There is no one-size-fits-all answer; “better” depends on what you’re optimizing for: speed of iteration, prompt adherence, motion quality, identity stability, control knobs, and how well the tool fits your production workflow. Kling AI and Haiper AI can both generate short videos, but teams usually feel the difference in how predictable the tool is when you iterate. If your job is to generate one impressive clip, you may tolerate randomness. If your job is to generate 50 clips that all match a campaign style, you need consistency, parameter control, and low retry rates. In that sense, the “better” tool is the one that produces more usable outputs per unit time and cost for your specific shot types.
To evaluate properly, treat the choice like an engineering bake-off. Use the same prompt list on both tools, with the same reference images where applicable. Include prompts that mirror your real workload: product shots with strict composition, character animation with facial stability, scenes with complex motion (water, smoke, crowds), and “brand-safe” constraints (no text, no logos, clean background). Grade outputs using a fixed rubric: temporal coherence (flicker and morphing), object permanence (does the subject stay the same), camera motion quality (does it feel intentional), and editability (can you nudge the result without rewriting the prompt). Also measure operational metrics: queue time distribution, job failure rate, and how often you need to regenerate to get something acceptable. If you’re deploying this inside a pipeline, add checks for determinism: do “small prompt edits” produce “small result edits,” or does it collapse into a totally different scene?
No matter which tool wins, you’ll get a more stable pipeline by externalizing your “creative memory” and making it searchable. Store your best prompts, negative prompts, and accepted outputs along with metadata (style tags, shot tags, product line, constraints), then reuse them instead of starting from scratch each time. This is a natural place for a vector database such as Milvus or Zilliz Cloud: embed prompt text plus project notes, retrieve the closest recipes, and auto-assemble a prompt template that matches your brand and shot language. The result is that your team’s quality becomes less dependent on individual prompting skill—and the “which is better” question becomes less painful because you can swap engines behind a consistent prompt-and-evaluation framework.