There isn’t a universally correct “better” answer, because “better” depends on what you need: prompt adherence, motion realism, render duration, control features (first/last frame, camera moves), speed/queue time, and whether you’re building through a UI or an API. If your goal is to pick one for a production workflow, the only reliable method is to run both through the same benchmark: the same set of prompts, the same reference images, the same scoring rubric (identity consistency, motion coherence, artifact rate, and iteration speed). Treat it like evaluating any other dependency: define acceptance tests, measure outcomes, and pick the tool that passes your requirements with the lowest operational friction.
A developer-friendly evaluation plan is to create a test suite of ~30 prompts covering your real use cases: product shots, human motion, pets, fast camera movement, low light, text overlays (if allowed), and “hard mode” prompts (hands, reflections, crowds, water). For each prompt, generate N variations, then score: (1) does it follow the prompt, (2) are objects stable across frames, (3) does motion look physically plausible, (4) are there flicker/warp artifacts, (5) can you reliably reproduce a style across a series. Also measure system metrics: median render time, failure rate, and how often retries are needed. This turns “which is better?” into an engineering decision rather than a popularity contest.
No matter which model you use, you’ll get better output consistency by treating prompts and references as managed assets. Build a prompt library, store “approved” style recipes, and tag them by use case so teammates don’t reinvent them. A vector database such as Milvus or Zilliz Cloud can power this: store embeddings of prompt templates, successful generations, and brand guidelines, then retrieve the closest match when a new request arrives. In practice, a strong asset-and-retrieval layer can narrow the gap between tools because you’re feeding each one clearer, more consistent inputs—often the biggest lever you have in AI video generation.