The 2026 AI video models, in brief: Veo, Kling, Seedance, Runway
In 2026, four model families dominate the conversation about AI-generated video in a business context: Veo, from Google DeepMind, Kling, from Kuaishou, Seedance, from ByteDance, and Gen-4, from Runway. There's no single "best" among the four — each was built with a different emphasis, and a serious production team combines them as needed, rather than picking just one for everything it publishes.
- 01Veo (Google DeepMind) Generates video from text or from an image, with native audio produced directly in the clip — sound effects, ambient noise, sometimes dialogue — plus separate control over style, character, camera and motion. Outputs in 1080p or 4K.
- 02Kling (Kuaishou) Native 4K resolution, control over long storyboards with multiple linked scenes, and a feature that ties a character's visual identity to their vocal tone, so the two stay in sync from one scene to the next.
- 03Seedance (ByteDance) Generates clips of up to 15 seconds, with multiple frames and two-channel stereo audio, starting simultaneously from up to nine images, three clips and three reference audio files — useful precisely for keeping a product or character recognisable.
- 04Runway (Gen-4) Its strength is consistency from a single reference image: a character, object or product can appear in different angles and lighting without retraining, which makes it well suited to product catalogues or ad sets built around the same visual "hero".
What companies actually use AI-generated video for
Beyond the spectacular demos, real use inside companies has settled around four types of content, with one thing in common: none of them depends on a photo shoot or a film set for every new variant.
Product visualisation puts the same product in different scenes, backgrounds or uses without a new photo shoot. UGC-style ads mimic the "person talking to camera" format, either fully generated or starting from a single real recording later expanded into variants. Explainer clips take a service or a workflow from text to moving image, where the clarity of the steps matters more than the direction. Virtual tours chain together frames generated from a set of static photos of a space or product, a technique covered in detail in the consistency section below.
None of these uses replaces real filming where trust or emotion are essential — genuine customer testimonials, for example. What it covers is the volume of variants that repeated filming would make disproportionately expensive: ten product angles, five ad variants for testing, a tour of a space that no longer exists physically.
Where you can still tell, in 2026, that the material is generated
Quality has improved far faster than three specific problems have closed, and those remain the main reason a clip still "smells" of AI even at 4K resolution.
On-screen text — a sign, a label, text on a display — distorts or becomes illegible far more often than the rest of the image; the practical fix is to add the text in editing, rather than relying on the model to generate it correctly. Hands remain a known weak point, especially in fast motion or object interaction — extra fingers, unnatural grip. And the consistency of a product or character across different clips, not just within the same clip, can drift subtly — a colour, a proportion, the position of a logo — unless it's deliberately anchored with the same reference image every time.
A serious pre-publication checklist tests exactly these three points, not general image quality, which already looks good on most 2026 models anyway.
How to keep a product or character consistent from one clip to the next
Two techniques make the real difference, and neither is technically complicated — but both demand discipline in preparing the reference material.
The first is the reference image: you upload a fixed photo of the product or character, which the model treats as "ground truth" for every new generation. Runway is built exactly on this — a character, object or product starting from a single reference image, no retraining, reproduced in different angles and lighting conditions. Seedance takes the idea further, accepting multiple reference images, clips and audio files at once, "reading" composition, motion and voice from them and reproducing these in the new clip.
The second is keyframe chaining: the last frame of one clip becomes the starting image of the next, a technique used often for continuous virtual tours, where the motion needs to read as a single path, not clips stuck together.
In both cases, the quality of the reference decides the quality of the result: a simple background, even lighting and a clear angle hold consistency together; an unclear reference propagates as an error through the whole batch of clips generated from it.
What the law requires, from 2 August 2026, for labelling AI-generated content
From 2 August 2026, Article 50 of the AI Act (Regulation (EU) 2024/1689) becomes applicable, and for any company publishing AI-generated or AI-modified video in the European Union, the legal side is no longer a footnote.
The obligation has two layers. Model providers — Google, Kuaishou, ByteDance, Runway — must technically mark, in a machine-detectable format, the synthetic content they generate. Separately, the company publishing the material must clearly disclose, on the public's first contact with it, any deepfake-type content — material bearing a strong resemblance to a real person, place or event, realistic enough that it could mislead. Artistic or satirical content carries a lighter obligation, only "in an appropriate manner that does not hinder viewing".
In practice, if you publish a UGC-style ad in which an AI-generated "customer" speaks to camera and could pass for a real person, that clip needs a visible or audible disclosure that it's AI-generated — not just hidden metadata in the file. Content published before the law takes effect doesn't need to be labelled retroactively, and systems already on the market get an additional deadline, until 2 December 2026, for the technical marking mechanism.
What a realistic AI video production workflow looks like
The generation itself takes minutes. What decides whether the result is publishable is the workflow around it, and that looks predictable regardless of the model: a clear brief, then cleaning up the reference material — product photos, a brand guide, possibly a voice sample — batch generation rather than isolated frame by frame, a human review pass dedicated specifically to the three weak points above, fixing text and brand elements in editing, and only at the end applying the labelling the law requires.
The real bottleneck is no longer render time, it's review discipline — a team that treats the first generated output as a rough draft, not as a deliverable ready to send the client, consistently gets better results than one that publishes straight from the first generation. A production team that takes these tools seriously, like the one at The Niche Society, builds this review step into the workflow as a mandatory stage, not an optional extra.
How cost is calculated: credits and seconds, not "price per video"
None of these platforms sells "a video" as a unit. They sell credits, converted into seconds of output at a given resolution and model, so cost rises with duration, with resolution, and with the number of retries when a generation doesn't come out right.
Runway publishes this logic openly: the base plan starts at $12 a month, billed annually, for 625 credits a month, and the Gen-4.5 model consumes 60 credits for every 5 seconds of video — meaning that subscription covers roughly 52 seconds of video a month, not unlimited production. Higher plans, from $28 and $76 a month billed annually, scale the credit budget up proportionally, but the underlying logic stays the same.
The rest of the major platforms use a similar logic, on credits or directly per second of output. The useful question for a company isn't "how much does AI video cost", but "how many finished seconds do we need this month, at what resolution, with what margin for retries" — because as long as hands, text or consistency can still come out wrong, a realistic budget includes a buffer for reshoots from the start, not just the "ideal" cost of a single generation.
Sources and further reading.
Frequently asked questions
What exactly is AI-generated video?
It's video content produced by a model trained on large volumes of images and clips, starting from text, an image or another video clip — not filmed with a camera. Models such as Veo, Kling, Seedance or Runway Gen-4 generate the frames, the motion and, increasingly, the sound, directly from a prompt and from reference material uploaded by the user.
Do I have to label AI-made ads under EU law?
Yes, if you publish in the European Union. From 2 August 2026, Article 50 of the AI Act (Regulation 2024/1689) requires model providers to technically mark synthetic content, and a company publishing deepfake-type material — content that could be mistaken for real — must clearly flag it to the public, on first contact with it.
Roughly, what does an AI-generated video clip cost?
You don't pay "per video", but in credits converted into seconds of output. At Runway, for example, a basic subscription from $12 a month, billed annually, gives you 625 credits — enough for roughly 52 seconds of video on the Gen-4.5 model — and the rest of the major platforms use a similar logic, with price rising alongside resolution and native audio.
Can an AI model keep a product or character exactly consistent from one clip to the next?
Partly, and only if it's "anchored" with clean reference images — a simple background, even lighting, a clear angle. Models like Runway Gen-4 or Seedance are built specifically for this, but an unclear reference propagates as an error through the whole batch, so manual review remains mandatory before publishing.
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