Learn more: [original model announcement]

DeepSeek-R1 excels at reasoning tasks using a step-by-step training process, such as language, scientific reasoning, and coding tasks. It features 671B total parameters with 37B active parameters, and 128k context length.

DeepSeek-R1 builds on the progress of earlier reasoning-focused models that improved performance by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining reinforcement learning (RL) with fine-tuning on carefully chosen datasets. It evolved from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and showed strong reasoning skills but had issues like hard-to-read outputs and language inconsistencies. To address these limitations, DeepSeek-R1 incorporates a small amount of cold-start data and follows a refined training pipeline that blends reasoning-oriented RL with supervised fine-tuning on curated datasets, resulting in a model that achieves state-of-the-art performance on reasoning benchmarks.

We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:

• Avoid adding a system prompt; all instructions should be contained within the user prompt.
• For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
• When evaluating model performance, it is recommended to conduct multiple tests and average the results.