Model Selection

No single model is a silver bullet. Anyone claiming one model dominates all tasks should be viewed skeptically.

Different Models, Different Strengths

Models have distinct personalities. Some overcomplicate solutions. Some struggle with long tasks. Some excel at prose but fumble at code. Knowing these tendencies helps you pick the right tool.

Long-horizon reasoning: Claude Opus 4.5 represents a leap forward for extended autonomous work—porting codebases, running tests, translating between languages. It can run for hours, self-correct, and stay on track without succumbing to context collapse as quickly as other models.

High-level thinking: OpenAI models can excel at drafting prose, architectural discussions, and “vibe coding” where you want the model to generate a high-level understanding rather than you providing one.

Volatility and Reliability

Model behavior isn’t static. Providers update models, sometimes improving them, sometimes introducing regressions. What worked last month might not work today.

Recent OpenAI releases have shown more volatility. Codex has exhibited issues like selecting wrong tool versions even when explicitly instructed otherwise. These problems may get fixed, or they may not. The point is: test in your environment before committing.

Tooling Differences

Beyond the models themselves, the tools wrapping them vary in capability and reliability. Copilot has tooling limitations. Cursor uses “rules” instead of “skills.” Claude Code pioneered the skills pattern that others are adopting.

You can organize your prompts and instructions in a vendor-agnostic way, papering over these differences until the tooling converges.

The Pattern

Match the model to the task. Use long-horizon models for extended autonomous work. Use conversational models for high-level brainstorming. Test before trusting. And stay flexible—the landscape keeps shifting.