E-Commerce AI Video Maker Benchmark: Veo 3 vs Sora 2
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.
Check out our methodology and evaluation metrics to see how we decided on these ratings.
Examples from AI video makers
We conducted a comparative test across 6 AI video generation platforms, using 12 image-and-prompt inputs.
- Veo 3 is the top-performing model, achieving the highest total and average scores. It delivers consistent and high-quality results across nearly all evaluation dimensions and maintains strong realism, lighting accuracy, and brand detail.
- Wan 2.5 and Kling 2.5 form the second performance tier.
- Wan 2.5 performs reliably across most prompts but shows weaknesses with the chair and boots prompts, indicating challenges with rigid geometry and footwear textures.
- Kling 2.5 performs very well on simple single-object scenes such as “mug”, “plant”, and “lantern”, but shows lower accuracy on complex cosmetic items and irregular shapes such as “boots” and “lipstick and blush”.
- Hailuo 02 Pro demonstrates mid-level performance. It performs well on straightforward catalog-style prompts such as “plant”, “brown bag”, and “4 lipsticks”, but is less consistent on brand fidelity and complex objects like “bags” and “shoes”.
- Sora 2 exhibits variable performance. It achieves strong results on structured prompts such as “mug” and “brown bag”, but performs poorly on others such as “boots” and “4 lipsticks”. The model appears sensitive to scene complexity and lighting variation.
- Pixverse v5 ranks lowest overall. It performs poorly on multiple prompts involving footwear, bags, and cosmetics, suggesting weak handling of proportion and product identity.
- Pixverse failed to generate output for the chair prompt: “The content could not be processed because it contained material flagged by a content checker: ‘content_policy_violation'”.
- The other models successfully processed the chair prompt and generated the video. This indicates a reliability issue and a possible limitation in Pixverse’s prompt filtering or content moderation system.
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 only 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, only 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-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.
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).
Kling AI
Kling AI’s latest update, KLING 2.1, brings two notable improvements to its image-to-video generation tool. The integration of Deepseek allows users to enhance their prompts for more accurate and detailed outputs.
Additionally, the update introduces support for adding sound effects, enabling audio elements to be included alongside visuals.
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.
OpenAI Sora
Sora can be used with the ChatGPT Plus and Pro subscriptions, with an increased video generation limit in the Pro.
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.
Despite its capabilities, PixVerse has limitations in handling complex scenes, achieving artistic precision, and offering high-resolution output in its free plan.
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.
Methodology
Products used
- Veo 3
- Wan 2.5 Preview
- Kling 2.5 Turbo Pro
- Hailuo 02 Pro
- Sora 2
- Pixverse v5
Note: All products are 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
- Basic manipulation: Product movement and rotation in a neutral setting
- 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
- Scene-to-video conversion accuracy
- Maintenance of existing lighting and atmosphere
- 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
- Inter-product physical interactions
- Consistent scale maintenance
- Group movement dynamics
- 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
FAQ
Further reading
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