E-Commerce AI Video Maker Benchmark: Veo 3 vs Kling

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 compared the top 6 AI video makers using 12 image-and-prompt inputs to evaluate their capabilities in generating product demonstration videos:

AI video maker benchmark results

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

Potential reasons behind the performance differences

Differences in model maturity and training scale

• Veo 3’s higher success rate likely suggests a more mature model, likely trained on larger and more diverse video-image-text datasets.
• Lower-performing tools (e.g., Pixverse v5, Sora 2) appear less capable when handling varied product categories, indicating limited generalization across object types, materials, and scenes.
• Models in the middle tier (Wan 2.5, Kling 2.5, Hailuo 02 Pro) show partial strengths, implying narrower or more uneven training coverage.

Sensitivity to object complexity and geometry

Performance varies strongly by product type:

• Simple, rigid, single-object items (e.g., mugs, plants, lanterns) are handled more reliably across models.
• Complex objects with irregular geometry, reflective materials, or articulated structures (e.g., boots, bags, cosmetics) can cause distortions and failures.

This suggests differences in how models learn and preserve 3D structure, proportions, and surface properties during video generation.

Prompt-following and semantic alignment limitations

All tools show degradation as prompts become more detailed or involve multiple actions, objects, or stylistic constraints.

• Higher success rates correlate with models that better translate textual intent into visual motion and scene changes.

For example, Pixverse’s failure to generate output for a neutral “chair” prompt highlights shortcomings in prompt interpretation or moderation filtering, affecting reliability rather than visual quality alone.

Product integrity and brand fidelity challenges

Lower-scoring models frequently alter:

• Product proportions and scale
• Textures, materials, and colors
• Brand-defining visual details

Veo 3’s advantage appears tied to better temporal consistency, maintaining product identity across frames, which directly impacts scores in product integrity and physical accuracy.

These differences likely reflect how strongly models are optimized for generic visual realism versus product-centric accuracy, which is critical in e-commerce contexts.

Scene consistency and physical realism

Models differ in their ability to maintain:

• Coherent lighting and shadows
• Plausible object–environment interactions
• Stable camera motion

Tools with lower scores often violate real-world physics (e.g., unnatural hand motion, floating objects, inconsistent reflections), indicating weaker internal representations of physical constraints.

Evaluation design effects

The benchmark emphasizes prompt compliance, physical accuracy, and product integrity, which favors models that prioritize structured realism over artistic variation.

The limited number of prompts (12) and reliance on stock images may amplify the impact of:

• Prompt sensitivity
• Single failure cases
• Category-specific weaknesses

As a result, differences between models become more pronounced, especially for complex, multi-object scenarios.

Examples from AI video makers

The following examples showcase each prompt alongside its corresponding output video:

1. The red high-heel shoes and black handbag in the photo, shown in close-up as the camera slowly pans from left to right, light reflections gliding across the glossy heels while the handbag chain gives a subtle metallic glimmer, ending with a soft focus on the full arrangement.

2. The small green plant in the white vase in the photo, placed against a clean white background, as a hand gently enters from the right side, lifts the vase smoothly, and carries it out of frame.

3. The backpack in the photo, resting on a stone surface with trees in the background, as the camera slowly zooms in while a hand reaches from the side, picks up the backpack by its top handle, and carries it out of frame.

4. The four lipsticks in the photo standing upright with shiny silver and black casings, set in a surreal underwater scene where bubbles drift upward and shimmering light rays filter through the water, as the camera slowly circles around to highlight each shade.

5. The perfume bottle in the photo standing on a dark surface, as a hand enters smoothly, picks it up, and presses the spray to release a fine mist that catches the light in slow motion against the background.

6. The white enamel coffee mug in the photo on a wooden table, as a hand enters from above and tilts a kettle to pour a smooth stream of hot coffee into the mug; steam curls upward and gentle ripples form on the surface while the camera holds a close-up.

7. The leather shoulder bag in the photo displayed on a plain background, as it begins to rotate smoothly in a full 360-degree spin, showing all angles and details of the straps, buckles, and stitching while the camera stays centered.

8. The pink vase with colorful flowers in the photo, set against a black background, begins to slowly rotate as petals and leaves gently detach in slow motion and float upward like they are defying gravity, illuminated by soft glowing light beams, while the vase itself stays solid and glowing at the base.

9. The dark brown high-heeled boots in the photo, shown being worn as the lower legs and feet are visible, walking gracefully across a smooth white surface; the camera follows the steps in close-up, capturing the shine of the leather and the confident rhythm of the walk.

