Evidence-led model guide
SOTA means state of the art, but no video model leads every task. Compare current model strengths against your input, audio, motion, control, speed, and budget requirements.
Developed by Google
Veo is a strong choice when generated audio, cinematic composition, and polished prompt-led output matter more than low-cost iteration.
Developed by OpenAI
Sora is useful for prompt-led visual exploration, narrative concepts, and teams developing a scene before choosing a production model.
Developed by ByteDance
Seedance is a strong choice for prompt-led direction, cinematic framing, and scenes that need a clear sequence of actions or camera changes.
Developed by Kuaishou
Kling is useful for expressive movement, character-led scenes, and image-to-video shots where the subject needs to stay recognizable while motion increases.
Developed by Alibaba
Wan is a practical option for teams that want to test more prompt variations, balance credit cost, and iterate before moving a winning idea into a premium render.
| Comparison | Veo 3.1 | Sora 2 | Seedance 2 | Kling 3 | Wan 2.7 |
|---|---|---|---|---|---|
| Choose it for | Cinematic scenes with audio | Narrative concepts and visual exploration | Story-led clips and directed camera work | Character motion and action-focused clips | Fast drafts and cost-aware iteration |
| Starting input | Text and image references | Text and image references | Text and image references | Text and image references | Text and image references |
| Typical workflow | Ads, dialogue, trailers, social video | Story concepts, social clips, previsualization | Ads, cinematic concepts, story sequences | Social clips, characters, product motion | Concept tests, content batches, variations |
Selection framework
Start with the output requirement. Choose Veo for cinematic video with audio, Sora for visual exploration, Seedance for directed sequences, Kling for expressive motion, or Wan for cost-aware iteration.
Then match the input. Image-to-video is better when the subject, product, or framing must stay close to a reference. Text-to-video is better when you are exploring a scene from scratch.
Run the same short brief across two suitable models. Version, duration, resolution, references, audio, and credit cost can change the result, so use the live generator settings as the source of truth.
SOTA model questions
Direct answers about state-of-the-art models, changing model leaders, audio support, hosted generation, and practical comparison.
SOTA means state of the art: the strongest known performance for a defined task or benchmark at a point in time. In AI video, a model can be SOTA for audio or motion without being the best choice for every workflow.
There is no permanent winner. Veo is a strong fit for audio-led cinematic work, Seedance for directed sequences, Kling for expressive motion, Sora for exploration, and Wan for efficient drafts. Test the same brief before scaling.
The guide follows the model versions, modes, controls, estimated generation time, and credit costs currently available in the product. Always confirm the selected model and live settings before generating.
Audio support depends on the selected model and mode. Models marked Audio expose sound generation when supported; duration, resolution, and audio can change the credit estimate.
Some video models publish weights or code under specific licenses, while others are proprietary services. SOTA AI Video is an independent hosted generator, not a model developer or model-weight distributor.
Use the same prompt, source image, aspect ratio, duration, and resolution. Compare instruction following, motion, subject consistency, audio, generation time, and cost instead of judging a single showcase clip.
SOTA AI Video is not affiliated with OpenAI, Google, ByteDance, Kuaishou, Alibaba, or their AI products. Model names belong to their respective owners, and capabilities can change by version and provider.
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