The field of artificial intelligence is evolving at a breakneck speed, and two language models that have intrigued the interest of many in recent times are DeepSeek AI and OpenAI’s ChatGPT.

Both are designed to work with human-like text and produce human-like text. Nevertheless, they differ in development approaches, metrics of performance, and price structures.

DeepSeek vs. ChatGPT – A Comparison

Development Approaches

A Chinese startup created DeepSeek AI, which uses a technique called “distillation.” It leverages existing models, querying them, and then uses their responses to train a separate new AI.

Leveraging this approach, DeepSeek has established a competitive model at a significantly lower cost versus the traditional methods. In comparison, ChatGPT is developed by OpenAI and based on training with plenty of data (it requires a lot of computation). 

This traditional approach includes training the model on lines upon lines of data, which consumes significant power and infrastructure.

Performance Metrics

Performance metrics are vitally important for assessing AI. They help explain how each platform performs real-world tasks and the results you can expect to see.

In several benchmark tests, DeepSeek R1’s performance was, at best or nearly on par with, ChatGPT o1. For example, regarding mathematical tasks, DeepSeek R1 achieves 90.2% accuracy on the MATH-500 benchmark, while ChatGPT-o1 achieves 96.4% accuracy.

Cost Structures

In terms of cost structures, one of the more profound differences between ChatGPT and DeepSeek is price. DeepSeek’s price is much lower than o1 and its performance concerning coding and math is almost the same.

Ethical Considerations

This method of distilling knowledge from existing models by DeepSeek raises ethical concerns on the use of existing AI models, while ChatGPT was developed more traditionally: by training a model on a very large dataset.

Conclusion

Now, AI training has high costs. However, DeepSeek uses a distillation approach that runs akin to a lightweight model yet delivers the same performance with fewer cycles of computing power.

Nevertheless, it does present ethical questions surrounding the employment of existing AI work. ChatGPT, although more resource-heavy, had a more conventional development process.

Arguably, as the AI landscape continues to expand, it will be essential to maintain a balance between innovation and ethics to ensure responsible advancement.