DeepSeek R1 is the open-source reasoning model that shocked the AI industry when it matched OpenAI's o1 on math, code, and reasoning benchmarks while costing 27x less to deploy. Released January 20, 2025 by Chinese AI company DeepSeek, R1 was trained via large-scale reinforcement learning (RL) without supervised fine-tuning as a preliminary step - a first for reasoning models. With 671 billion parameters using Mixture-of-Experts architecture (37B active per inference), R1 demonstrates emergent behaviors like self-reflection and step-by-step reasoning. The model is fully open-source under MIT license, meaning you can distill it, modify it, and use it commercially without restrictions.
Key Statistics
671B
Total parameters (MoE)
Source: DeepSeek Paper 2025
37B
Active parameters per inference
Source: DeepSeek Paper 2025
27x
Cheaper than OpenAI o1
Source: DeepSeek Pricing 2026
86.7%
AIME 2024 with majority voting
Source: DeepSeek Benchmarks
Pros and Cons
✓ Pros
- • Matches OpenAI o1 on reasoning benchmarks
- • 27x cheaper deployment cost than commercial alternatives
- • MIT license with full commercial rights - no restrictions
- • Transparent reasoning in <think> tags you can inspect
- • OpenAI SDK compatible for trivial integration
- • Open weights for self-hosting and fine-tuning
- • Multiple distilled variants for different compute budgets
✗ Cons
- • Higher latency than non-reasoning models (seconds not milliseconds)
- • Not suitable for simple tasks - overkill and expensive
- • Longer outputs due to reasoning chains
- • May require prompt adjustment from o1
Benchmark Performance
DeepSeek R1 achieves performance comparable to OpenAI o1 across key reasoning benchmarks. On AIME 2024 (math olympiad problems), the pass@1 score increased from 15.6% to 71.0%, and with majority voting further improved to 86.7% - matching OpenAI-o1-0912. On Codeforces (competitive programming), R1 scores 96.3. On GPQA-Diamond (graduate-level science questions), it achieves 71.5. On MATH-500 (mathematical problem solving), it reaches 97.3. For general knowledge, MMLU scores 90.8. The May 2025 update (R1-0528) pushed performance even further, approaching OpenAI o3 and Gemini 2.5 Pro levels while cutting hallucination by 45-50%.
- → AIME 2024: 71.0% pass@1 (86.7% with majority voting)
- → Codeforces: 96.3 score
- → GPQA-Diamond: 71.5 score
- → MATH-500: 97.3 score
- → MMLU: 90.8 score
How R1 Reasoning Works
Unlike traditional LLMs that produce immediate responses, R1 uses extended chain-of-thought reasoning visible in <think> tags. When you ask R1 a complex question, it first reasons through the problem step-by-step in the thinking phase, then delivers a final answer. The average token usage during reasoning nearly doubled in the 0528 update - from 12K to 23K tokens per AIME question - indicating deeper reasoning. This transparent reasoning makes R1 valuable for applications where you need to understand HOW the model reached its conclusion, not just the answer.
- → Reasoning appears in <think>...</think> tags
- → Self-reflection and error correction during reasoning
- → 12K-23K tokens typical for complex math problems
- → Reasoning is billed as output tokens (more expensive)
R1-Distill Variants
DeepSeek also released distilled versions of R1 for resource-constrained environments. R1-Distill-Qwen-1.5B is a tiny 1.5B parameter model distilled from R1's reasoning capabilities. R1-Distill-Qwen-7B and R1-Distill-Llama-8B offer mid-range options. R1-Distill-Qwen-32B and R1-Distill-Llama-70B provide high capability with lower cost than full R1. These distilled models capture R1's reasoning patterns in smaller packages that run faster and cheaper.
- → R1-Distill-Qwen-1.5B: Smallest, runs on consumer hardware
- → R1-Distill-Qwen-7B / R1-Distill-Llama-8B: Good balance
- → R1-Distill-Qwen-32B: High quality reasoning
- → R1-Distill-Llama-70B: Closest to full R1 capability
Key Features
deepseek r1 offers these core capabilities:
- → 671B total parameters with 37B active per inference (MoE architecture)
- → Chain-of-thought reasoning visible in <think> tags for transparency
- → MIT licensed - distill, modify, and commercialize freely
- → Matches OpenAI o1 on math (AIME 86.7%), code (Codeforces 96.3), and reasoning
- → OpenAI SDK compatible - change base URL and you're running R1
- → 27x cheaper than OpenAI o1 to deploy
- → Self-reflection and emergent reasoning behaviors
- → Distilled variants from 1.5B to 70B parameters
Use Cases
Here are the most common ways people use deepseek r1:
- → Complex mathematical problem solving (olympiad level)
- → Competitive programming and algorithmic challenges
- → Multi-step code generation, debugging, and review
- → Scientific reasoning requiring detailed explanations
- → Research where transparent reasoning chains matter
- → Cost-effective alternative to OpenAI o1 for reasoning tasks
- → Running with OpenClaw for intelligent agent reasoning
Getting Started
Follow these steps to set up deepseek r1:
- → Create account at platform.deepseek.com and get API key
- → Install OpenAI SDK: pip install openai
- → Set base_url='https://api.deepseek.com' in client initialization
- → Use model='deepseek-reasoner' for R1 reasoning mode
- → Send request - reasoning appears in <think> tags before answer
- → For local: Download from HuggingFace and run with vLLM or Ollama
Official Resources
Key Takeaways
- → Matches OpenAI o1 on reasoning benchmarks
- → 27x cheaper deployment cost than commercial alternatives
- → MIT license with full commercial rights - no restrictions
- → Transparent reasoning in <think> tags you can inspect
Related Searches
Frequently Asked Questions
How does DeepSeek R1 compare to OpenAI o1?
DeepSeek R1 achieves performance comparable to OpenAI o1 across math, code, and reasoning tasks. It's significantly cheaper (around 10x less expensive) while being fully open-source with MIT licensing.
What are the <think> tags?
DeepSeek R1 shows its reasoning process in <think> tags within its response. The model works through the problem step-by-step in these tags, then provides the final answer after </think>. This transparency helps you understand how it reached its conclusion.
Should I use R1 or regular DeepSeek?
Use R1 (deepseek-reasoner) for complex reasoning, math, or multi-step problems. Use DeepSeek V3 (deepseek-chat) for general conversation and simpler tasks. R1 has higher latency and cost due to its reasoning process.
Can I run DeepSeek R1 locally?
Yes, the model weights are available on Hugging Face. You'll need significant GPU resources (671B params). Quantized versions work with llama.cpp for more accessible hardware requirements.
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