DeepSeek-R1 is an innovative open-source reasoning AI model that matches the performance of OpenAI's o1 across various benchmarks.

This model employs large-scale reinforcement learning with minimal labeled data, enabling advanced reasoning capabilities.

DeepSeek-R1 is available in two versions: DeepSeek-R1-Zero, which relies solely on reinforcement learning, and DeepSeek-R1, which utilizes a multi-stage training pipeline.

While DeepSeek-R1-Zero exhibits strong reasoning capabilities, it struggles with issues such as endless repetition and poor readability.

The model enhances response accuracy through a Chain of Thought (CoT) approach, generating detailed reasoning steps prior to final answers.

DeepSeek-R1's architecture supports complex reasoning tasks, making it suitable for advanced education, tutoring systems, and research.

The R1 series features a Mixture of Experts (MoE) architecture with 671 billion parameters, activating less than 10% during processing for efficiency.

DeepSeek promotes transparency in AI research by making its development process, including training data and methodologies, publicly accessible.

The release of DeepSeek-R1 reflects a growing trend in open-source reasoning models, which are emerging as competitive alternatives to proprietary systems.

Additionally, DeepSeek has launched smaller, efficient models ranging from 1.5 billion to 70 billion parameters, retaining strong reasoning capabilities.

The model's API access features a tiered pricing structure, balancing accessibility with operational sustainability.

DeepSeek recently gained attention for outperforming major tech companies' AI models at significantly lower costs, highlighting its competitive edge.