I tested Fable 5 on two coding tasks in parallel with Claude 4.8 and ChatGPT. In those tests, Fable 5 was slightly better. It understood the work, produced strong results, and looked like a genuine step forward.

But two successful tasks measure capability, not operational reliability. I have also seen repeated reports of Fable 5 degrading during real workflows, wasting tokens, breaking pipelines, and consuming time without producing usable results. Some users eventually rolled back to 4.8. At that point the newer model was not merely weaker. It was worse than useless because its output created additional work.

For me, AI-assisted engineering means using Codex and other AI coding agents as engineering tools, not as magic autocomplete. The difference matters. Chat can be useful for explanation, brainstorming, and quick drafts, but infrastructure work needs something stricter: changes that can be inspected, tested, reverted, and judged against the original requirements.

My own experience is that I trust OpenAI/Codex-style agents more than Claude Code-style workflows for long-running implementation work. That is a practical tooling judgment, not a universal law. But the public DeepSWE benchmark gives useful external support for the same pattern I see in practice: OpenAI models perform very strongly on long-horizon software tasks, with better requirement completion and strong efficiency under a shared agent harness.