols, so it’s safer for sensitive data.

• DeepSeek: Open-source but may lack strict compliance measures

• ChatGPT: Follows GDPR & enterprise security protocols

AI Bias and Content Moderation

DeepSeek is more likely to censor responses. For example, it may skew answers to what looks good for the Chinese government. ChatGPT employs stricter moderation.

DeepSeek’s open-source model offers more control over AI-generated content because:

• It’s completely customizable

• You can host your data on your own servers

• You can change the underlying training methodology

• There are no limits in terms of API or third parties

Which AI Subscription is Worth It: DeepSeek vs ChatGPT?

Both offer interesting possibilities. You need to consider the DeepSeek vs ChatGPT comparison carefully before deciding. What is most important?

Choose DeepSeek if you need an open-source, cost-effective model for fine-tuning and coding. It’s a good idea for LLM data labeling.

Choose ChatGPT if you want a feature-rich, user-friendly AI assistant with enterprise-ready API solutions.

Future of DeepSeek and ChatGPT in AI Development

Will the two fight for dominance? In the DeepSeek R1 vs. ChatGPT debate, which company has more lasting power. It’s clear that OpenAI has some competition, but the products are relatively different. ChatGPT’s multimodal nature is a definite advantage.

What’s Next for DeepSeek?

We can expect exciting things going forward. The upcoming DeepSeek R2 model is on the horizon and offers better fine-tuning capabilities, multilingual reasoning, and coding. The company is apparently pushing the release forward.

Besides, we’ll see more efficiency gains in the Mixture-of-Experts framework. The company will continue to run on an open-source basis, which should see interesting innovations from the community.

What’s Next for ChatGPT?

The company is rising to the challenge with:

• GPT-5 with deeper enterprise integrations and improved multimodal features

• Stronger compliance and AI explainability improvements

• Integrating Sora, OpenAI’s video generation model

Meanwhile, as these innovations continue to evolve, you can rely on expert video annotation services to ensure high-quality labeled data for your projects.

About Label Your Data

If you choose to delegate data annotation, run a free data pilot with Label Your Data. Our outsourcing strategy has helped many companies scale their ML projects. Here’s why:

No Commitment

Check our performance based on a free trial

Flexible Pricing

Pay per labeled object or per annotation hour

Tool-Agnostic

Working with every annotation tool, even your custom tools

Data Compliance

Work with a data-certified vendor: PCI DSS Level 1, ISO:2700, GDPR, CCPA

FAQ

Is DeepSeek or ChatGPT better?

That depends on what you are using it for. DeepSeek is more cost-effective and excels at fine-tuning and coding. If you’re looking for more features, better customization, and better scaling, you’ll want to choose DeepSeek.

Which is better than ChatGPT?

DeepSeek is better than ChatGPT when it comes to fine-tuning specific datasets. It’s also completely customizable and allows you more freedom in terms of what third-party applications and APIs you can use.

How is DeepSeek different?

DeepSeek is different in that it runs on a Mixture-of-Experts architecture. This means that it costs less to run because it only uses a subset of parameters for each query. It’s also an open-source application, making it completely customizable.

What are the pros and cons of DeepSeek AI?

DeepSeek AI is free to use and customizable. It excels at logic and technical tasks. However, it’s not as creative as ChatGPT, making it less useful for content creation. A big con is that the company doesn’t adhere to the GDPR privacy regulations.

Written by

Karyna Naminas CEO of Label Your Data

Karyna is the CEO of Label Your Data, a company specializing in data labeling solutions for machine learning projects. With a strong background in machine learning, she frequently collaborates with editors to share her expertise through articles, whitepapers, and presentations.