DeepSeek R1: A Complete Review of the Reasoning Model That Shocked the Industry

In January 2026, the AI world is still talking about DeepSeek R1 — a reasoning model from China that matches OpenAI o1 but costs 30 times less. This week the company updated its technical documentation, expanding it from 22 to 86 pages. Here's everything you need to know about DeepSeek R1.

What Is DeepSeek R1

DeepSeek R1 is a reasoning model developed by the Chinese company DeepSeek. Its defining feature: the model "thinks out loud" before giving an answer, which lets it solve complex problems that require multi-step reasoning.

Two Versions of the Model

1. DeepSeek-R1-Zero — the base model, trained purely with reinforcement learning (RL), without supervised fine-tuning
2. DeepSeek-R1 — the full version, with cold-start data and improved readability

What "Reasoning" Means

Regular LLMs answer right away, without "thinking." Reasoning models first generate a chain of reasoning (chain of thought) and only then formulate an answer. This is especially effective for:

• Math problems
• Algorithm design
• Logic puzzles
• Scientific calculations
• Formal proofs

Benchmarks: On Par with OpenAI o1

According to DeepSeek's official data and independent tests:

What These Numbers Mean

• AIME — American Invitational Mathematics Examination, some of the hardest math problems around
• MATH-500 — a standard benchmark of math ability
• Codeforces — competitive programming
• GPQA Diamond — difficult PhD-level science questions
• SWE-bench — real-world bug-fixing tasks in open-source projects

DeepSeek R1 has practically caught up with o1 on every key metric.

Price: 30x Cheaper Than o1

Here's why the industry is in shock:

Savings: With heavy usage, DeepSeek R1 cuts your API budget by up to 97%.

Why It's So Cheap

1. Efficient architecture — MoE (Mixture of Experts) activates only the parameters it needs
2. Inference optimization — advanced caching techniques
3. Chinese resources — cheaper compute and engineers
4. Market-capture strategy — low-margin pricing to attract users

How It Works: The Technical Architecture

The updated documentation (86 pages) reveals the details.

Multi-Stage Training Pipeline

DeepSeek R1 is trained in several stages:

Stage 1: Cold-Start Data The model receives initial reasoning examples from human experts. This helps set the right "style" of thinking.

Stage 2: Reinforcement Learning The main stage. The model learns to reason through RL, receiving rewards for correct answers. Interestingly, DeepSeek-R1-Zero was trained ONLY with RL, with no cold-start data, and still delivered impressive results.

Stage 3: Rejection Sampling The model generates many reasoning variants, and the best ones are selected for further training.

Stage 4: Supervised Fine-Tuning Final polishing on the selected examples to improve readability and stability.

R1-Zero's Problems

The documentation is candid about the problems of the version trained without cold-start data:

• Endless repetition — the model sometimes gets stuck in loops
• Poor readability — the reasoning is hard for a human to follow
• Language mixing — it mixes languages within a single answer

R1 (the full version) fixes these problems thanks to the cold-start data.

Distilled Versions

DeepSeek released 6 "lightweight" versions of R1:

Knowledge distillation is a process in which a small model learns to imitate the behavior of a large one. The distilled versions retain most of R1's capabilities but run on ordinary hardware.

How to Run It Locally

With Ollama (the easiest way)

Install Ollama and run:

# 7B version (needs ~8GB VRAM)
ollama run deepseek-r1:7b

# 32B version (needs ~24GB VRAM)
ollama run deepseek-r1:32b

# 70B version (needs ~48GB VRAM)
ollama run deepseek-r1:70b

With vLLM (for production)

vLLM delivers high throughput:

pip install vllm

python -m vllm.entrypoints.openai.api_server \
  --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B \
  --tensor-parallel-size 2

With Hugging Face

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Important: use a special prompt for reasoning tasks
prompt = "<think>\n"  # Start of the reasoning block

Practical Applications

Where R1 Beats Regular LLMs

1. Math and physics — solving olympiad-level problems
2. Algorithms — writing complex algorithms with proofs of correctness
3. Code review — deep analysis of logic errors
4. Research — help with scientific work

Where Regular Models Are a Better Fit

1. Simple tasks — for "write an email," reasoning is overkill
2. Creative work — storytelling, copywriting
3. When speed is critical — reasoning takes time
4. Chatbots — users don't want to wait while the model "thinks"

Censorship and Restrictions

DeepSeek is a Chinese company, and the model has built-in restrictions:

• Refuses to discuss politically sensitive topics (Taiwan, Tiananmen)
• Promotes the PRC's position on certain issues
• Censors content that is banned in China

The distilled versions (especially the Llama-based ones) have fewer restrictions because they are built on Western models.

Impact on the Industry

For OpenAI

DeepSeek R1 is a serious blow to OpenAI's business model. If you can get reasoning 30 times cheaper, why pay for o1?

Possible responses:

• Lower prices for o1
• Faster development of o2
• A focus on enterprise features (security, compliance)

For Open Source

R1 proves that frontier models can be open. This:

• Accelerates research
• Democratizes access to AI
• Reduces dependence on Big Tech

For Developers

More choice, lower prices, more possibilities. Recommendations:

• Test R1 on your own tasks
• Compare it with o1 on quality and price
• Consider running it locally for private data

Conclusion

DeepSeek R1 is one of the most significant models of 2025-2026. It proved that:

1. Reasoning is available to everyone — not just OpenAI subscribers paying $200/month
2. Open source is competitive — MIT license, full access to the weights
3. China is a serious player — despite sanctions and restrictions on GPUs
4. Prices will keep falling — competition is forcing everyone to cut margins

Recommendation: Try DeepSeek R1 on your own tasks. Start with the API (cheap) or a distilled version (free to run locally).