The latest models from Anthropic (Opus 4.6) and OpenAI (Codex 5.3) represent two distinct engineering methodologies. Opus is an autonomous agent you delegate to, while Codex is an interactive collaborator you pair-program with. Choosing a model is now a workflow decision, not just a performance one.

Unlike models that immediately generate code, Opus 4.5 first created a detailed to-do list within the IDE. This planning phase resulted in a more thoughtful and functional redesign, demonstrating that a model's structured process is as crucial as its raw capability.

OpenAI recommends a bifurcated approach. Startups building bleeding-edge, code-focused agents should use the specialized Codex model line, which is highly opinionated and optimized for its tool harness. Applications requiring more general capabilities and steerability across various tools should use the mainline GPT model instead.

Many AI tools expose the model's reasoning before generating an answer. Reading this internal monologue is a powerful debugging technique. It reveals how the AI is interpreting your instructions, allowing you to quickly identify misunderstandings and improve the clarity of your prompts for better results.

AI models develop strong 'habits' from training data, leading to unexpected performance quirks. The Codex model is so accustomed to the command-line tool 'ripgrep' (aliased as 'rg') that its performance improves significantly when developers name their custom search tool 'rg', revealing a surprising lack of generalization.

The differing capabilities of new AI models align with distinct engineering roles. Anthropic's Opus 4.6 acts like a thoughtful "staff engineer," excelling at code comprehension and architectural refactors. In contrast, OpenAI's Codex 5.3 is the scrappy "founding engineer," optimized for rapid, end-to-end application generation.

Effective prompting requires adapting your language to the AI's core design. For Anthropic's agent-based Opus 4.6, the optimal prompt is to "create an agent team" with defined roles. For OpenAI's monolithic Codex 5.3, the equivalent prompt is to instruct it to "think deeply" about those same roles itself.

The comparison reveals that different AI models excel at specific tasks. Opus 4.5 is a strong front-end designer, while Codex 5.1 might be better for back-end logic. The optimal workflow involves "model switching"—assigning the right AI to the right part of the development process.

To optimize AI agent costs and avoid usage limits, adopt a “brain vs. muscles” strategy. Use a high-capability model like Claude Opus for strategic thinking and planning. Then, instruct it to delegate execution-heavy tasks, like writing code, to more specialized and cost-effective models like Codex.