DeepSeek R1

DeepSeek R1 uses reinforcement learning to train the model to perform extended chain-of-thought reasoning, generating intermediate reasoning steps that are visible to users before the final answer. The model learns to decompose complex problems into sequential logical steps through RL optimization rather than traditional supervised fine-tuning, enabling transparent reasoning traces that show the model's problem-solving process.

DeepSeek R1 achieves 79.8% accuracy on AIME 2024 (American Invitational Mathematics Examination), a benchmark of advanced high-school mathematics requiring multi-step reasoning, symbolic manipulation, and proof construction. The model handles algebraic equations, geometry, number theory, and combinatorics through its RL-trained reasoning capability combined with mathematical knowledge from training data.

DeepSeek R1 exposes intermediate reasoning steps as visible traces in the output, enabling users and developers to inspect the model's problem-solving process, verify logical correctness, and debug incorrect answers. The reasoning traces show the model's decomposition of problems into sub-steps, intermediate conclusions, and decision points.

DeepSeek R1 generates correct solutions to competitive programming problems with a Codeforces rating of 2029 (equivalent to expert-level competitive programmer), handling algorithm design, data structure selection, and edge case handling through extended reasoning. The model produces syntactically correct, optimized code in multiple languages with reasoning traces explaining the algorithmic approach.

DeepSeek R1 provides distilled variants at 1.5B, 7B, 8B, 14B, 32B, and 70B parameters, enabling deployment across different hardware constraints and latency requirements. These models are derived from the 671B base model through knowledge distillation, trading reasoning depth for inference speed and memory efficiency while maintaining reasoning capability.

DeepSeek R1 is released under the MIT license, enabling unrestricted commercial use, modification, and redistribution. The full model weights are publicly available, allowing developers to deploy locally, fine-tune, and integrate into proprietary systems without licensing restrictions or API dependency.

DeepSeek R1 is accessible via a web interface at deepseek.com and native mobile applications (iOS/Android), with a free tier enabling users to interact with the model without payment. The interface supports real-time conversation with visible reasoning traces and response streaming.

DeepSeek R1 is available via an API through the DeepSeek Open Platform, enabling programmatic integration into applications. The API supports model selection (base and distilled variants), streaming responses, and integration with standard ML frameworks, though specific endpoint specifications, authentication methods, rate limits, and pricing tiers are not documented.

DeepSeek R1 claims performance parity with OpenAI o1 on scientific reasoning benchmarks (specific benchmarks and scores not documented), enabling the model to handle physics, chemistry, biology, and other scientific domains through extended reasoning. The model applies domain knowledge and logical inference to scientific problem-solving.

DeepSeek R1 supports multiple languages with a primary interface in Chinese and documented English support. The model processes reasoning tasks across languages, maintaining reasoning capability and trace visibility regardless of input language, though language-specific performance variations are not documented.

DeepSeek R1 uses a Mixture of Experts (MoE) architecture with 671B total parameters but only 37B active parameters during inference. This sparse activation pattern enables efficient inference by routing inputs to specialized expert subnetworks, reducing computational cost and latency compared to dense models of equivalent capability.