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Leonardo AI Model Selector: Choose Image and Video Models by the Job

Published September 01, 2026Last updated September 01, 2026By Gary Whittaker
What this guide will help you do

A durable Leonardo model-selection system for images and video: define the job, compare controlled outputs, log evidence and avoid rebuilding your workflow around every new model launch.

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Choose Leonardo Models by the Job

Leonardo is increasingly a multi-model environment. That is useful only when model choice follows a production requirement. “Newest” and “most expensive” are not creative specifications.

Define the test first

Image job

Portrait identity, cinematic key art, readable design/text, product fidelity, illustration, photorealism, fast ideation, editing or style transfer.

Video job

Subtle image animation, character performance, camera move, dynamic action, start/end transition, reference-driven motion, audio-enabled scene or speed/cost draft.

The JR model test

  1. Use the same source/prompt/reference package.
  2. Choose two models that plausibly solve the job.
  3. Keep aspect ratio and core settings aligned where the interfaces permit.
  4. Generate the smallest useful comparison.
  5. Score the actual requirement.
  6. Record cost/time only after quality is judged.
Score 1–5: requirement fit / identity preservation / prompt adherence / unwanted drift / editability / speed / token efficiency / readiness for next production step.

When a fast model wins

Drafting, storyboard exploration, motion direction tests and campaign ideation may value speed and iteration more than maximum finish. Spending premium capacity before the direction is approved is usually a workflow error.

When a stronger model earns the cost

Hero frames, close identity shots, difficult motion, client-facing finals or shots that failed repeatedly on cheaper models can justify a more capable option. The reason should be visible in the output—not assumed from the model name.

Separate model failure from prompt failure

If every model produces the same wrong concept, the brief may be wrong. If one model alone loses identity, motion or typography, model fit is the stronger suspect. Change one variable at a time.

Maintain a living selector, not an evergreen ranking

Leonardo adds and retires models frequently. The current platform has recently added new image/video families while older models have also been retired from some workflows/API paths. A static “Top 10 Leonardo Models” article decays. A job-based test remains useful.

Living model log: date / model / image-or-video / job / source / result / strength / weakness / speed / token cost at test time / keep for / avoid for.

Current model names belong in a dated box

The live Leonardo interface currently exposes multiple image and video choices, and that roster can change without the creator workflow changing. Verify the current app when exact availability matters. Existing JR articles such as Motion 2.0 and Veo on Leonardo remain useful context, but they are not the architecture.

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