No current video model is the right answer for every job. The differences between them are large enough that picking the wrong one for a given piece of work costs real money — in credits spent on outputs that get discarded, and in time spent fighting a model to do something another one does without effort.
What follows is where each of the current front-runners is strongest, what Seedance 2.0 does that the others don’t, and how to match a model to the work rather than picking one and forcing everything through it.
The field as it stands
The landscape shifted meaningfully in the first half of 2026. ByteDance released Seedance 2.0 in February, and it moved to the top of the Artificial Analysis text-to-video rankings alongside Alibaba’s HappyHorse-1.0, which arrived in April. Runway’s Gen-4.5, which led those rankings at its late-2025 launch, has since dropped out of the top ten — not because it got worse, but because the field moved around it.
OpenAI’s Sora 2 complicates any comparison written this year. It was deprecated in April 2026 with its API scheduled to shut down in September, which makes it a poor foundation for anything new regardless of how it performs. It still appears in most comparison articles because it remains what people search for.
That leaves a realistic shortlist: Seedance 2.0, Google’s Veo 3.1, Kuaishou’s Kling 3.0, and Runway for a specific category of work.
What each model is built for
Seedance 2.0 is built around input flexibility. It accepts text, images, video clips, and audio in a single generation — up to nine images, three video clips, and three audio files — with each reference addressed from within the prompt and assigned a specific role. One image can establish a character’s face, another a product’s exact appearance, a video clip a camera movement to imitate, an audio file a rhythm to cut against.
Nothing else on this list takes that range of input in one pass. Combined with native audio generated alongside the picture and multi-shot sequences produced within a single generation, it collapses several stages that would otherwise be separate: sourcing sound, matching cuts, and holding a subject consistent across shots.
Veo 3.1 is the resolution and speech leader. Native 4K at up to 60fps, and synchronised audio that extends to intelligible dialogue rather than ambient sound and effects alone. For anything requiring a character to speak on camera, or output destined for a large display, it is the strongest option available and the comparison is not close.
Kling 3.0 is the volume play. Per-second cost is the lowest of the major models, motion quality is consistently good, and multilingual lip sync arrived in early 2026. For teams producing a high number of clips where each individual one does not need to be exceptional, the economics are difficult to argue with.
Runway has kept something that matters more than raw quality for certain work: the best control surface in this space. Motion brushes allow specific regions of an image to be painted with movement direction, a kind of control no text prompt reaches. For VFX work, motion graphics, and anything being assembled in a real post-production pipeline, it remains the professional choice.
What multimodal reference input actually changes
The distinction worth understanding is between describing a result and supplying one.
Text-only prompting asks a model to produce something matching a description. That works well for generic subjects and poorly for specific ones. A prompt describing a matte black speaker with a fabric grille will produce a plausible speaker; it will not produce the client’s speaker.
Reference input inverts this. The specific things that must appear are supplied directly, and the prompt describes only the relationships between them — what moves, in which direction, under what light, shot how. For commercial work this is the difference between output that resembles the brief and output that can actually be delivered.
The same mechanism drives consistency. A character who must remain recognisable across a sequence is supplied as a reference alongside each generation rather than described in words each time, which holds appearance far more reliably. Product logos, packaging, and fine identifying detail behave the same way.
This is where Seedance 2.0’s design is doing something the others are not, and it is worth weighing heavily for anyone whose work involves specific real-world subjects rather than generic ones.
Matching the model to the job
Product and advertising work with a specific item. Reference control dominates everything else here. The product must appear as it is, not as a model imagines it, which puts multi-reference input at the centre of the decision.
Character-driven sequences across multiple shots. Consistency across cuts is the constraint, and reference-based identity holding addresses it more directly than prompt engineering can.
Speech-driven content. Explainers, talking-head segments, anything where dialogue must be understood — Veo 3.1’s audio work is the deciding factor.
High-volume social output. When per-clip cost dominates and individual clips are disposable, Kling 3.0’s pricing structure wins on arithmetic.
Work finished in a post pipeline. If the output is an element to be composited rather than a finished clip, Runway’s regional control earns its place.
Large-format and broadcast delivery. Native 4K at high frame rates narrows the field considerably, and Veo 3.1 leads it.
Reading the benchmarks carefully
Leaderboard position is worth something, and less than it appears. Artificial Analysis rankings reflect aggregate human preference across a prompt set that may look nothing like a given production workload. A model winning on average can lose on the specific category that matters most for a particular job, and averages hide that.
The more useful exercise is running the same prompt suite across candidates on actual work. Ten prompts drawn from real briefs, run at low resolution across three models, costs very little and reveals more than any published comparison including this one. Platforms exposing Seedance 2.0 alongside other models under a single account make that test straightforward to run, which is a practical argument for multi-model access over committing to one provider.
Worth noting on tooling: Seedance2.ai moved to Seevio.ai earlier this year, so anyone returning to a tool they used previously will find it under a different name — the migration notice sets out what carried over.
The multi-model reality
Most teams producing video at any scale end up using more than one model, because the strengths genuinely do not overlap. The cost of that is administrative rather than technical — separate accounts, separate billing, separate credit pools that expire unspent, and separate API integrations to maintain.
That overhead is the real argument for aggregation, and it has nothing to do with which model currently ranks highest. The models will keep leapfrogging each other, and the ordering in this article will be out of date within months. What stays constant is that being able to move between them without re-tooling is worth more than backing whichever one happens to lead this quarter.
