DeepSeek-R1 is a reasoning-oriented large language model developed by DeepSeek-AI and introduced in January 2025, positioned as the successor to the earlier DeepSeek-R1-Lite-Preview work and framed as a direct response to OpenAI's o1 family. The accompanying arXiv paper, "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning," frames the model around reinforcement learning as the primary mechanism for eliciting chain-of-thought behavior, making it part of a small but growing family of models that treat verifiable reasoning, rather than next-token fluency, as the central training objective. As a text-in, text-out system with open weights, DeepSeek-R1 is intended for developers and researchers who want to inspect, fine-tune, or self-host a model whose reasoning traces are exposed rather than hidden behind a closed API.

An upgraded release in May 2025, DeepSeek-R1-0528, brought measurable improvements over the original launch: tighter benchmark scores, fewer hallucinations, and the addition of structured-output capabilities such as function calling and JSON formatting. These additions broaden the model's practical fit, letting it serve both as a research artifact for studying reinforcement-learned reasoning and as a deployable assistant for tool-augmented workflows. Combined with its open-weight availability, the model is well suited to teams that want a strong general-purpose reasoner without committing to closed, API-only providers, and it has become a reference point for cost-conscious reasoning deployments across the open-source community.