"AI coding" involves various tasks like quick Q&A, debugging, and UI design, which means no single AI model can dominate in all scenarios. The article highlights the complexity of selecting AI tools for coding, emphasizing the need to consider specific roles, costs, and product compatibility. It categorizes models based on their functions: fast runners for frequent tasks, deep thinkers for complex issues, and agentic coding models that handle iterative processes. For instance, Haiku 4.5 is recommended for everyday tasks due to its speed and affordability, while Opus 4.5 offers deeper insights for more intricate coding challenges. The article further breaks down the best AI models and products for coding, noting that product performance varies even with the same model. For backend engineering, Cursor is favored because it integrates well with codebases, allowing for efficient edits and testing. In contrast, Builder is ideal for frontend work, where visual verification is key. ChatGPT is highlighted as useful for planning and reasoning but lacks in enforcing code correctness. It notes that models like Codex and Flash are strong contenders for structured workflows, but their effectiveness is tied to the surrounding product environment. The piece stresses the importance of context in AI interactions. The same model can yield different results depending on the product used, underlining the need for a well-thought-out AI stack. Terminal agents like OpenCode provide explicit control over workflows, while Zed emphasizes speed and manual verification for power users. The article paints a clear picture of the evolving landscape of AI tools for coding, helping readers navigate their options based on specific needs and workflows.