An effective AI development workflow involves treating models as a team of specialists. Use Claude as the reliable 'workhorse' for building an application from the ground up, while leveraging models like Gemini or GPT-4 as 'advisory models' for creative input and alternative problem-solving perspectives.

A common pattern for developers building with generative media is to use two types of models. A cheaper, lower-quality 'workhorse' model is used for high-volume tasks like prototyping. A second, expensive, state-of-the-art 'hero' model is then reserved for the final, high-quality output, optimizing for cost and quality.

It's counterintuitive, but using a more expensive, intelligent model like Opus 4.5 can be cheaper than smaller models. Because the smarter model is more efficient and requires fewer interactions to solve a problem, it ends up using fewer tokens overall, offsetting its higher per-token price.

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.

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 costs, users configure powerful models like Claude Opus as the 'brain' to strategize and delegate execution tasks (e.g. coding) to cheaper, specialized models like ChatGPT's Codec, treating them as muscles.

Don't pay for Claude's most expensive tier just for coding. A hybrid approach uses the cheaper Claude Pro plan for its superior file-handling and writing. For heavy coding, switch to the terminal inside Cursor, which provides access to top models like Opus for only $20/month, creating a powerful stack for under $40.

An emerging rule from enterprise deployments is to use small, fine-tuned models for well-defined, domain-specific tasks where they excel. Large models should be reserved for generic, open-ended applications with unknown query types where their broad knowledge base is necessary. This hybrid approach optimizes performance and cost.

To optimize AI costs in development, use powerful, expensive models for creative and strategic tasks like architecture and research. Once a solid plan is established, delegate the step-by-step code execution to less powerful, more affordable models that excel at following instructions.