E-Commerce AI Video Maker Benchmark in 2025

Product visualization plays a crucial role in e-commerce success, yet creating high-quality product videos remains a significant challenge. Recent advancements in AI video generation technology offer promising solutions.

We evaluated leading AI video makers’ capabilities in generating product demonstration videos:

AI video maker benchmark results

Figure 1: Success of the tools in creating videos following the prompts and input images.

Examples from AI video makers

Kling AI KLING 1.6

Figure 2: An example image of a lantern in front of faded lights.

Prompt: Make the lantern’s flame flicker naturally. Add a slight glow effect that shifts with the breeze, keeping the nighttime atmosphere intact.

Output of KLING 1.6:

This video is rated 10/10 for fully meeting all criteria, including prompt accuracy, lighting and shadow, real-world physics, product integrity, and brand-specific details.

OpenAI Sora

Figure 3: An example image of an orange bag with brown straps.

Prompt: Pure white background, soft studio lighting. Smooth 360-degree rotation, starting and ending with front view. Keep bag centered and maintain consistent rotation speed.

Output of OpenAI Sora:

This video is rated as 6/10 due to these issues:

• Prompt compliance: It failed to demonstrate consistency between prompt requirements and the generated output regarding product appearance, environment rendering, and camera movements. (-3 points)
• Preservation of product / brand-specific features: The side clips and the rings on the front are distorted as the point of view is rotated. (-1 point)

Check out our methodology and evaluation metrics to see how we decided on these ratings.

Wan AI

Figure 4: An example image of a perfume bottle.

Prompt: Show a slow rotation of the perfume bottle against a white background. Add a soft mist spray effect while keeping the bottle’s reflections and transparency intact.

Output of Wan AI:

This video is rated 7/10 due to these issues:

• Prompt compliance: The tool failed to generate the video as specified in the prompt. For instance, while the prompt requested a mist spray effect, the resulting video does not depict it. (-3 points)

Figure 5: An example image of a coffee mug.

Prompt: Add soft steam rising from the coffee mug. Keep the motion subtle and natural, with a slight lighting shift for warmth.

Output of Wan AI:

This video is rated 10/10 for fully meeting all criteria, including prompt accuracy, realism, physics, lighting, product integrity, and brand-specific details.

Methodology

Products used

• Kling AI KLING 1.6 (March/2025)
• Wan AI 2.1 (March/2025)
• Kling AI KLING 1.5 (December/2024)
• Hailuo AI I2V-01-live (December/2024)
• Hailuo AI I2V-01-director (March/2025)
• Runway Gen3 Alpha Turbo (December/2024)
• OpenAI Sora (December/2024)
• Veo2.ai (March/2025)

Test Image Classification and Objectives

Our study utilized three distinct categories of product images, each designed to test the specific capabilities of AI video generation tools:

White Background Products

Purpose: Evaluate dual capabilities

1. Basic manipulation: Product movement and rotation in a neutral setting

2. Environmental adaptation: Integration of products into new contexts

Test focus: AI’s ability to maintain product integrity while adding or changing environments.

Contextual Product Images

Purpose: Assess environmental animation capabilities

1. Scene-to-video conversion accuracy

2. Maintenance of existing lighting and atmosphere

3. Adding dynamic elements to an established setting

Test focus: AI’s ability to bring static environmental product shots to life.

Multi-Product Scenes

Purpose: Test complex product relationships and interactions

1. Inter-product physical interactions

2. Consistent scale maintenance

3. Group movement dynamics

4. Collective lighting effects

Test focus: AI’s ability to handle multiple products while maintaining individual integrity and natural interactions.

This three-category approach enables us to evaluate not only individual product rendering and environment creation but also the AI’s capability to manage complex multi-product scenarios, providing a more complete assessment of real-world e-commerce applications.

Our evaluation metrics are:

Prompt Compliance: (3 points)

• Consistency between prompt requirements and generated output for the product

• Consistency between prompt requirements and generated output for the environment

• Consistency between prompt requirements and generated output for the camera and shooting.

Physical Accuracy: (3 points)

• Adherence to real-world physics

• Accuracy of object interactions (surface contact, movement)

• Lighting and shadow behavior

Product Integrity: (4 points)

• Consistency in product appearance throughout the video generation
• Preservation of product / brand-specific features and details
• Maintenance of product proportions and scale
• Texture, color, and material rendering accuracy

Each generated video is rated out of 10 based on these metrics.

Dataset: We used stock images from pexels.¹

What are the issues with AI video generators?

We tried these video generation tools to promote a product on e-commerce sites using only its photograph and a prompt, but the outputs showed us that this was not possible.

In most cases, these AI tools could not:

• Communicate accurately to the buyer the product’s features, brand-specific details, size, color, texture, etc.
• Generate a video that is 100% compatible with the prompt.

Tips: To address these issues, we recommend enhancing prompts and contextualizing AI video generators through LLM fine-tuning, contextual RAG, or Agentic RAG.

AI video generation tools

*Tools provide a credit system, and the credits spent depend on many factors, like the resolution, the duration of the video, and the model used in creation.

Kling AI

Kling AI’s KOLORS 1.5 model in image generation introduced the “AI Model” feature, enhancing image quality and portrait aesthetics, which can benefit advertisers and e-commerce users.

Wan AI

Wan AI’s flagship model, Wan 2.1, enables text-to-video, image-to-video, and video editing with cinematic effects.

It supports multilingual text generation (Chinese & English) and runs on consumer GPUs (8.19GB VRAM for 5s 480p videos).

Hailuo AI

Hailuo AI is designed for artists and creators to transform static images into animated videos.

Its key features include Image to Video (I2V), which animates 2D images with smooth motion; Text to Video (T2V), which converts text descriptions into video content; and Live Animation (I2V-01-Live), which creates fluid, lifelike animations from illustrations.

Runway ML

Runway ML allows users to train custom models to help reflect corporate identity.

OpenAI Sora

Sora can be used with the ChatGPT Plus and Pro subscriptions, with an increased video generation limit in the Pro.

Veo2.ai

Veo2.ai offers tools for automated video analysis, visual search, object detection, and scene understanding.

CapCut Commerce Pro

CapCut Commerce Pro takes product images, text descriptions, and brand assets as input and uses AI to generate promotional videos.

The tool applies templates, motion effects, auto-captioning, and voiceovers to create engaging content optimized for platforms like TikTok, Instagram, and e-commerce stores.

Note: We did not include CapCut Commerce Pro in our benchmark study because, unlike other AI video generators we tested, it does not create videos from an image and a prompt.

Instead, CapCut relies on structured templates and automated editing features, making its workflow fundamentally different from the generative AI approach used by other tools.

FAQ

Further reading

Discover more on generative AI capabilities, use cases, and tools by checking out:

• Top 100+ Generative AI Applications with Real-Life Examples
• Top 35+ Generative AI Tools by Popularity & Category

External sources

• 1. Free Stock Photos, Royalty Free Stock Images & Copyright Free Pictures · Pexels.