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To benchmark AI video models for a team, test each candidate on the same representative prompts and inputs, score outputs against a rubric set in advance, and measure the full cost and time to produce an accepted result. Keep the test reproducible, and treat provider-published rankings as evidence about their stated test—not a guarantee of performance on your work.
Decide what the benchmark must answer
Start with the work your team expects to ship. A model that looks impressive on a showcase prompt may not handle your subjects, motion, reference assets, output requirements, or production constraints. Define the decision and the pass/fail conditions before generating clips; Runway’s comparison framework likewise recommends workload-specific comparisons and hard constraints.
Write down only the requirements that genuinely apply to your workflow. Possible gates include:
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- Output format and minimum usable duration.
- Commercial permission for the intended use.
- A maximum cost per accepted output and a turnaround target.
- Privacy, retention, and safety controls that meet your organization’s requirements.
Confirm each candidate’s current capabilities and terms directly. Features, policies, pricing, and availability can change, and the requirements may differ by deployment or region.
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Build a test set that represents production
Use identical prompts and input assets for every candidate wherever the interfaces permit. Include routine jobs as well as difficult cases: the subjects, compositions, camera movement, actions, and transitions that matter to your team. For image-to-video work, give each model the same reference images and instructions. Record unavoidable differences between interfaces rather than silently treating the test as identical.
There is no universal minimum test-set size. Published examples illustrate different scopes, not prescriptions:
| Test | Reported scope | How to interpret it |
|---|---|---|
| T2VEval paper | 148 prompts, 1,783 generated videos, and 13 models; authors’ 2025 paper. | The paper evaluates subjective dimensions including overall impression, text-video consistency, realness, and technical quality. See the T2VEval paper. |
| VBench image-to-video comparison | 355 image-and-text pairs; Google DeepMind’s page does not state a year. | A provider-reported human-preference comparison on a defined setup, not a general ranking for other prompts. See Google DeepMind’s Veo page. |
| Runway evaluation | 250 image-to-video prompts; the retrieved article does not state a year. | Runway reports testing configurations across a workload-specific dataset. This is an example of test design, not a required sample size. See its comparison framework. |
These figures describe different experiments and goals, so they should not be compared as though they were a single leaderboard.
Score outputs with a shared rubric
Before anyone reviews the clips, define what a passing result looks like and use the same scale for every candidate. Separate criteria so a reviewer can identify why an output missed the mark. The dimensions below combine the kinds of evaluation discussed in Video-Bench and T2VEval with operational needs teams commonly have to assess themselves.
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| Dimension | What reviewers should assess |
|---|---|
| Prompt and input alignment | Are the requested content, actions, and reference-image details present? |
| Visual quality | Does the result meet the team’s defined craft and technical threshold? |
| Motion and temporal consistency | Does motion look acceptable, and does the scene remain coherent throughout the clip? |
| Controllability | Do changes to prompts or inputs reliably produce the intended changes? |
| Workflow and technical usability | Can the output be used in the intended workflow, with acceptable input/output support and handling? |
Use a consistent numeric scale or explicit pass/fail criteria, with written anchors so different reviewers apply it similarly. When practical, hide the model identity during review to reduce brand or expectation bias. Weight the dimensions according to the work: for example, strict input alignment may matter more than stylistic preference for a product demonstration.
Video-Bench discusses human-aligned evaluation, including condition alignment, aesthetic quality, and temporal consistency; T2VEval reports subjective measures such as overall impression, text consistency, realness, and technical quality. Automated metrics can help sort or flag large batches, but the cited work does not establish a single automated score as a substitute for human judgment across production contexts. Validate any automated signal against the decisions your reviewers need to make. See the Video-Bench paper and the T2VEval paper.
Measure the cost and time of usable output
Nominal generation price alone can be misleading. For every request, log completion or failure, retries, generation latency, reviewer time, spend, and whether the result passed your acceptance bar. Include finishing effort if a clip needs work before it can be used.
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Also assess workflow support: documentation, SDKs, webhooks, versioning, and error handling can affect the effort required to operate a model in production. Runway’s comparison framework highlights API maturity and cost per usable output; its model router documentation describes cost, latency, and quality preferences as selection considerations. Those are vendor-described features, not a substitute for your own evaluation.
Preserve enough detail to reproduce the comparison
For each generation, retain the prompt, input asset, settings, date, output, model name and identifier, and any version or snapshot label the provider exposes. Note interface or API differences that affected the result. This makes later reviews and reruns interpretable instead of relying on memory or a folder of unlabeled clips.
Version pinning can improve consistency when a provider exposes it. OpenAI’s Sora 2 Pro API documentation says snapshots can lock a specific model version so performance and behavior remain consistent. A pinned version helps make a comparison repeatable; it does not establish that the model is the best fit. Rerun the pilot when the model, API, or material parts of the workflow change.
Make safety and procurement part of the decision
Evaluate the content and workflows the team actually intends to use, then have the appropriate stakeholders review each candidate’s current use policies, privacy and retention terms, commercial conditions, and contract requirements. Do not assume that controls or terms documented by one provider apply to another, or that they are the same in every jurisdiction.
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For example, OpenAI’s Sora system card discusses risks including non-consensual intimate imagery and misleading video, as well as classifier-based mitigation. That describes OpenAI’s documented approach; it is not a cross-provider comparison or proof that a team’s particular risks are covered. Confirm applicable terms and safeguards directly with each candidate before procurement.
Turn the pilot into a decision
Apply hard gates first; a high average score should not conceal a failure on a requirement the team considers mandatory. For candidates that pass, compare the rubric results alongside cost per accepted output, latency, reliability, and workflow effort. Set score weights before reviewing results, and document the trade-offs so a decision can be revisited if requirements change.
A provider-reported benchmark can add context when its task, sample, and comparison scope match your needs. It cannot predict your team’s results by itself: Google DeepMind’s reported 355-pair preference test, for example, concerns a defined VBench image-to-video setup, while other published evaluations use different data and measures. The most decision-relevant evidence is the controlled pilot on your own representative workload.
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