Skip to main content
Available now on ModelsLab · AI Model

DeepSeek R1 (Original)Reason Step by Step

Try DeepSeek R1 (Original)API Documentation

Master Complex Reasoning

MoE Architecture

671B Parameters Efficient

Activates 37B per pass for scalable reasoning in DeepSeek R1 (Original) model.

RL Training

Chain-of-Thought Native

Pure reinforcement learning builds self-verification in DeepSeek R1 (Original) LLM.

Benchmark Leader

Math Coding Superior

Outperforms o1 on AIME and MATH via DeepSeek R1 (Original) API.

Examples

See what DeepSeek R1 (Original) can create

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

Math Proof

“Solve this AIME-level math problem step by step: Prove that the sum of the first n odd numbers is n squared, using induction and provide numerical verification for n=5.”

Code Debug

“Write Python code to implement binary search on a sorted array, then debug this faulty version: def binary_search(arr, target): ... and explain fixes with reasoning.”

Logic Puzzle

“Solve the Einstein riddle: Five houses in different colors, owners of different nationalities drink different beverages, smoke different brands, and keep different pets. Who owns the fish? Reason chain by chain.”

Science Reasoning

“Explain quantum entanglement step by step, derive Bell's inequality violation, and discuss EPR paradox implications with mathematical formalism.”

For Developers

A few lines of code.
Reasoning LLM. One Call.

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 (Original)

Read the docs

DeepSeek R1 (Original) is an open-source LLM with 671B MoE parameters, trained via pure RL for chain-of-thought reasoning. It excels in math and coding benchmarks. Use DeepSeek R1 (Original) API for efficient inference.

Send prompts to the LLM endpoint for step-by-step reasoning outputs. Activates 37B parameters per pass for speed. Ideal for complex tasks via deepseek r1 original api.

It matches or beats OpenAI o1 on AIME (52.5%) and MATH (91.6%). Provides transparent CoT unlike black-box models. DeepSeek R1 (Original) alternative for reasoning.

Pure RL without initial SFT, plus cold-start data for coherence. Builds self-reflection and verification. Powers DeepSeek R1 (Original) LLM strengths.

Available via ModelsLab LLM endpoint. Supports high-speed processing for real-time apps. Direct integration for DeepSeek R1 (Original) model.

Open-source with superior efficiency and reasoning transparency. Handles multilingual NLP and multi-step planning. Scalable via deepseek r1 original api.

Ready to create?

Start generating with DeepSeek R1 (Original) on ModelsLab.

Try DeepSeek R1 (Original)API Documentation