The video compares DeepSeek V4 models with Claude Opus 4.7 and ChatGPT 5.5 on real coding tasks, finding that while DeepSeek is significantly cheaper and shows promise in bug detection, it generally underperforms in coding quality and stability, with issues like crashes and limited creativity. Despite its cost advantage, concerns about data privacy, reliability, and incomplete openness suggest DeepSeek is best used cautiously and selectively alongside more robust models like Claude and ChatGPT.
The video reviews the newly released DeepSeek V4 models, comparing them against Claude Opus 4.7 and ChatGPT 5.5 using real code tests. DeepSeek offers two versions: V4 Pro and V4 Flash. While DeepSeek’s own benchmarks claim superiority in some areas, these appear cherry-picked, as independent benchmarks show DeepSeek generally underperforming compared to Claude and ChatGPT across multiple coding and software engineering tests. The video highlights that DeepSeek’s claimed wins are limited and not representative of overall performance.
Cost-wise, DeepSeek models are significantly cheaper—about ten times less expensive than ChatGPT 5.5 and Claude in some cases. However, the video cautions that the official DeepSeek API routes data through China, raising potential privacy and regulatory concerns. Additionally, while DeepSeek is open weights (allowing users to download and run the model locally), it is not fully open source, and the training data remains unclear. The presenter also discusses the complexities of AI model pricing, including infrastructure costs and profit margins, suggesting that DeepSeek’s lower cost might come with trade-offs.
The video then tests the models on practical coding tasks, starting with redesigning a landing page. Claude produced a cleaner, more polished design, while ChatGPT’s version missed key brand elements. DeepSeek’s redesign was a mixed bag—it changed some parts but largely reused existing code, showing limited creativity or improvement. The presenter was not impressed with DeepSeek’s output and noted technical issues like crashes and retries during the process, which added to the frustration.
Next, the models were tested on bug detection in code. Both Claude and ChatGPT found nine bugs each but focused on different issues. ChatGPT identified more critical configuration and packaging bugs, while Claude highlighted frontend and webhook problems. DeepSeek, despite its lower benchmark rankings, surprisingly found more bugs overall, including some critical ones. However, the model was slow, prone to errors, and sometimes crashed, making it less reliable in practice. The presenter found this bug-finding ability intriguing but remained cautious about fully adopting DeepSeek.
In conclusion, while DeepSeek V4 offers a compelling price advantage and shows some promise in bug detection, it falls short in overall coding performance and stability compared to Claude and ChatGPT. The video emphasizes the importance of considering data privacy, model reliability, and real-world usability alongside cost. The presenter suggests that DeepSeek might be worth integrating into workflows for specific tasks but advises caution due to its current limitations and technical issues.