What is DeepSeek-R1 model?

DeepSeek-R1 is a 671B MoE LLM with 37B active parameters. It specializes in reasoning via RL-trained chain-of-thought. Matches o1 on math and code benchmarks.

How does deepseek r1 LLM work?

Uses Mixture-of-Experts on DeepSeek-V3 base. RL incentivizes self-reflection and verification. Outputs start with blocks.

What are DeepSeek-R1 API benefits?

Efficient inference for complex tasks. Open-weight under Apache 2.0. Distilled variants from 1.5B to 70B for GPUs.

Is deepseek-r1 a DeepSeek-R1 alternative?

DeepSeek-R1 serves as open-source alternative to closed reasoning models like o1. Lower training cost at $294K. Full CoT transparency.

What tasks suits deepseek r1 model?

Math proofs, code generation, scientific explanations, multi-step planning. Handles English and Chinese.

Are deepseek r1 alternatives available?

Six distilled DeepSeek-R1 variants exist in Qwen-2.5 and Llama-3 bases. Run on consumer hardware with inherited reasoning.

Available now on ModelsLab · Language Model

DeepSeek-R1
DeepSeek-R1 Reasoning Power

Try DeepSeek-R1 API Documentation

Master Complex Reasoning

MoE Efficiency

671B Parameters Active 37B

Routes queries to specialized experts for scalable inference.

RL Trained

Chain-of-Thought Emerges

Self-verifies and corrects via reinforcement learning without SFT data.

Benchmark Leader

Matches OpenAI o1

Excels in math, coding, science with explicit <think> blocks.

Examples

See what DeepSeek-R1 can create

Copy any prompt below and try it yourself in the playground.

Math Proof

“Prove Fermat's Last Theorem for n=3 using step-by-step chain-of-thought reasoning. Include self-verification and output final proof in <think> tags followed by concise answer.”

Code Debug

“Debug this Python function for sorting linked lists with duplicates: def sortList(head): ... Generate fixed code with explanations in <think> block, test cases, and optimized version.”

Science Explainer

“Explain quantum entanglement in Bell's theorem context. Use chain-of-thought to derive inequalities, self-correct assumptions, and summarize key implications.”

Logic Puzzle

“Solve Einstein's riddle: five houses, colors, nationalities, drinks, smokes, pets. Reason step-by-step in <think>, identify fish owner, verify solution.”

For Developers

A few lines of code.
Reasoning via DeepSeek-R1 API

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

Serverless: scales to zero, scales to millions
Pay per token, no minimums
Python and JavaScript SDKs, plus REST API

API Documentation

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about DeepSeek-R1

Read the docs

Ready to create?

Start generating with DeepSeek-R1 on ModelsLab.