DeepSeek R1 Guide: Architecture, Benchmarks, and Practical Usage in 2026

DeepSeek R1 proved that open-source models can match closed-source reasoning capabilities. Released in January 2025 under the MIT license, it scores 79.8% on AIME 2024 and 97.3% on MATH-500, putting it in the same tier as OpenAI's o1 series.

A year later, R1 remains one of the most cost-effective reasoning models available. At $0.55/$2.19 per 1M tokens, it's 5-10x cheaper than comparable closed-source alternatives. Here's what you need to know to use it effectively.

Architecture: Why 671B Parameters Doesn't Mean 671B Cost

DeepSeek R1 uses a Mixture of Experts (MoE) architecture:

671 billion total parameters
37 billion activated per forward pass
Built on DeepSeek-V3-Base foundation
128K token context window

The MoE design means R1 has the knowledge capacity of a 671B model but the inference cost of a ~37B model. Each input token activates only a subset of "expert" networks, keeping compute requirements manageable.

For comparison: running a dense 671B model would require ~1.3TB of memory. R1's MoE architecture brings this down to ~336GB at Q4 quantization, making it runnable on high-end consumer hardware (Mac Studio M3/M5 Ultra with 512GB).

Benchmark Performance

Mathematics

Benchmark	DeepSeek R1	OpenAI o1	Claude Opus 4.6
AIME 2024	79.8%	83.3%	~65%
MATH-500	97.3%	96.4%	~90%
Codeforces Elo	2,029	1,891	~1,600

R1 matches or exceeds o1 on most mathematical benchmarks. The Codeforces rating of 2,029 places it in the "Candidate Master" range, competitive with strong human programmers.

Coding

R1 is strong at algorithmic coding (competitive programming, mathematical proofs) but less optimized for software engineering tasks (multi-file refactoring, API design). On SWE-Bench Verified, Claude Sonnet 4.6 (72.7%) significantly outperforms R1.

Use R1 for algorithm implementation and mathematical code. Use Claude or GPT-5 for general software engineering.

Reasoning

R1's chain-of-thought reasoning is transparent and inspectable. Unlike closed-source models where reasoning happens in a hidden "thinking" phase, R1's reasoning traces are part of the output. This makes it valuable for:

Debugging reasoning errors (you can see where the model went wrong)
Educational applications (students can follow the reasoning process)
Research (analyzing how LLMs approach problems)

Training Innovation: Pure RL Without Human Labels

R1's training approach was its most significant contribution to the field.

Traditional approach: collect human-labeled reasoning examples, then fine-tune the model to imitate them.

DeepSeek's approach: train via large-scale reinforcement learning without any supervised reasoning data. The model (DeepSeek-R1-Zero) developed self-verification, reflection, and long chain-of-thought reasoning through RL alone.

The practical implication: R1 demonstrated that reasoning capabilities can emerge from RL training without expensive human annotation. This opened the door for other labs to train reasoning models more efficiently.

The final R1 model uses a two-stage pipeline:

RL stages to develop reasoning patterns
SFT (supervised fine-tuning) stages to clean up output quality and reduce issues like repetition and language mixing

Practical Usage

When to Use R1

Mathematical proofs and derivations
Competitive programming problems
Algorithm design and optimization
Data analysis requiring step-by-step reasoning
Research tasks where transparent reasoning matters
Budget-conscious applications that need reasoning capability

When Not to Use R1

General software engineering (use Claude Sonnet 4.6)
Creative writing (use Claude or GPT-5)
Quick Q&A where reasoning overhead is unnecessary (use GPT-4.1-mini)
UI/frontend code generation (R1 is weaker here)
Tasks requiring up-to-date information (R1's training data has a cutoff)

Optimizing R1 Usage

R1's reasoning traces can be verbose. A simple math problem might generate 500+ tokens of chain-of-thought before the final answer. Tips to manage this:

Set max_tokens appropriately. R1 outputs can be 3-5x longer than non-reasoning models for the same task.
Parse the final answer. R1 typically wraps its conclusion in a clear format after the reasoning trace.
Use distilled versions for simpler tasks. DeepSeek offers R1 distilled at 1.5B, 7B, 8B, 14B, 32B, and 70B parameters. The 32B and 70B versions retain most reasoning capability at much lower cost.

Pricing Comparison

Model	Input / 1M	Output / 1M	Reasoning capability
DeepSeek R1	$0.55	$2.19	Strong (79.8% AIME)
OpenAI o3	$2.00	$8.00	Strong (~83% AIME)
Claude Opus 4.6	$5.00	$25.00	Good (~65% AIME)
OpenAI o4-mini	$1.10	$4.40	Good (optimized for speed)

R1 is 4x cheaper than o3 on input and 4x cheaper on output. For workloads where reasoning quality is comparable (math, algorithms), R1 offers significant cost savings.

Open Source Ecosystem

R1 is MIT licensed. You can:

Use it commercially without restrictions
Fine-tune it on your own data
Distill it to train smaller models
Run it locally (requires ~336GB RAM at Q4 for the full model)
Deploy it on your own infrastructure

Available distilled versions:

Version	Parameters	Use case
R1-Distill-Qwen-1.5B	1.5B	Edge devices, mobile
R1-Distill-Qwen-7B	7B	Local development, testing
R1-Distill-Llama-8B	8B	Local development
R1-Distill-Qwen-14B	14B	Production (light reasoning)
R1-Distill-Qwen-32B	32B	Production (strong reasoning)
R1-Distill-Llama-70B	70B	Production (near-full capability)

The 32B distilled version is the sweet spot for most production deployments: strong reasoning at a fraction of the full model's cost.

Getting Started

Via API

from openai import OpenAI

client = OpenAI(
    api_key="sk-lemon-xxx",
    base_url="https://api.lemondata.cc/v1"
)

response = client.chat.completions.create(
    model="deepseek-r1",
    messages=[{
        "role": "user",
        "content": "Prove that the sum of the first n odd numbers equals n²."
    }],
    max_tokens=4096  # R1 reasoning traces can be long
)

print(response.choices[0].message.content)

Running Locally

# Via Ollama (requires ~336GB RAM for full model)
ollama pull deepseek-r1:671b-q4

# Or use the 32B distilled version (requires ~20GB RAM)
ollama pull deepseek-r1:32b

What's Next: DeepSeek V3 and Beyond

DeepSeek V3 (the non-reasoning successor) has already been released with improved general capabilities. The DeepSeek team continues to push the boundary of what open-source models can achieve.

For reasoning tasks, R1 remains the best open-source option. For general tasks, DeepSeek V3 at $0.28/$0.42 per 1M tokens is one of the most cost-effective models available.

Both are accessible through LemonData with a single API key. $1 free credit on signup.

Benchmarks as of February 2026. DeepSeek R1 weights available at huggingface.co/deepseek-ai.