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.
Large language models (LLMs) can be overkill for simple text classification tasks that require straightforward, deterministic outcomes, such as determining whether a message is a lead or not. The use of LLMs in such scenarios can lead to high costs, slower response times, and non-deterministic outputs, without leveraging user feedback to improve the model. By replacing the LLM with a simpler system using sentence embeddings and an online classifier, the process becomes more efficient, cost-effective, and responsive to user feedback, with the added benefit of complete control over the learning loop. This highlights the importance of choosing the right tool for the task, reserving LLMs for tasks requiring complex reasoning or handling ambiguous language.