DeepSeek-V3 vs. R1: Model Comparison Guide

Recently, two DeepSeek models (DeepSeek-V3 and DeepSeek-R1) have gained significant attention in the AI community. DeepSeek-V3, released in December 2024, features a proprietary Mixture of Experts (MoE) architecture with 671 billion parameters and has demonstrated exceptional performance across multiple benchmarks. In late January 2025, DeepSeek introduced the DeepSeek-R1 model, which excels in mathematics, coding, and natural language reasoning tasks, achieving performance comparable to OpenAI's latest models. The release of DeepSeek-R1 sparked global discussion in the tech community, as its efficient training methodology and relatively low cost challenged traditional AI development paradigms. Additionally, DeepSeek-R1's open-source nature has significantly accelerated its adoption in the AI field.

However, DeepSeek-V3 and DeepSeek-R1 exhibit notable differences in their model positioning, training methodologies, performance characteristics, and use cases.

Note: The DeepSeek-V3 and R1 mentioned below are both 671B versions (full-scale versions).

Key Features:

• A general-purpose AI language model designed for diverse business and research applications
• Implements Mixture of Experts (MoE) architecture with 671B parameters, activating only 37B parameters per computation for optimal performance and efficiency
• Pre-trained on 14.8T tokens, excelling in multilingual processing, content generation, text comprehension, customer service, and knowledge Q&A
• Leverages efficient computational architecture to deliver robust capabilities while reducing operational costs, offering high ROI for AI solutions

Use Cases:

• Customer Service: Automated customer inquiry handling for improved service efficiency
• Content Creation: Generation of marketing copy, fiction, news articles, and more
• Knowledge Base: Quick and accurate information retrieval and data organization
• Voice Assistance: Support for multilingual translation and voice comprehension

Key Features:

• Purpose-built for complex reasoning, mathematical computation, and code generation
• Utilizes dense Transformer architecture with 671B parameters, activating 37B parameters per computation
• Enhanced logical reasoning capabilities, particularly in mathematics, programming, and scientific research
• Training methodology based on Reinforcement Learning (RL), prioritizing complete and accurate reasoning paths over traditional large-scale supervised fine-tuning (SFT)
• Achieves superior reasoning capabilities with minimal annotated data, particularly excelling in code generation, mathematical reasoning, and logical analysis

Use Cases:

• Advanced Mathematics: Solving complex mathematical problems and supporting scientific research
• Code Development: Assisting developers with code generation and optimization
• Logical Analysis: Handling deep reasoning tasks in legal analysis and algorithm design
• Financial Analysis: Supporting quantitative analysis and data modeling for financial analysts

Choose DeepSeek-V3 if you need a versatile AI assistant for daily tasks such as content creation, customer service, and knowledge queries. It offers low computational costs, fast response times, and excellent general-purpose capabilities.

Opt for DeepSeek-R1 if your work involves mathematical computation, code development, or complex logical reasoning. It excels in deep reasoning and complex calculations, providing more precise solutions for specialized problems.

To accommodate diverse usage scenarios, DeepSeek-R1 offers multiple distilled versions for users to choose based on their specific requirements.

Rise CAMP has implemented optimized support for both DeepSeek-V3 and DeepSeek-R1, enabling users to efficiently deploy and run these models on Rise CAMP's unified compute resource management platform. The support includes:

One-Click Deployment

• Pre-configured optimized runtime environments for DeepSeek-V3 and DeepSeek-R1, eliminating manual dependency setup
• Compatible with major GPU platforms (NVIDIA GPU, Ascend NPU, Hygon DCU, etc) worldwide, ensuring optimal performance across different hardware configurations

Intelligent Resource Scheduling

• Implements Mixture-of-Experts (MoE) scheduling for V3 to optimize resource allocation and reduce operational costs
• Provides high-performance inference acceleration for R1, with FP16 and INT8 quantization support to enhance inference efficiency

Flexible Resource Management

• Enables dynamic scheduling of DeepSeek-V3 and DeepSeek-R1 within a shared GPU resource pool to prevent resource underutilization or overload
• Integrates with Rise CAMP's task priority management for optimal resource allocation across large-scale inference and training workloads

Monitoring and Optimization

• Real-time monitoring of key metrics including memory usage, inference latency, and throughput for DeepSeek models
• Leverages Rise CAMP's automated optimization strategies to intelligently adjust resource allocation and improve operational efficiency

Users can deploy DeepSeek-V3 and DeepSeek-R1 directly through Rise CAMP's web interface or API without additional configuration, achieving a comprehensive AI computing solution that offers flexible deployment, efficient inference, and optimized computational costs.