I also had the chance to test the early access version of GPT 5.6 Sol. After spending time with both models, I think they excel in very different ways. Fable 5 stands out because of the intelligence that comes from its sheer model scale. It constantly surprises you with unexpected directions, proposes alternative approaches, and even refines your ideas before you ask. It feels highly metacognitive, with rich language, expressive writing, and a broad way of thinking. In many ways, it reminds me of the feeling I had when GPT-4.5 first arrived. GPT 5.6 Sol feels completely different. I'm not sure I'd describe it as having the same level of metacognition, but when you give it a concrete problem, it becomes incredibly persistent about finding the best possible solution. It doesn't wander—it stays focused. Where Sol really shines is structured execution. If the workflow is clear, it follows it almost perfectly, breaking complex tasks into logical steps, creating sub-agents when appropriate, and systematically solving the problem from start to finish. It feels less like a creative collaborator and more like a dedicated research engineer. Because of that, I think GPT 5.6 Sol is the ideal model when you already know the problem you're trying to solve. Give it a research paper, a technical specification, or a difficult engineering task, and it feels like it'll keep working until it's implemented correctly. The persistence, execution quality, and overall performance are genuinely impressive. The main reason I kept using Fable was its expansive thinking, writing style, and frontend generation capabilities. But if my goal is coding or solving difficult engineering problems, GPT 5.6 Sol would very naturally become my primary model. And at this point, with AGENTS.md and Skills, we already have the tools to teach GPT the workflows and reasoning patterns we want. Sol follows those instructions with remarkable consistency, which makes it an exceptionally strong foundation for agentic coding.