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.

Recognizing there is no single "best" LLM, AlphaSense built a system to test and deploy various models for different tasks. This allows them to optimize for performance and even stylistic preferences, using different models for their buy-side finance clients versus their corporate users.

Instead of relying on a single AI platform, marketers should adopt a 'best-of-breed' approach. The speaker recommends using Claude for its strength in writing, Gemini for real-time research on current events, and ChatGPT for its advanced capabilities in analyzing marketing content and tactics.

Don't rely on a single AI model for all tasks. A more effective approach is to specialize. Use Claude for its superior persuasive writing, Gemini for its powerful analysis and image capabilities, and ChatGPT for simple, quick-turnaround tasks like brainstorming ideas.

Rather than committing to a single LLM provider like OpenAI or Gemini, Hux uses multiple commercial models. They've found that different models excel at different tasks within their app. This multi-model strategy allows them to optimize for quality and latency on a per-workflow basis, avoiding a one-size-fits-all compromise.

Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.

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.

Treat different LLMs like colleagues with distinct personalities. Zevi Arnovitz views Claude as a collaborative dev lead, Codex (GPT) as a brilliant but terse bug-fixer, and Gemini as a creative but chaotic designer. This mental model helps in delegating tasks to the most suitable AI, maximizing their strengths and mitigating their weaknesses.

For professional coding tasks, GPT-5 and Claude are the two leading models with distinct 'personalities'—Claude is 'friendlier' while GPT-5 is more thorough but slower. Gemini is a capable model but its poor integration into Google’s consumer products significantly diminishes its current utility for developers.