DeepSeek R1 employs a Mixture-of-Experts (MoE) architecture with 671 billion parameters, activating only 37 billion per request to balance performance and efficiency.

Deepseek R1 is designed to deliver comparable performance to high-end AI models like OpenAI’s offerings but at a significantly lower cost.

This makes it more accessible to businesses and developers looking to integrate advanced AI capabilities without high expenditure.

The model generates high-quality reasoning data, which can then be used to train smaller, more specialized models.

This enables efficient deployment of AI solutions on edge devices with constrained resources.

R1 features self-reflective mechanisms, allowing it to re-evaluate previous answers and refine its approach.
This self-improvement capability is not commonly found in older models, making R1 more dynamic in complex tasks.

Traditional models often provide direct answers, whereas Deepseek R1 uses structured step-by-step reasoning.

This results in higher-quality outputs with enhanced logical consistency and decision-making capabilities.

Unlike traditional AI models that rely heavily on pre-trained data, R1 benefits from extended reasoning during inference.

The model improves performance by allowing more processing time, enabling it to think through problems with greater depth.