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Cost plays a significant role when choosing an AI model for your applications. OpenAI’s GPT models (like GPT-4o and GPT-4 Turbo) are powerful but expensive, whereas DeepSeek offers similar capabilities at a fraction of the cost. But why is DeepSeek cheaper than OpenAI? The answer lies in its architecture and optimization techniques.
In this article, we’ll compare DeepSeek and OpenAI, explain their architectural differences in simple terms, and provide real-world examples to illustrate the cost difference.
Understanding the Core Difference: Architecture
The primary reason why DeepSeek is cheaper than OpenAI is its Mixture of Experts (MoE) architecture, compared to OpenAI’s fully dense transformer models. In simple terms, DeepSeek works like a team of specialists, where only the relevant experts contribute to answering a query, whereas OpenAI's GPT models use a single large network that processes every request with all its parameters, regardless of whether they are needed.
To better understand, imagine you have a team of experts (DeepSeek) versus a single large expert (GPT-4o). If you ask a question about coding, only the coding experts in DeepSeek respond, while the others remain idle, saving computational effort and cost. In contrast, OpenAI's dense transformer model makes every layer process the query, even if only one part of the model is required. This increases cost and processing time unnecessarily.
MoE allows DeepSeek to reduce inference costs since only a small fraction of the model is activated at any time, unlike GPT models, which must run fully each time. As a result, DeepSeek can provide AI responses at a much lower cost while maintaining competitive performance.
Cost Comparison: DeepSeek vs. OpenAI
DeepSeek’s cost advantage is visible in token-based pricing. AI models charge based on tokens (units of text), with separate pricing for input tokens (user prompts) and output tokens (AI responses).
To put this into perspective, imagine running a chatbot that processes 1 million input tokens and 1 million output tokens. With OpenAI's GPT-4o, this would cost $20.00, whereas with DeepSeek-Chat, the cost could be as low as $1.17, leading to over 90% cost savings. If an application requires billions of tokens per month, the savings with DeepSeek can be massive.
Where DeepSeek Saves Money in Real-World Applications
For businesses that require high-volume AI usage, choosing the right model can drastically affect operational costs. For example, customer support AI chatbots need to process thousands of queries every minute. With OpenAI’s GPT-4o, this would result in significant expenses, while DeepSeek provides a budget-friendly alternative with comparable accuracy and response quality.
Similarly, enterprise AI assistants that require fast responses across different domains can benefit from MoE’s specialized approach. Instead of engaging the entire model, DeepSeek directs the request to the most relevant expert, making it much more efficient. This makes DeepSeek an attractive option for AI-driven applications in search engines, financial forecasting, automated research tools, and large-scale chatbots.
Should You Use DeepSeek or OpenAI?
While OpenAI’s GPT models are known for their robust reasoning and high-quality responses, they come at a premium cost. DeepSeek, on the other hand, leverages MoE architecture to offer a cost-efficient alternative, making it ideal for businesses and developers who require large-scale AI processing without breaking the bank.
If your priority is best-in-class AI reasoning and multi-modal capabilities, OpenAI remains a strong choice. However, if cost-efficiency, scalability, and affordability are your main concerns, DeepSeek is the way to go.