DeepSeek-R1 is a first-generation reasoning model developed by DeepSeek AI that comes in two main variants: DeepSeek-R1-Zero and DeepSeek-R1. Built on a Mixture-of-Experts (MoE) architecture with 671B total parameters and 37B activated parameters, it represents a significant breakthrough in AI reasoning capabilities. The model is designed to handle complex reasoning tasks through chain-of-thought processes and can work with a context length of 128K tokens. It's available both through DeepSeek's chat platform and as an open-source model, with multiple distilled versions ranging from 1.5B to 70B parameters based on Llama and Qwen architectures.

DeepSeek-R1 is an advanced open-source AI reasoning model that achieves performance comparable to OpenAI's o1 model across math, code, and reasoning tasks. It was trained using large-scale reinforcement learning and features a unique architecture that enables step-by-step reasoning, self-verification, and reflection capabilities. The model has been distilled into smaller versions based on Llama and Qwen, making it more accessible while maintaining strong performance. Advanced Reasoning Capabilities: Employs chain-of-thought reasoning with self-verification and reflection patterns, allowing for transparent step-by-step problem-solving Large-Scale RL Training: First open research to validate that reasoning capabilities can be developed purely through reinforcement learning without supervised fine-tuning Flexible Model Options: Available in multiple sizes through distillation (1.5B to 70B parameters), offering options for different computational requirements while maintaining strong performance Extended Context Length: Supports up to 128K tokens context length, enabling processing of longer inputs and generating more detailed responses

Advanced Mathematics Problem Solving: Excels at solving complex mathematical problems, including AIME and MATH-500 benchmarks, with step-by-step reasoning Software Development and Coding: Performs high-level coding tasks, competitive programming problems, and software engineering challenges with strong accuracy Educational Assistance: Helps students and educators by providing detailed explanations and step-by-step problem-solving approaches across various subjects Multilingual Reasoning Tasks: Handles complex reasoning tasks in both English and Chinese, making it valuable for international applications

Open-source and commercially usable under MIT License Performance comparable to proprietary models like OpenAI's o1 Available in multiple sizes for different computational needs

Requires significant computational resources for larger models Temperature setting needs careful tuning to prevent repetitions System prompts not supported - all instructions must be in user prompts

Choose Access Method: You have three options to access DeepSeek-R1: Web Interface, API, or Local Installation Web Interface Access: Visit chat.deepseek.com, log in, and enable the 'DeepThink' button to interact with DeepSeek-R1. Note: Limited to 50 messages per day in advanced mode API Access: 1. Sign up at platform.deepseek.com to get an API key 2. Use the OpenAI-compatible API by specifying model='deepseek-reasoner' 3. Set base_url to https://api.deepseek.com/v1 Local Installation (Distilled Models): Install vLLM or SGLang to run smaller distilled versions locally. For vLLM use: 'vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager' Configure Usage Settings: Set temperature between 0.5-0.7 (0.6 recommended), avoid system prompts, include instructions in user prompts, and for math problems add '\boxed{}' directive Select Model Version: Choose between DeepSeek-R1-Zero (pure RL model), DeepSeek-R1 (full model), or distilled versions (Qwen/Llama based) based on your computational resources Format Prompts: Include all instructions in the user prompt without system prompts. For math problems, request final answers within \boxed{} Generate Multiple Responses: For best results, generate multiple responses and average results when evaluating model performance