Kling, Seedance and Vidu Are Taking Different Paths Through AI Video

Chinese AI video models are beginning to take noticeably different product paths. A 36Kr analysis describes Seedance as emphasizing industrialized production, Kling as emphasizing control, and Vidu as emphasizing expressive freedom.

Three different priorities

The article discusses Kling 4.0, Vidu Q4 Preview and the growing focus on lower-cost experimentation. For creators, the practical question is not which model wins every task, but whether a workflow needs repeatable production, detailed control or more room for visual experimentation.

How to read the comparison

The 36Kr article is a publisher analysis, not a controlled benchmark. Its examples and product framing should not be treated as a universal performance ranking. Model versions, credits, export terms and regional access can change, so creators should check the current product pages before choosing a tool.

Source

Read the original 36Kr report. The reported Vidu pricing and product observations remain source-reported, not independently tested by AI Tech Hub.

Practical reader guidance

Readers should test the exact workflow they care about with a small, reversible example. Record the product surface, model or version, account type and input, then review the result against the original material. Teams should decide who can access the feature, who reviews output and what happens when the system is wrong. Developers should use a test environment; creators should check export, attribution and licensing; organisations should review privacy and retention before wider use. Pricing, regional availability and usage limits should be checked on current official documentation before a purchase. This source report is useful context, not a controlled benchmark or proof of a universal winner. A limited pilot with clear success criteria, documented failures and a human decision-maker is the safest way to evaluate a new AI workflow.