DeepSeek R1
Solve complex reasoning and coding tasks with open-source AI
Target Audience
- AI Developers
- Research Scientists
- Enterprise Tech Teams
- Open-Source Enthusiasts
Overview
DeepSeek R1 is a powerful AI model that tackles advanced math problems, generates code, and reasons through complex challenges. What makes it special? It runs directly in your web browser using cutting-edge WebGPU technology, keeps your data private, and costs 90% less than alternatives like OpenAI. The open-source model comes in multiple sizes (1.5B-70B parameters) and shines in technical tasks - it can outperform 96% of Codeforces competitors and solve advanced math problems with 97% accuracy.
Key Features
MoE Architecture
37B active parameters handle complex tasks efficiently
Cost Efficiency
90-95% cheaper than OpenAI for equivalent tasks
WebGPU Support
Runs locally in browser with offline capability
Benchmark Leader
97.3% accuracy on MATH-500 challenges
Open-Source
MIT-licensed for commercial use and customization
Use Cases
Generate production-grade code
Solve advanced math problems
Research technical documentation
Multilingual NLP applications
AI research experiments
Pros & Cons
Pros
- Open-source model with commercial use rights
- Browser-based operation ensures data privacy
- Outperforms proprietary models in technical benchmarks
- Multiple model sizes for different hardware needs
Cons
- Requires technical knowledge for local deployment
- Limited mobile support (web browser only)
Pricing Plans
deepseek-chat
per million tokensFeatures
- 64K context length
- 8K max output tokens
- Cache-optimized pricing
deepseek-reasoner
per million tokensFeatures
- 32K CoT tokens
- 128K context support
- Advanced reasoning capabilities
Pricing may have changed
For the most up-to-date pricing information, please visit the official website.
Visit websiteFrequently Asked Questions
How does DeepSeek R1 compare to OpenAI?
Offers equivalent reasoning capabilities at 90-95% lower cost while being open-source
Can I run this locally?
Yes, through vLLM/SGLang with distilled models from 1.5B to 70B parameters
What makes the architecture unique?
Uses pure reinforcement learning with MoE (37B active/671B total parameters)
Reviews for DeepSeek R1
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