DeepSeek R1 vs DeepSeek V3: Which is better at coding?

Testing methodology and scope: A comprehensive evaluation of DeepSeek’s V3 and R1 models was conducted by a journalist at ZDNET using four established coding challenges that have previously been used to benchmark other AI models.

• The testing framework included writing a WordPress plugin, rewriting a string function, debugging code, and creating a complex automation script
• Both V3 and R1 variants were evaluated against identical criteria to ensure consistent comparison
• The assessment focused on code accuracy, functionality, and practical implementation

Performance breakdown: DeepSeek V3 emerged as the stronger performer, successfully completing three out of four challenges while R1 managed two successful completions.

• Both models excelled at WordPress plugin development and bug identification tasks
• V3 demonstrated superior capability in string function manipulation, while R1 showed potential stability issues
• Neither model could successfully navigate the complex automation script involving AppleScript, Chrome, and Keyboard Maestro integration

Technical strengths and limitations: DeepSeek’s performance indicates capabilities comparable to GPT-3.5, while highlighting areas for potential improvement.

• The code output tends toward verbosity, often including unnecessary complexity
• Both models demonstrate strong fundamental programming knowledge
• The platform shows impressive capabilities despite operating with reduced infrastructure compared to major competitors

Competitive positioning: DeepSeek’s performance relative to established AI coding assistants reveals promising potential in the development tools landscape.

• The V3 model outperformed several major platforms including Gemini, Copilot, Claude, and Meta’s offerings
• Its open-source nature provides transparency and potential for community-driven improvements
• The platform achieves competitive results while maintaining a lighter infrastructure footprint

Future outlook: While DeepSeek shows promise as an emerging development tool, several factors will influence its adoption and evolution in the AI coding assistant space.

• The platform’s ability to maintain competitive performance while using fewer resources suggests potential for efficient scaling
• Continued development could address current limitations in handling complex, multi-platform automation tasks
• The open-source approach may accelerate improvement through community contributions and transparency