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Comparing DeepSeek to Popular AI Models: A Visual Analysis in Three Charts


DeepSeek, a Chinese artificial intelligence company, has recently gained attention in the tech industry for its large language models that have outperformed many top AI developers worldwide. One of its models, R1, topped the Apple App Store charts, surpassing OpenAI’s ChatGPT. DeepSeek’s models are more cost-effective and energy-efficient compared to those of U.S. tech giants, using a “mixture-of-experts” system to divide the model into smaller submodels that specialize in specific tasks.

Despite operating with limited resources, DeepSeek’s models are competitive with top American models like OpenAI’s o1 and Google’s Gemini 2.0 Flash. Their models prioritize explanation through chain-of-thought reasoning, allowing users to follow the model’s rationale. DeepSeek’s V3 model, developed at a fraction of the cost of its U.S. counterparts, performed on par with leading AI models upon its release. Their latest model, Janus-Pro-7B, has outperformed OpenAI’s DALL-E and Stable Diffusion’s 3 Medium in various benchmarks.

DeepSeek’s models are cheaper and quicker to train than competitors’, with V3 developed in just two months for under $6 million. The company utilizes a “mixed precision” framework incorporating both 32-bit and 8-bit numbers to save memory and processing time. DeepSeek’s success has prompted a reevaluation of AI development practices, suggesting that more efficient and cost-effective methods are viable.

Note: The image is for illustrative purposes only and is not the original image associated with the presented article. Due to copyright reasons, we are unable to use the original images. However, you can still enjoy the accurate and up-to-date content and information provided.

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