DeepSeek R1: The Open-Source Reasoning Model That Shook the AI Industry

DeepSeek R1 is the model that put DeepSeek on the global AI map. Released in January 2025 under an MIT license, it demonstrated that frontier-level reasoning capability could be achieved through reinforcement learning alone — without the massive supervised fine-tuning datasets that Western labs relied on. It matched OpenAI's o1 on major reasoning benchmarks while being openly available for download and self-hosting.

What DeepSeek R1 actually is

R1 is a reasoning model — a class of LLM trained to think through problems step by step before answering, rather than generating responses directly. Like OpenAI's o1, R1 uses chain-of-thought reasoning traces that are visible in the response, showing its work on math proofs, coding problems, and logical puzzles.

The key architectural facts:

• 671B total parameters — Mixture of Experts (MoE) with 37B active per token
• MIT license — weights fully open, commercial use permitted, downloadable from Hugging Face
• Training method — reinforcement learning without supervised fine-tuning on reasoning traces (the key innovation)
• Context window — 128K tokens

The R1-Zero breakthrough

Before R1, DeepSeek released R1-Zero — a model trained purely through reinforcement learning with no human-labeled reasoning demonstrations. R1-Zero spontaneously developed reasoning behaviors: self-verification, backtracking, and reflection. It was the first public demonstration that RL alone could produce sophisticated reasoning.

R1 built on R1-Zero by adding a cold-start supervised fine-tuning phase before RL training, fixing R1-Zero's tendency toward repetition and language mixing while preserving its reasoning gains.

Benchmarks

R1 matched or exceeded o1 on math and coding benchmarks while being open-source and available to run locally — a combination that had not existed before.

The distilled models

DeepSeek also released smaller distilled variants of R1 — dense models (not MoE) ranging from 1.5B to 70B parameters, based on Qwen and Llama architectures. These distilled models inherit R1's reasoning capabilities at a fraction of the compute cost:

Running R1 locally

ollama pull deepseek-r1          # 7B distilled (default)
ollama pull deepseek-r1:14b      # 14B distilled
ollama pull deepseek-r1:32b      # 32B distilled
ollama run deepseek-r1

R1 vs DeepSeek V4

R1 is a reasoning model — optimized for deep thinking on hard problems. DeepSeek V4 Flash is a general-purpose model with optional thinking mode. For most production workloads V4 Flash is the better choice. R1 (or its distilled variants) is better when you need maximum reasoning depth on math, logic, or complex coding problems and want to run it locally.

Data privacy note

The R1 API sends data to DeepSeek's China-based servers. For privacy-sensitive workloads, run the distilled models locally via Ollama or vLLM.