AI Video Models Explained: How They Work and the Best in 2026

This guide to video models AI studios run today covers what they are, how they turn a prompt into footage, and how the leaders differ. It is written for someone new to the field.

We did not run these models ourselves. Technical claims below come from published papers and vendor pages, which we name, and clip specs and credit rates come from Vidwip’s own model picker.

What is an AI video model?

An AI video model is a neural network that generates a short video clip from a text prompt, a still image, or both. It learns from large sets of videos paired with captions.

Most current models also generate sound. Google says Veo can add sound effects, ambient noise and dialogue, all generated natively.

How text-to-video models work

Most current models follow the same recipe: compress, add noise, learn to remove it, then decode. The pieces come from image research that was extended to video.

Step 1: compress the video into a latent space

Raw video has far too many pixels to model directly. The latent diffusion paper (Rombach et al.) says pixel-space diffusion can take “hundreds of GPU days” to train, and moves the work into the latent space of a pretrained autoencoder instead.

Video models do the same across time as well as space. OpenAI’s Sora report says its compression network outputs a latent “compressed both temporally and spatially”, and a matching decoder maps results back to pixels.

The CogVideoX paper uses a 3D VAE for the same purpose.

Step 2: learn to remove noise

Diffusion training adds random noise to compressed clips and teaches the network to undo it. At generation time, the model starts from pure noise and cleans it up in many steps.

Your prompt steers each step. The latent diffusion paper adds cross-attention layers so the model can take conditioning such as text.

Step 3: use a transformer as the denoiser

Early video models borrowed a U-Net from image generation. The DiT paper swapped in a transformer that works on “latent patches”, and found that more compute consistently gave lower FID, a measure where lower is better.

Its largest model reached a state-of-the-art FID of 2.27 on ImageNet 256x256.

Video versions treat chunks of space and time as tokens. OpenAI’s Sora report describes “spacetime patches” that act as transformer tokens.

The open Wan paper says Wan is built on the “mainstream diffusion transformer paradigm”.

Step 4: decode back into frames

Once the latent is clean, the decoder turns it into frames. Sound models add an audio track generated alongside the picture, not pasted on afterward.

Text-to-video vs image-to-video

Text-to-video starts from words alone, so the model invents the subject, the look and the motion. Image-to-video starts from a still, so only the motion is new.

That is why image-to-video is the safer choice when a face, product or logo must stay the same. The Stable Video Diffusion paper describes a base model that serves as a motion representation for image-to-video.

One model can do both. The Seedance 1.0 paper says a single training paradigm learns text-to-video and image-to-video jointly.

Why clips are short and slow to make

Every extra second adds frames that must stay consistent with each other. Compute grows quickly with length and resolution.

The Seedance 1.0 authors report generating a 5-second 1080p clip in 41.4 seconds on an NVIDIA L20, after multi-stage distillation sped inference up roughly tenfold. CogVideoX’s paper headlines 10-second clips at 16 fps and 768 x 1360.

Today’s best clips on Vidwip’s roster run up to 30 seconds.

Open weights exist if you want to run one yourself. Wan 2.1’s smaller 1.3B model needs only 8.19 GB of VRAM, according to its paper.

Our guide to open-source AI video models covers the field.

Video models AI studios offer: how the leaders differ

The differences you will feel are clip length, resolution, sound and price. The table lists seven families as they appear in Vidwip’s picker, with the credit cost per second of each family’s main tier.

Some families have cheaper tiers. Veo 3.1 Lite is 8 credits a second, Veo 3.1 Fast 19, Kling 3.0 Standard 20 and Grok Imagine 1.5 Lite 5.

A credit is $0.01.

Three things stand out:

• Length: Seedance 2.5 and Wan 3.0 reach 30 seconds, while Veo 3.1 stops at 8.
• Resolution: Hailuo 3 outputs 2K, Veo 3.1 outputs 1080p, and the rest of this table outputs 720p on Vidwip.
• Price: the same second of video costs 5 credits on Grok Imagine 1.5 Lite and 60 on Veo 3.1.

For spec tables see our video AI models comparison. For third-party quality scores see the video model leaderboard.

Single-model write-ups: Veo 3.1, Seedance 2.5, Hailuo 3, Wan 3.0 and Grok Imagine 1.5.

We could not verify the internal architecture of the closed models in the table. Vendors publish little about them, so the recipe above describes the research lineage, not each product.

How to choose a model for your shot

Match the model to the job, not the hype.

• Need dialogue and realism? Start with Veo 3.1 or Kling 3.0.
• Need a long take? Try Seedance 2.5 or Wan 3.0, which go to 30 seconds.
• Drafting ideas cheaply? Use a Lite or Fast tier first, then re-run the keeper on a bigger model.
• Need a subject to match a photo? Use image-to-video.

Since results vary by prompt, the reliable test is to run the same prompt on two or three models and compare.

Try several models on one prompt

Vidwip’s AI video generator runs 16 video models from one account, with text-to-video and image-to-video on every one. The models page lists them all.

AI video needs a paid plan. Starter’s 2,500 credits buy about 500 seconds on Grok Imagine 1.5 Lite at 5 credits a second, or ten 4-second Veo 3.1 shots at 240 credits each.

See pricing for plans, and our guide to free video models for other free options.