An AI Video Model Selection Scorecard for Creator Projects

Choose an AI video workflow with a task-specific scorecard that separates must-have requirements from visual preferences. Access, permissions, output format and factual fidelity are gates; style is a preference after those gates pass. This prevents an attractive sample from winning a comparison even though it cannot deliver the actual project.

Documentation basis: Kling 4 on fal; Creating with Runway Gen-4.5. The recommendations below are editorial analysis and original teaching exercises, not a hands-on product benchmark or a forecast of search performance.

What belongs in the must-have column?

List permitted use, supported inputs, required framing, essential subject details and deliverable format. A failure in one of these can block the workflow regardless of visual appeal. Keep the list short enough that each requirement is genuinely necessary.

What belongs in the preference column?

Include motion style, lighting feel and other creative choices that can vary without breaking the brief. Evaluate these only after factual and delivery checks pass. This avoids trading away product accuracy for a more cinematic-looking result.

How should revision cost be recorded?

Track the request, attempts, charges shown and manual repair time for one bounded change. Note whether correct elements changed unexpectedly. The ability to revise a nearly finished asset is central to client work and may outweigh a small first-pass quality advantage.

How do you compare unavailable models?

Mark them untested or unavailable in the relevant account. Use official documentation to describe supported routes, but do not assign invented quality scores. Availability itself can be a practical constraint when a project has a real deadline.

What should the final recommendation say?

Name the task and conditions under which the workflow fits, the main limitation and the evidence level. Distinguish documentation review from actual tests. A conditional recommendation is more useful than a universal ranking that hides different project requirements.

A practical exercise

Build a scorecard for a vertical product clip with three gates: correct product details, usable portrait framing and an editable audio workflow. Compare only candidates that pass. Record style preferences separately and retain the option to film the shot if no generation route meets the gates.

Before you decide

• Separate gates from preferences.
• Record revision effort.
• Label untested candidates.

Sources and scope

Sources checked October 24, 2026. Follow the current documentation for your exact model, application, account and destination.

• Kling 4 on fal
• Creating with Runway Gen-4.5
• Google DeepMind Veo

AN AI VIDEO MODEL SELECTION SCORECARD FOR CREATOR PROJECTS
Original Creator Lane decision worksheet | 2026-10-24

TASK
Audience / project:
Required outcome:
Current workflow and its specific limitation:
Options actually available without a new purchase:
Source material and permission:

DECISION QUESTIONS
What belongs in the must-have column?
Your evidence / answer:
Unresolved detail / owner:

What belongs in the preference column?
Your evidence / answer:
Unresolved detail / owner:

How should revision cost be recorded?
Your evidence / answer:
Unresolved detail / owner:

How do you compare unavailable models?
Your evidence / answer:
Unresolved detail / owner:

What should the final recommendation say?
Your evidence / answer:
Unresolved detail / owner:


PRACTICE BRIEF
Build a scorecard for a vertical product clip with three gates: correct product details, usable portrait framing and an editable audio workflow. Compare only candidates that pass. Record style preferences separately and retain the option to film the shot if no generation route meets the gates.

ACCEPTANCE CHECKS
[ ] Separate gates from preferences
Evidence:
[ ] Record revision effort
Evidence:
[ ] Label untested candidates
Evidence:

RESULT LOG
Option / exact version / account or interface:
Date / source URLs checked:
Input / settings / output version:
What was actually tested (leave blank if not tested):
Accepted results / failures / correction time:
Actual charges, if any (do not estimate as measured):
Privacy / permissions / delivery checks:
Decision and main limitation:
Next action / owner:
Recheck trigger:

REFERENCES
Kling 4 on fal: https://fal.ai/kling-4
Creating with Runway Gen-4.5: https://help.runwayml.com/hc/en-us/articles/46974685288467-Creating-with-Gen-4-5
Google DeepMind Veo: https://deepmind.google/models/veo/