DeepSeek R1 vs OpenAI o1: The Battle for AI Reasoning

Table of Contents

1. →System 1 vs System 2 Thinking
2. →The Benchmarks
3. →The Distillation Revolution
4. →Technical Comparison
5. →Pricing Analysis
6. →When to Use Each Model
7. →How to Run DeepSeek R1 Locally
8. →FAQ

For years, AI scaling laws were about "bigger is better." Bigger data, bigger parameters, bigger compute. But in late 2024, OpenAI shifted the paradigm with o1 (Project Strawberry), introducing "Test-Time Compute." The idea: give the model time to "think" before answering.

The industry assumed OpenAI had a multi-year lead. Then, weeks later, DeepSeek released DeepSeek R1. Not only did it match o1's reasoning performance in math and code, but they did something OpenAI didn't: they open-sourced it.

This article breaks down the technical duel between these two "System 2" thinkers.

System 1 vs. System 2 Thinking

To understand R1 vs. o1, we must understand the shift in AI architecture.

• →GPT-4 / Claude 3 (System 1): Fast, intuitive, immediate. Like a human giving a quick answer. Good for writing, summarizing, and standard code.
• →o1 / R1 (System 2): Slow, deliberative, logical. Like a human solving a math proof or debugging a race condition.

When you ask DeepSeek R1 a question, you often see a Thinking... block in the UI. It isn't loading; it is literally generating thousands of tokens of internal monologue—testing hypotheses, catching errors, back-tracking—before it outputs the final answer. This "Chain of Thought" (CoT) is no longer just a prompting technique; it is baked into the model's training via Reinforcement Learning (RL).

The Benchmarks: A Dead Heat?

DeepSeek's release paper claims performance parity with OpenAI's o1 on the hardest AI benchmarks. Here's the data:

Official Benchmark Comparison

Where Each Model Excels

DeepSeek R1 strengths:

• →✅ Mathematical proofs and competition math
• →✅ Algorithmic problem solving (Codeforces, LeetCode)
• →✅ Code generation and debugging
• →✅ Scientific reasoning with clear logic

OpenAI o1 strengths:

• →✅ General knowledge and trivia (MMLU)
• →✅ Creative writing and nuanced responses
• →✅ Following vague or ambiguous instructions
• →✅ Safety alignment and refusal of harmful requests

The catch? R1 is a laser—brilliant at technical tasks. o1 is a Swiss Army Knife that includes a laser plus general-purpose tools.

The "Distillation" Revolution

The most disruptive part of DeepSeek's release wasn't the 671B model—it was the Distilled Models released under MIT license.

DeepSeek used R1 to generate training data (thinking patterns) and taught smaller models to reason. The full lineup:

DeepSeek R1 Distilled Model Family

Key Finding: 32B Beats o1-mini

The DeepSeek-R1-Distill-Qwen-32B model outperforms OpenAI o1-mini on several benchmarks:

This means you can run o1-mini-level reasoning on a single RTX 4090.

Why this matters: Local reasoning agents can now be deployed in privacy-sensitive environments (hospitals, law firms, government) where sending data to OpenAI is impossible. No API calls, no data leakage, full control.

Technical Comparison

Architecture & Specifications

Mixture of Experts (MoE) Explained

DeepSeek R1 uses a Mixture of Experts architecture:

• →671B total parameters, but only 37B activated per token
• →This makes it efficient despite the massive size
• →Comparable inference speed to a 70B dense model
• →Enables high-quality reasoning without prohibitive compute costs

Chain of Thought Visibility

A key difference is reasoning transparency:

OpenAI o1: Shows a summary like "Thought for 23 seconds" but hides the actual reasoning chain. You see the answer, not the process.

DeepSeek R1: Exposes the full <think>...</think> block. You can see:

• →How it breaks down the problem
• →False starts and corrections
• →The complete reasoning trace

This visibility is invaluable for debugging, education, and understanding model behavior.

Pricing Analysis: The 53x Difference

The cost gap between R1 and o1 is staggering:

API Pricing Comparison (January 2026)

Cost Comparison for 1 Million Queries

Assume each query uses 500 input + 1000 output tokens:

Result: DeepSeek R1 is 120x cheaper than o1 for the same workload.

Self-Hosting Economics

If you self-host DeepSeek R1 or its distilled versions:

• →API cost: $0 (you own the hardware)
• →Hardware cost: One-time investment
• →Distilled 32B on RTX 4090: ~$1,600 GPU, unlimited queries

Break-even vs o1 API: ~25,000 queries.

⚠️ Note on o1 Successors: OpenAI has released o3 and o4-mini as successors to o1. However, o1 remains available and this comparison focuses on the original reasoning model matchup.

