DeepSeek
DeepSeek is a family of large language models developed by DeepSeek AI, a Chinese AI research company, notable for releasing highly capable models as open-weight releases and for pioneering efficient training and inference techniques that…
Definition
DeepSeek is a family of large language models developed by DeepSeek AI, a Chinese AI research company, notable for releasing highly capable models as open-weight releases and for pioneering efficient training and inference techniques that achieve strong performance at comparatively low reported compute cost. DeepSeek models cover general chat, reasoning, and code-focused variants.
Overview
DeepSeek AI is a Chinese AI research lab that gained significant international attention for releasing large language models that achieved competitive benchmark performance against leading Western models while emphasizing training efficiency. The company has published detailed technical reports describing architectural and systems-level innovations — including mixture-of-experts (MoE) architectures that activate only a subset of model parameters per input to reduce compute cost, and reinforcement-learning-based training approaches for improving reasoning — that contributed to lower reported training costs relative to some competitors. DeepSeek's release strategy has emphasized open weights, making many of its models available for download, self-hosting, and fine-tuning under permissive or semi-permissive licenses, in contrast to the closed, API-only approach of some Western labs. This openness accelerated adoption among researchers, startups, and developers who wanted to run or customize the models directly rather than relying solely on hosted APIs. The company has released distinct model lines for general-purpose chat and coding, as well as models specifically optimized for extended chain-of-thought reasoning on math, science, and logic problems, competing with dedicated reasoning-focused models from other labs. DeepSeek's emergence intensified global discussion about the compute and cost assumptions underlying frontier AI development, and about competition in AI development between US and Chinese labs. As an open-weight model family, DeepSeek models are commonly used as a base for further fine-tuning, distillation into smaller models, and deployment in self-hosted or cost-sensitive environments where full control over inference infrastructure is desired.
Key Features
- Developed by DeepSeek AI, a Chinese AI research lab
- Emphasizes open-weight releases enabling self-hosting and fine-tuning
- Uses mixture-of-experts (MoE) architecture to reduce active compute per inference
- Published detailed technical reports on training efficiency innovations
- Includes dedicated reasoning-focused model variants for math/logic/science tasks
- Competitive benchmark performance reported at comparatively lower training cost
- Popular base model for community fine-tuning and distillation
Use Cases
Alternatives
History
DeepSeek is a Chinese AI company based in Hangzhou, founded in July 2023 by Liang Wenfeng and funded by the quantitative hedge fund High-Flyer, which he also leads. It drew global attention with two releases: DeepSeek-V3 in December 2024, a mixture-of-experts model with 671 billion total parameters and a 128,000-token context, notable for its low training cost; and the DeepSeek-R1 reasoning model on January 20, 2025, which reached near-parity with leading closed reasoning models such as OpenAI's o1 at a fraction of the operating cost. R1's performance and efficiency sparked wide debate about the gap between Chinese and US AI, and DeepSeek released the models with open weights.
Sources
- DeepSeek — Wikipedia · as of 2026-07-17
- IISS — "DeepSeek’s release of an open-weight frontier AI model" (2025) · as of 2026-07-17