CoSupport AI
What is DeepSeek R1?
DeepSeek R1 is a cutting-edge, open-source language model that sets new benchmarks in AI reasoning. Built with a Mixture of Experts (MoE) architecture, it features 37 billion active parameters out of 671 billion total parameters and supports a 128K context length.
This model utilizes advanced reinforcement learning techniques, enabling capabilities such as self-verification and multi-step reflection. DeepSeek R1 provides exceptional performance in mathematical reasoning, code generation, and complex problem-solving while maintaining open-source accessibility with MIT licensing.
Features
- Architecture: MoE (Mixture of Experts) with 37B active/671B total parameters and 128K context length.
- Reinforcement Learning: Implements advanced reinforcement learning for self-verification, multi-step reflection, and human-aligned reasoning.
- Performance - Math: 97.3% accuracy on MATH-500.
- Performance - Coding: Outperforms 96.3% of Codeforces participants.
- Performance - General Reasoning: 79.8% pass rate on AIME 2024 (SOTA).
- Deployment - API: OpenAI-compatible endpoint ($0.14/million tokens).
- Open Source: MIT-licensed weights, 1.5B-70B distilled variants for commercial use.
- Model Ecosystem: Base (R1-Zero), Enhanced (R1), and 6 lightweight distilled models.
Use Cases
- Complex problem-solving
- Mathematical modeling and reasoning
- Production-grade code generation
- Multilingual natural language understanding
- AI research
- Enterprise applications requiring advanced reasoning
FAQs
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How does DeepSeek R1 compare to OpenAI o1 in pricing?
DeepSeek R1 costs 90-95% less: $0.14/million input tokens (cache hit) vs OpenAI o1's $15, with equivalent reasoning capabilities. -
Can I deploy DeepSeek R1 locally?
Yes, DeepSeek R1 supports local deployment via vLLM/SGLang and offers 6 distilled models (1.5B-70B parameters) for resource-constrained environments. -
What safety measures does DeepSeek R1 implement?
Built-in repetition control (temperature 0.5-0.7) and alignment mechanisms prevent endless loops common in RL-trained models. -
Where can I find technical documentation for DeepSeek R1?
Access full specs via the DeepSeek R1 Technical Paper and API docs.
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