The core evolution of AIGC has shifted from the "single-model capability competition" to the "multi-model collaborative orchestration" stage. The true gap in efficiency is not whether a certain model performs better on a specific benchmark, but whether it can build a stable pipeline based on task layering:

Strong reasoning models like Claude Opus 4.7 and GPT-5.4 are responsible for understanding requirements, organizing knowledge, breaking down strategies, generating code, and orchestrating workflows; multi-modal models such as GPT-Image-2, Gemini, and Ji Meng handle visual asset generation, style alignment, consistency control, and content expression; then, systems for video, speech, editing, and distribution complete dynamic, productized, and scaled delivery.
Whether it's AI dramas, marketing content, digital humans, e-commerce materials, or software development, the industry's key variables have shifted from "single-point quality generation" to "task routing capability, model collaboration efficiency, asset reuse rate, controllability, and end-to-end delivery efficiency."