How We Test AI Video Tools
AI video models can produce dramatically different results from the same prompt. For that reason, AW Blog does not evaluate tools solely from feature lists or company marketing material.
When we publish a hands-on comparison, we aim to test competing tools with equivalent prompts and evaluate the resulting videos across practical production criteria such as prompt adherence, motion quality, visual consistency, camera control, generation speed, workflow flexibility and cost.
Where a conclusion is based on documentation rather than direct testing, we identify it accordingly.
AI video products change quickly, so comparisons include a publication or update date and may be revised as models and pricing change.
Evaluation Criteria
When testing AI video generators, we evaluate across these practical dimensions:
Prompt Adherence
Does the output match the written prompt? Are the subject, environment, action, framing and style correct?
Motion Quality
Is movement natural and believable? Do objects and characters move with realistic weight, speed and physics?
Character Consistency
Does a character's face, body, clothing and accessories remain stable across frames and between separate generations?
Camera Control
Does the model follow camera instructions accurately? Are tracking shots, push-ins, orbits and static shots executed correctly?
Visual Quality
Are textures, lighting, skin, materials and environments rendered at a high standard? Are there visible artifacts, distortions or hallucinations?
Generation Speed
How long does the model take to produce a usable clip? Is the speed consistent across different prompt types?
Workflow Flexibility
Does the platform support image-to-video, video-to-video, extension, upscaling, references and editing tools?
Cost
What is the practical cost per usable minute of video, including failed generations and revisions?
Testing Process
- Select equivalent prompts — Use the same or comparable text/image inputs across all tools being compared.
- Generate multiple attempts — Run each prompt multiple times to account for variation in model outputs.
- Evaluate against criteria — Score or describe each result using the criteria above.
- Document actual results — Include screenshots, descriptions and specific observations rather than generic impressions.
- Calculate real costs — Factor in failed generations, revisions and platform-specific credit systems.
- State limitations — Note when testing was limited by access, credits, time or platform restrictions.
Transparency
No testing methodology is perfect. AI video models are non-deterministic — the same prompt can produce different results each time.
AW Blog aims to be honest about what was tested, how it was tested and what the limitations of each comparison are. Where testing was conducted using free-tier access, limited credits or specific model versions, that context is provided.