Deepseek R1

Revolutionary Open-Source AI Model for Advanced Reasoning that beats Openai o1

Freemium

Description

DeepSeek R1 represents a significant advancement in artificial intelligence, delivered as an open-source model with state-of-the-art performance in complex reasoning, mathematics, and coding tasks. This innovative model is designed to match, and in some cases exceed, the capabilities of leading proprietary AI solutions while ensuring full accessibility for broader research, development, and commercial use through its MIT license.

At its core, DeepSeek R1 utilizes a sophisticated Mixture of Experts (MoE) architecture, featuring 37 billion activated parameters from a total of 671 billion, and supports a 128K context length. The model is trained using advanced reinforcement learning techniques, which imbue it with capabilities like self-verification and multi-step reflection critical for tackling complex problems. It is accessible via an OpenAI-compatible API and offers various distilled models for efficient local or in-browser deployment.

Key Features

Advanced Reasoning: Implements self-verification, multi-step reflection, and human-aligned reasoning through pure reinforcement learning, without supervised fine-tuning.
High Performance Benchmarks: Achieves 97.3% accuracy on MATH-500, outperforms 96.3% of Codeforces participants, and secures a 79.8% pass rate on AIME 2024.
MoE Architecture: Built on a Mixture of Experts (MoE) system with 37 billion active parameters (out of 671B total) and a 128K context length.
Open Source & Commercial Use: MIT-licensed with full model weights and six distilled variants (1.5B to 70B parameters) available on GitHub for commercial applications.
Cost-Effective API: Offers an OpenAI-compatible API endpoint (for 'deepseek-reasoner' model) with intelligent caching, priced at $0.14/million input tokens (cache hit).
Flexible Deployment: Supports local deployment via vLLM/SGLang and offers distilled models optimized for efficient in-browser inference using WebGPU acceleration.
Chain-of-Thought Visualization: Addresses AI 'black box' challenges by providing visibility into the model's reasoning steps, enhancing transparency.

Use Cases

Solving complex mathematical problems and proofs
Generating and understanding production-grade software code
Advancing AI research in reasoning and natural language processing
Developing multilingual AI applications requiring deep understanding
Building enterprise solutions that demand advanced analytical capabilities and problem-solving

Frequently Asked Questions

What makes DeepSeek-R1’s architecture unique?

DeepSeek R1 uses a MoE (Mixture of Experts) system with 37B active/671B total parameters and 128K context support. It's optimized through pure reinforcement learning, achieving advanced capabilities without supervised fine-tuning.

Can DeepSeek R1 be deployed locally or in a browser?

Yes, DeepSeek R1 supports local deployment using vLLM/SGLang. Additionally, it offers distilled models (e.g., DeepSeek-R1-Distill-Qwen-1.5B, 1.5B-70B parameters) that can run entirely in your browser with WebGPU acceleration.

What are the key cognitive abilities of DeepSeek R1?

DeepSeek R1 excels in advanced reasoning by featuring self-verification and multi-step reflection. It can solve complex problems through a visible chain-of-thought process, enhancing transparency.

How does DeepSeek R1's API pricing compare to models like OpenAI o1?

DeepSeek R1's API (for the 'deepseek-reasoner' model) is significantly more cost-effective, priced approximately 90-95% lower than OpenAI o1. For instance, input tokens for DeepSeek R1 are $0.14 per million (cache hit), while OpenAI o1's are $15 per million.

Is DeepSeek R1 open source and available for commercial use?

Yes, DeepSeek R1 is open source under the MIT license. Its full model weights and various distilled versions are available on GitHub, permitting free use, modification, and commercialization.