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Are you aware that there are different types of AI model architectures?
Two of the most common are Dense (GPT-style) models and Mixture of Experts (MoE) models.
I find myself using both.
For general day-to-day tasks, I usually prefer dense GPT-style models.
But when I'm researching a topic or working through a complex plan, I often reach for a Mixture of Experts model.
Why?
Let's look at how they differ.
Dense Models (Traditional GPT-Style)
A traditional GPT model is called a dense model.
To help you visualise better, I will use an example.
Imagine a company with 100 employees.
Every time a customer asks a question like:
"How do I bake a cake?"
All 100 employees gather together to answer it.
Even if only 10 of them actually know anything about baking, everyone still participates.
This is similar to how a dense model works.
Every layer of the model is used for every prompt.
All of the model's parameters are active during inference.
For example, if a dense model has 100 billion parameters, all 100 billion parameters are involved every time you ask it a question.
Mixture of Experts (MoE)
Now imagine the company works differently.
Instead of making everyone attend every meeting, there are specialists:
- Programming expert
- Law expert
- Biology expert
- Writing expert
- Translation expert
When someone asks:
"Write Python code."
The manager says:
"Only the programming expert and the writing expert are needed."
The other experts stay idle.
This is exactly how a Mixture of Experts (MoE) model works.
A small component called the router decides which experts should handle your prompt.
So if a model contains 64 experts, perhaps only 2 or 4 experts are activated for a given request.
The rest remain inactive.
Examples of Dense and MoE Models
Dense (Traditional GPT-Style)
These models use all of their parameters for every request.
- OpenAI GPT-3
- OpenAI GPT-3.5
- Meta Llama 1
- Meta Llama 2
- Meta Llama 3, 3.1, and 3.2
- Google Gemma (all versions)
- Qwen 2, 2.5, and the dense variants of Qwen 3
- Mistral 7B
- Microsoft Phi-2, Phi-3, and Phi-4
Mixture of Experts (MoE)
These models activate only a subset of experts for each request.
- Mixtral 8x7B
- Mixtral 8x22B
- DeepSeek V2
- DeepSeek V3
- DeepSeek R1
- Gemini 1.5
- Gemini 2.0
- Gemini 2.5
- Grok 2
- Grok 3
- GPT-4 (widely reported as MoE)
Conclusion
Neither architecture is universally better.
Dense models are simpler and use their entire network for every prompt.
Mixture of Experts models improve efficiency by activating only the experts that are most relevant to the task.
As AI models continue to grow larger, MoE has become an increasingly popular approach for scaling performance without proportionally increasing inference costs.
Understanding the difference helps explain why modern LLMs can vary so much in speed, memory usage, and overall efficiency, even when they appear similar on the surface.
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