A fair image-to-video benchmark should answer a production question: which model gives you the highest rate of usable clips for the work you actually make?
It should not begin with a favorite model, select one lucky output, and then invent a score around it. A credible benchmark fixes the inputs, repeats generations, defines failure before testing, and publishes enough evidence for another creator to reproduce the comparison.
Use this protocol to compare Kling 3.0, Wan 3.0, Seedance 2.5, or any future image-to-video models available in the PhotoArtify image-to-video generator.
Define the benchmark question
“Which model is best?” is too broad. Replace it with a decision such as:
- Which model preserves portrait identity during subtle motion?
- Which model keeps product geometry stable during a camera move?
- Which model handles physically believable action with the fewest retries?
- Which model produces the lowest cost per usable second for social video?
- Which model maintains a coherent environment during a longer clip?
Write the question before choosing images or prompts. This prevents the test set from being changed to favor an early result.
Build a four-image test set
A compact benchmark can expose most production failures with four source categories.
Test A: close portrait
Use a clear face with visible eyes, mouth, hairline, clothing, and accessories. Avoid a source that is already distorted. The test should measure identity preservation, expression, blinking, skin detail, and mouth stability.
Prompt:
The subject breathes naturally, blinks once, and turns slightly toward the side light. Very slow stabilized push-in. Preserve exact facial identity, age, hairstyle, clothing, skin texture, and background.
Test B: structured product
Use furniture, electronics, packaging, a vehicle, or another object with straight edges and recognizable proportions. The test should reveal stretching, duplicated parts, label drift, and reflection errors.
Prompt:
The camera makes a restrained 15-degree clockwise orbit around the product while a soft studio light moves across its surface. Preserve exact geometry, proportions, seams, materials, color, and background perspective.
Test C: environment with depth
Use a scene with foreground, middle ground, and distance. The test should evaluate parallax, horizon stability, atmospheric motion, architecture, and whether the model invents or removes major objects.
Prompt:
Wind moves through the field in layered waves while clouds drift slowly and the subject takes one measured step. Gentle forward camera glide. Preserve the landscape, horizon, body proportions, clothing, and natural light direction.
Test D: complex cinematic scene
Use a scene with several interacting motion systems such as water, fabric, smoke, vehicles, creatures, or weather. This is the stress test, not the only test.
Prompt:
The ship moves steadily through the glowing water while the sails respond to the wind and the wake spreads naturally behind the hull. Slow side-tracking camera, cinematic night lighting. Preserve ship design, sail structure, horizon, scale, and color palette.
Lock the test conditions
Record the following before generating:
| Variable | Benchmark rule |
|---|---|
| Source | Same original file for every model |
| Prompt | Same wording for the baseline |
| Aspect ratio | Same ratio wherever supported |
| Duration | Use the longest common duration for the baseline |
| Resolution | Match output resolution where possible |
| Audio | Either disabled for all or enabled for all |
| Attempts | Minimum three attempts per model and test |
| Selection | Score every attempt, not only the best clip |
| Review | Hide model names during quality scoring when practical |
If one model lacks an overlapping control, record the difference instead of silently changing the setup. You can run a second “maximum capability” round after the controlled baseline.
Use a weighted scorecard
Score every category from 1 to 5, where 1 is unusable and 5 is production-ready with no meaningful repair.
| Criterion | Weight | What to inspect |
|---|---|---|
| Subject identity | 20% | Face, clothing, signature details, and continuity |
| Object geometry | 15% | Shape, edges, labels, limbs, and proportions |
| Motion quality | 20% | Weight, acceleration, contact, and environmental reaction |
| Prompt adherence | 15% | Requested action, timing, exclusions, and priorities |
| Camera control | 10% | Direction, speed, smoothness, and framing |
| Background stability | 10% | Architecture, horizon, duplicate objects, and flicker |
| Final-frame usability | 5% | Whether the ending can be edited or extended |
| Audio quality | 5% | Synchronization, ambience, dialogue, and artifacts |
For silent tests, move the audio weight into the criteria most important to your project. Do not change weights after seeing the results.
Calculate the weighted quality score:
sum of each category score multiplied by its weight
Quality score alone is not enough. A model with one excellent result and five failures may be less useful than a model with consistently good results.
Measure usable-result rate
Define “usable” before reviewing. For example:
- no identity change visible at normal playback speed;
- no broken hands or major object deformation;
- requested action and camera direction are present;
- no background collapse or severe flicker;
- clip contains at least four usable seconds;
- no repair beyond normal editing and color correction.
Then calculate:
usable attempts / total attempts
Run at least three attempts per source and model. Five is better when generation cost allows. Report both the median score and usable-result rate so a lucky outlier cannot dominate the conclusion.
Calculate cost per usable second
Displayed generation price does not include failed attempts. Use:
total test cost / total usable seconds
Also record:
- average generation time;
- queue failures and timeouts;
- number of prompt revisions;
- manual repair time;
- external audio or upscaling cost;
- download and export limitations.
This converts a model comparison into an operating-cost comparison. It is especially important for agencies, e-commerce teams, and creators producing many clips per week.
Separate baseline and optimized rounds
The baseline round uses identical prompts and overlapping controls. It measures general behavior.
The optimized round allows model-specific features, prompt phrasing, references, duration, audio, first/last frames, or extensions. It measures the best workflow each model can offer.
Publish both rounds separately. Otherwise a model may appear weaker simply because its most useful controls were disabled, or stronger because it received more prompt tuning than its competitors.
Blind the quality review
Rename exported clips with neutral IDs and ask at least two reviewers to score them without seeing the model names. Randomize playback order. Review once at normal speed and once frame by frame.
After scoring, compare reviewer agreement. If two reviewers differ by more than two points in a category, discuss the exact frame or failure and document the resolution.
Blind review cannot remove every preference, but it reduces brand familiarity and expectation bias.
Publish evidence, not only rankings
A link-worthy benchmark should include:
- snapshot date and model versions;
- source images or clear descriptions of them;
- exact prompts;
- settings and number of attempts;
- all scored outputs or a representative failure gallery;
- scoring rubric and category weights;
- generation cost and timing;
- limitations and conflicts of interest;
- downloadable result table in CSV or spreadsheet format.
Do not publish a winner if the test does not support one. It is valid to conclude that one model is strongest for portraits, another for structured products, and another for longer storytelling.
Result table template
| Model | Test | Attempts | Median quality | Usable rate | Usable seconds | Total cost | Cost per usable second |
|---|---|---|---|---|---|---|---|
| Model A | Portrait | 5 | - | - | - | - | - |
| Model A | Product | 5 | - | - | - | - | - |
| Model B | Portrait | 5 | - | - | - | - | - |
| Model B | Product | 5 | - | - | - | - | - |
Leave cells blank until the generations have been reviewed. Never convert vendor claims or a single demo into a measured benchmark score.
A practical first benchmark
For an initial PhotoArtify comparison:
- Use the four source categories above.
- Choose one prompt for each category.
- Test Kling 3.0, Wan 3.0, and Seedance 2.5 at an overlapping duration.
- Generate three attempts per combination: 36 clips total.
- Have two reviewers score all clips blindly.
- Publish median scores, usable-result rate, cost per usable second, and representative failures.
Read Kling 3.0 vs Wan 3.0 vs Seedance 2.5 before selecting the capability round. Use the 25 image-to-video prompts to expand the test set after the first benchmark.
A benchmark earns trust when readers can disagree with the conclusion but still reproduce the method. Keep the inputs fixed, show failures as well as successes, and update the snapshot whenever model versions or product controls change.
