Neither is universally “better.” Kling AI and Minimax (often used to refer to Minimax’s video generation products) are both best treated as video generation engines whose real-world value depends on your workflow: prompt adherence, motion consistency, identity stability, edit controls, queue time, and cost per usable clip. If you’re choosing one for a team or a product pipeline, the honest answer is: the better option is the one that scores higher on your test set with your constraints. Video generation quality is not a single number, and one model can look great on cinematic landscape shots but struggle on human motion, while another does the opposite. So the decision should be driven by repeatable evaluation rather than viral demos.
A developer-friendly way to decide is to build a small benchmark harness and grade outcomes. Start with 20–40 prompts that match your actual work (product spins, talking head, character walk cycle, fast camera pan, low light, water/glass reflections, crowds, and hands). For each prompt, run N variations, then score: (1) prompt adherence (did it produce the subject/action you asked for), (2) temporal coherence (flicker, identity drift, weird morphing), (3) controllability (can you push it toward a specific shot language reliably), (4) editability (can you iterate without re-rolling everything), and (5) throughput (median render time, failure rate, queue variance). Track “cost per accepted clip” instead of “cost per generation,” because retries are the hidden budget killer. If you can, add a simple rubric in a spreadsheet and have two reviewers score blindly; subjective preferences are real, but you can still make them consistent.
Once you pick a winner, you’ll still get the biggest reliability gains from your surrounding system. Treat prompts and settings as versioned artifacts: prompt template, negative prompt, reference image hash, parameters, and the final output. This is where a vector database such as Milvus or Zilliz Cloud becomes genuinely useful: embed your best-performing prompts and tag them with metadata like “shot type,” “camera move,” “lighting,” and “brand style,” then retrieve the closest proven recipe for each new request. That reduces wasted iterations and makes output more consistent across a team. In practice, a strong “prompt memory + evaluation” layer often matters more than the model brand—because it turns video generation from ad-hoc prompting into an engineering process.