10. The simple wooden chair in the photo, now placed inside a bright modern kitchen in front of a dining table, as the camera smoothly changes angles from side to side and slightly above, highlighting the chair in its new setting with natural daylight streaming in.

11. The lipstick and blush in the photo transform into a magical beauty showcase, as the lipstick slowly twists upward by itself and leaves a glowing trail of pink light in the air, while the blush compact opens and releases a soft cloud of shimmering pink powder that gently swirls around both products before settling back down.

12. The lantern in the photo sits in a dark outdoor setting as the candle inside is lit: the wick catches, the flame blooms gently, and a warm golden glow spreads through the glass with soft flicker and star-shaped highlights, while the camera makes a slow push-in to emphasize the light against the blurred night background.

What are the issues with AI video generators?

AI video generation models show progress in visual synthesis, but current tools are not ready to produce product videos that meet e-commerce standards. The comparative evaluation of six models reveals several recurring technical and functional limitations.

1. Inaccurate representation of product features

Most AI video generators fail to depict key product attributes such as size, color, material, and surface texture.

• Models often distort rigid geometries (e.g., chairs, boots) or misrepresent reflective and textured materials like leather or metal.
• Brand-specific features such as logos or packaging details are inconsistently reproduced.
• The resulting videos may look visually plausible, but are not reliable representations of the actual product.

In e-commerce, these inaccuracies risk misleading potential buyers and eroding trust in the content.

2. Limited understanding of context and brand Identity

The systems lack contextual awareness of how a product should appear within a marketing or catalog scenario.

• Even when the prompt clearly indicates commercial intent, outputs tend to resemble generic animations or artistic renderings rather than product demonstrations.
• Variations in lighting, perspective, and background composition reduce the professional consistency required for promotional use.

This indicates that most models are not yet fine-tuned for the specific visual and semantic demands of branded content generation.

3. Misalignment between prompts and outputs

A common issue across all tested tools is partial failure to follow prompt instructions.

• Models perform acceptably on simple single-object prompts (“mug,” “plant”) but show errors or omissions in complex multi-object or descriptive prompts (“lipstick and blush,” “4 lipsticks”).
• Some tools, such as Pixverse, fail to generate outputs for neutral prompts due to restrictive or unreliable content filtering systems.

These results demonstrate that some of the current AI video generators interpret text inputs superficially and cannot reliably translate descriptive intent into visual form.

4. Inconsistent performance and reliability

Performance varies significantly between prompts and models.

• Even the best-performing system, Veo 3, maintains consistency within a subset of prompt types.
• Others, such as Sora 2 and Hailuo 02 Pro, fluctuate in quality across scenes with different lighting or object complexity.
• Failures caused by moderation filters or generation errors further reduce dependability for production workflows.

Inconsistent reliability makes these tools unsuitable for commercial use where output reproducibility is essential.

Recommendations to improve AI video quality

To improve AI-generated videos for e-commerce, technical adaptation is necessary rather than simple prompt iteration.

• Enhance prompt quality: Include structured descriptions of product attributes, materials, lighting, and intended usage context.
• Fine-tune on domain data: Use product catalogs and brand visuals to train or condition the models on specific brand standards.
• Integrate retrieval-based systems: Employ contextual or agentic retrieval-augmented generation (RAG) to supply relevant product and brand information during generation.

These measures can help bridge the gap between generic video synthesis and accurate, context-aware product representation.

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.

To calculate pricing for PixVerse: Price ≈ (duration ÷ 5 s) × (credits for 5 s quality) × $0.01. For example, 10-second 720p video: (10 ÷ 5) × 60 × $0.01 = $1.20.

Veo

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

• Veo 3 performs strongest across the full evaluation criteria. It consistently maintains accurate product structure, believable environments, and stable camera execution. Its physical accuracy is especially strong in real-world physics and object interactions, making the generated products feel grounded in the scene. From a product integrity perspective, Veo 3 also performs well in lighting, shadows, material rendering, proportions, and brand-specific details. It is the most balanced model in terms of both physical realism and product fidelity.