When to Use R1 vs o1

Choose DeepSeek R1 If:

• →✅ Cost is a priority — 53-120x cheaper than o1
• →✅ You need self-hosting — Data sovereignty, air-gapped environments
• →✅ Technical tasks dominate — Math, coding, algorithmic problems
• →✅ You want visible reasoning — Debug and understand the chain of thought
• →✅ You're building local AI agents — Run distilled models on consumer hardware

Choose OpenAI o1 If:

• →✅ Safety is paramount — Stronger refusal of harmful requests
• →✅ General knowledge matters — Slightly better MMLU scores
• →✅ You need managed infrastructure — No DevOps, just API calls
• →✅ Creative/nuanced tasks — Better at ambiguous instructions
• →✅ Enterprise compliance — SOC2, audit logs, support contracts

Use Both (Recommended for Production)

Many teams use a routing strategy:

1. →Simple queries → Fast cheap model (GPT-4o-mini, DeepSeek V3)
2. →Technical reasoning → DeepSeek R1 (cost-effective)
3. →Safety-critical or creative → OpenAI o1 (maximum alignment)

How to Run DeepSeek R1 Locally

Option 1: Ollama (Easiest)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Download and run DeepSeek R1 distilled (choose your size)
ollama run deepseek-r1:7b      # 7B - needs 8GB VRAM
ollama run deepseek-r1:14b     # 14B - needs 16GB VRAM  
ollama run deepseek-r1:32b     # 32B - needs 24GB VRAM
ollama run deepseek-r1:70b     # 70B - needs 48GB+ VRAM

Option 2: vLLM (Production)

pip install vllm

python -m vllm.entrypoints.openai.api_server \
    --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B \
    --tensor-parallel-size 2 \
    --max-model-len 32768

Option 3: Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Solve: What is the sum of all prime numbers less than 20?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Hardware Requirements Summary

FAQ

General Questions

Q: What is DeepSeek R1?
A: DeepSeek R1 is an open-source reasoning model with 671B parameters (37B activated via MoE) that matches OpenAI o1 on math and coding benchmarks at 53x lower cost.

Q: Is DeepSeek R1 really as good as OpenAI o1?
A: On technical tasks (math, code, logic), yes. On general knowledge and creative tasks, o1 has a slight edge. Both are "System 2" reasoning models.

Q: What's the difference between R1 and R1-Distill models?
A: R1 is the full 671B model (API or large cluster). R1-Distill models (1.5B-70B) are smaller versions trained to mimic R1's reasoning, runnable on consumer hardware.

Pricing Questions

Q: How much does DeepSeek R1 API cost?
A: $0.28 per million input tokens, $0.42 per million output tokens. With cache hits: $0.028/M input.

Q: How much does OpenAI o1 API cost?
A: $15 per million input tokens, $60 per million output tokens. o1-mini is cheaper at $1.10/$4.40.

Q: Can I use DeepSeek R1 for free?
A: Yes, if you self-host. The model weights are MIT licensed. You only pay for hardware.

Technical Questions

Q: What is the context window of DeepSeek R1?
A: 128,000 tokens input, up to 8,000 tokens output (configurable up to 64K with some distilled versions).

Q: Can I fine-tune DeepSeek R1?
A: Yes. The MIT license permits fine-tuning, commercial use, and derivative works.

Q: Does DeepSeek R1 support function calling?
A: Not natively like GPT-4. You can prompt-engineer tool use, but it's not as robust as OpenAI's function calling.

Privacy & Safety Questions

Q: Is DeepSeek R1 safe to use?
A: R1 has moderate guardrails. It may comply with requests that o1 would refuse. Implement your own content filtering for production.

Q: Can I run DeepSeek R1 without sending data to China?
A: Yes. Self-host the model and your data never leaves your infrastructure. This is a key advantage of open-weights models.

Conclusion: The Reasoning Gap Has Closed

DeepSeek R1 has proven that "reasoning" is not a moat protected by secret algorithms. It's a function of Reinforcement Learning and high-quality training data.

For developers and enterprises, this is a win-win:

The bottom line: If you're building math, code, or research applications and cost or privacy matters, DeepSeek R1 is now a serious contender. The 53x price difference is hard to ignore.

🚀 Master Chain of Thought Reasoning

Whether you use o1 or R1, the key to unlocking their power is understanding how they think. In Module 3 — Chain-of-Thought & Reasoning, we dive deep into:

• →How reasoning models differ from standard LLMs
• →Prompting techniques for System 2 thinking
• →Building reasoning chains for complex problems
• →Debugging and validating AI reasoning

📚 Start Module 3: Reasoning | 🎯 Explore All Modules

Related Articles:

• →DeepSeek V3 vs GPT-4o: The 2026 Economic Analysis
• →AI Agents 2026 Panorama: Claude, DeepSeek, Gemini
• →Chain-of-Thought Prompting Explained
• →Claude Cowork: Complete Guide 2026

Official Resources:

• →DeepSeek R1 GitHub Repository
• →DeepSeek R1 on Hugging Face
• →OpenAI o1 Documentation
• →DeepSeek API Pricing

Last Updated: January 28, 2026
Prices and benchmarks verified against official sources.