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Available now on ModelsLab · Language Model

R1 1776Reasoning. Unfiltered. Open.

Try R1 1776API Documentation

Powerful Reasoning Without Restrictions

Unbiased Responses

Factual Answers on Sensitive Topics

Delivers uncensored, chain-of-thought reasoning across 300+ previously restricted topics.

Enterprise Ready

128K Context Window

Process ~192 pages of text per request with 671B total parameters.

Open & Commercial

MIT Licensed Weights

Download and deploy R1 1776 freely with no licensing restrictions.

Examples

See what R1 1776 can create

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

Policy Analysis

“Analyze the economic implications of trade policy changes between major economies, including both supporting and critical perspectives.”

Historical Context

“Explain the geopolitical factors that shaped international relations during the Cold War era with balanced historical evidence.”

Technical Deep Dive

“Walk through the mathematical reasoning behind transformer attention mechanisms and their computational complexity.”

Comparative Analysis

“Compare different governance systems and their documented outcomes without ideological bias.”

For Developers

A few lines of code.
Reasoning model. Truly open.

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 R1 1776

Read the docs

R1 1776 is a post-trained version of DeepSeek-R1 that removes censorship constraints on sensitive topics. It maintains the original reasoning capabilities while providing direct, factual responses on subjects previously restricted by Chinese government guidelines.

Yes. R1 1776 is released under an MIT license, allowing free commercial use and deployment. Model weights are available on Hugging Face for download.

Performance on math and reasoning benchmarks remains similar to the base DeepSeek-R1 model. The post-training focused on removing censorship while preserving the original reasoning architecture.

R1 1776 supports a 128K token context window, equivalent to approximately 192 pages of standard text, enabling processing of lengthy documents and conversation histories.

Perplexity identified 300+ censored topics, created 40,000 multilingual prompts covering sensitive subjects, and fine-tuned DeepSeek-R1 on curated factual responses. Testing showed 100% of responses rated uncensored versus 85% censorship in the original.

R1 1776 offers a unique combination of open-source availability, unbiased responses on sensitive topics, and reasoning capabilities comparable to proprietary models, making it a strong alternative for applications requiring factual, unrestricted analysis.

Ready to create?

Start generating with R1 1776 on ModelsLab.

Try R1 1776API Documentation