{"id":1546,"date":"2026-09-11T14:28:53","date_gmt":"2026-09-11T14:28:53","guid":{"rendered":"https:\/\/ouaggad.com\/blog\/?post_type=ai_model&p=1546"},"modified":"2026-09-11T14:28:53","modified_gmt":"2026-09-11T14:28:53","slug":"what-is-deepseek-model-family","status":"publish","type":"ai_model","link":"https:\/\/ouaggad.com\/blog\/en\/ai\/models\/what-is-deepseek-model-family\/","title":{"rendered":"What Is DeepSeek? A Simple Guide to the AI Model Family"},"content":{"rendered":"

DeepSeek is the name of a family of artificial intelligence models developed by DeepSeek. The family includes general-purpose language models, coding-focused systems, and reasoning models. Some are available through DeepSeek\u2019s chat service and API, while the company also publishes weights and technical reports for several releases.<\/p>\n

What is DeepSeek?<\/h2>\n

DeepSeek is not one fixed model. It is an evolving model family. The line began with DeepSeek LLM and expanded through releases such as DeepSeek Coder, DeepSeek-V2, DeepSeek-V3, and DeepSeek-R1. Newer releases are listed in the company\u2019s official Transparency Center.<\/p>\n

At a basic level, these systems are large language models. They process an input as tokens and generate an output by predicting likely continuations. DeepSeek\u2019s official model disclosure explains that this is probabilistic generation rather than direct retrieval of a stored passage.<\/p>\n

What is the difference between DeepSeek and DeepSeek-R1?<\/h2>\n

DeepSeek is the broader family and service name. DeepSeek-R1 is a specific reasoning model within that family. DeepSeek introduced R1 and R1-Zero for reasoning-intensive tasks. Its official repository notes that R1 was designed to address problems observed in R1-Zero, including repetition, poor readability, and language mixing.<\/p>\n

How did the DeepSeek family develop?<\/h2>\n

DeepSeek released 7B and 67B versions of DeepSeek LLM, followed by specialized and more efficient generations. DeepSeek-V2 adopted a Mixture-of-Experts architecture. DeepSeek-V3 uses 671 billion total parameters while activating 37 billion for each token, according to its official repository and technical report.<\/p>\n

DeepSeek-R1 later placed greater emphasis on multi-step reasoning through reinforcement learning. Because the family continues to change, readers should consult the official model registry when they need the latest release or exact specifications.<\/p>\n

What can DeepSeek do?<\/h2>\n