Wan AI

The Wan2.6 series introduces new capabilities that expand users’ ability to generate and personalize AI content, particularly video narratives:

• Wan 2.5 Preview shows strong results across structured product scenarios. It performs well in product appearance, proportions, texture, color, and material rendering, especially when the scene has a clear object focus and straightforward composition. Its camera and environment adherence are reliable. However, it is less consistent in scenarios involving complex object interactions, difficult geometry, or multiple product elements within the same scene.
• Wan 2.6 performs reliably on structured product scenes and is strong in product appearance, scale, and material rendering when the scene is straightforward. However, it shows clearer weaknesses in more visually complex or irregular scenes. These issues appear mainly in physical accuracy, including real-world physics, object interactions, and camera/environment interpretation, as well as product integrity areas such as lighting accuracy and detailed product identity.

Kling AI

Kling VIDEO 3.0, the latest updates from Kling AI, introduces longer native video generation, stronger narrative control, and audio-visual integration:

• Kling v3 is also a strong performer, particularly in product integrity. It preserves product appearance, proportions, scale, texture, color, and material quality across many outputs. Its performance is strongest in simpler or more structured product scenes, where product shape, surface detail, and brand identity are easier to maintain. However, it becomes less consistent when scenes require more complex object geometry, irregular forms, or nuanced product interactions.
• Kling 2.5 Turbo Pro performs well in simpler, more structured product scenes. It shows strong physical accuracy and product integrity when the product form is clear, and the environment is controlled. Its texture, color, and material rendering are often reliable, and it preserves product proportions well. However, it struggles more with complex cosmetics, footwear-like forms, and scenes that require precise object interactions or fine-grained product details.

Hailuo AI

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

• Hailuo 2.3 delivers solid mid-level performance. It follows camera and environment requirements reasonably well and can produce convincing results when the product structure is clear. However, its product integrity is less consistent for detailed branded objects or visually complex product arrangements. The main weaknesses appear in product appearance, brand-specific detail, proportions, and material accuracy.
• Hailuo-02 shows uneven performance across the criteria. It performs better when the product shape and environment are simpler and easier to preserve. It can produce acceptable lighting and material rendering in controlled scenes. However, it struggles with physical accuracy and product integrity in more complex outputs. The main weaknesses are product proportions, product appearance, object interactions, and brand-specific detail consistency.

OpenAI Sora

Sora 2 is OpenAI’s multimodal AI model designed for high-performance visual understanding and reasoning tasks. Key capabilities include:

• Sora 2 has variable performance. It can perform strongly on simpler, structured scenes where the product shape and camera requirements are easier to maintain. However, its quality drops noticeably when the scene becomes more complex or requires precise object geometry, realistic physics, or multiple interacting product elements. Product integrity also varies, with inconsistencies in proportions, lighting, and fine details of the product’s appearance.

As of March 2026, OpenAI decided to shut down Sora, despite the tool’s popularity and major backing, including a planned $1B partnership with Disney to use its characters.¹

Other reasons included:

• High compute costs: Video generation consumed large amounts of scarce AI chips.
• Lack of profitability: The product reportedly lost about $1 million per day.
• Weak user retention: Initial interest faded quickly, and usage declined significantly.

PixVerse

PixVerse AI is an AI video generation platform that creates short videos from text prompts or static images, suitable for social media content creation. It includes features such as automatic audio generation, lip-syncing, and cinematic camera movements.

• Pixverse v5 performs the weakest overall. It struggles across several dimensions of physical accuracy, including product structure, camera adherence, real-world physics, and object interactions. It also shows weaker product integrity, especially in product proportions, brand-specific details, texture, color, and material rendering. The issues are most visible in scenes involving complex product identity, detailed materials, or difficult object forms.
• Pixverse v5 also failed to process one prompt due to a content checker flag, while the other tools successfully generated the video. This suggests an additional reliability limitation related to prompt filtering or content moderation.

AI video maker benchmark methodology

Products used

• Kling v3
• Wan 2.6
• Hailuo 2.3

Note: We tested these products in June 2026.

• Veo 3
• Wan 2.5 Preview
• Kling 2.5 Turbo Pro
• Hailuo 02 Pro
• Sora 2
• Pixverse v5

Note: The above products were tested in October 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 individual product rendering and environment creation, as well as 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.²

FAQs

Further reading

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

• Generative AI Applications with Real-Life Examples

Cite this benchmark

Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.

@misc{ermut2026,
 author = {Ermut, Sıla and Alper, Şevval},
 title = {{E-Commerce AI Video Maker Benchmark: Veo 3 vs Kling}},
 year = {2026},
 month = jun,
 howpublished = {\url{https://aimultiple.com/ai-video-maker}},
 note = {AIMultiple. Retrieved June 24, 2026}
}