AI Models Tested: Building Tetris

In a practical test to evaluate AI models’ capabilities in building a Tetris game, Claude Opus 4.5 from Anthropic delivered a smooth, playable game on the first attempt, showcasing its efficiency and user-friendly experience. GPT-5.2 Pro from OpenAI, despite its high cost and extended reasoning capabilities, produced a bug-ridden game initially, requiring additional prompts to fix issues, yet still offering a less satisfying user experience. DeepSeek V3.2, while the most cost-effective option, failed to deliver a playable game on the first try but remains a viable choice for developers on a budget willing to invest time in debugging. This comparison highlights Opus 4.5 as the most reliable for day-to-day coding tasks, while DeepSeek offers budget-friendly solutions with some effort, and GPT-5.2 Pro is better suited for complex reasoning tasks rather than simple coding projects. This matters because it helps developers choose the right AI model for their needs, balancing cost, efficiency, and user experience.

In the rapidly evolving world of artificial intelligence, developers and tech enthusiasts are constantly seeking the most efficient and cost-effective models to integrate into their projects. The task of building a fully functional Tetris game with a single prompt serves as an intriguing benchmark for evaluating AI capabilities. This exercise is not just about creating a game; it highlights the practical challenges and considerations when selecting AI models for real-world applications. The comparison of Claude Opus 4.5, GPT-5.2 Pro, and DeepSeek V3.2 offers insights into how these models perform under specific conditions and their suitability for different types of tasks.

Claude Opus 4.5 emerges as a standout performer, delivering a smooth and playable Tetris game on the first attempt. This model’s ability to produce a complete and enjoyable user experience efficiently underscores its potential for day-to-day coding tasks. The success of Opus 4.5 in this context matters because it demonstrates the importance of balancing cost, reliability, and user experience in AI model selection. Developers looking for a dependable tool for coding tasks can find value in Opus 4.5’s performance, as it minimizes the need for iterative debugging and reduces overall development time.

On the other hand, GPT-5.2 Pro, despite being a flagship model from OpenAI, struggled to deliver a flawless game on the first try. This highlights a crucial point about AI model selection: more expensive or advanced models do not always guarantee better results for every task. GPT-5.2 Pro’s strengths lie in complex reasoning and scientific research, suggesting that it may be over-engineered for simpler tasks like building a Tetris game. This distinction is vital for developers to understand, as it can guide them in choosing the right tool for the right job, optimizing both cost and efficiency.

DeepSeek V3.2 presents itself as a budget-friendly alternative, albeit with the trade-off of requiring multiple iterations and debugging efforts to achieve a playable game. This model’s affordability makes it an attractive option for developers with limited budgets who can afford to invest time in refining their output. The analysis of these models emphasizes the importance of aligning AI capabilities with project requirements, budget constraints, and the desired level of output quality. As AI continues to advance, such practical evaluations will be essential for making informed decisions in the tech industry.

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