How to Choose an AI Image or Video Model: A Capability-First Workflow

There is no universally best image or video model. The useful question is: which available workflow removes the largest production risk for this specific shot?

A creator making a typography-heavy poster has different needs from an animator preserving a character across scenes. A social concept test values fast iteration; a final campaign hero may value resolution and reference control. A capability-first selection process prevents model shopping from replacing creative planning.

Write a model-agnostic task brief

Describe the task without a model name:

Create a six-second vertical product reveal from an approved first image.
The bottle geometry and label must remain stable. One slow camera move.
No dialogue. Final frame needs clean space above the product for a title.

Now the requirements can be mapped to capabilities: image-to-video input, strong reference fidelity, vertical output, short duration, controlled camera, and a usable end frame.

Score the requirements

Use three priority levels:

• Must: failure makes the output unusable.
• Should: important, but can be fixed in editing.
• Could: improves convenience or style.

Example:

A model that wins on optional resolution but fails a required input mode is the wrong choice.

Match the input mode

Text to image

Best for open-ended ideation, illustrations, backgrounds, and scenes without a fixed subject. Evaluate prompt following, composition, text rendering, and style range.

Image to image or reference editing

Best when identity, product shape, layout, or art direction must carry forward. Evaluate which parts can be preserved independently and how many references the workflow supports.

Text to video

Best for shots whose subject and composition can remain flexible. Evaluate motion quality, instruction following, duration, aspect ratio, camera behavior, and whether audio matters.

Image to video

Best when the opening composition is approved. Evaluate first-frame fidelity, subject stability, motion range, and how the camera departs from the source image.

First-and-last-frame video

Best when the ending is a hard requirement. Evaluate whether both anchors can remain compatible and whether the transition fits the duration.

Multimodal reference video

Best when separate assets guide identity, action, camera, rhythm, or sound. Evaluate supported reference types and the ability to assign each input a clear role.

Separate exploration from finishing

The model used for concepting does not have to be the model used for final delivery.

An efficient pipeline can be:

1. fast, lower-cost generations for composition;
2. one chosen direction rebuilt with stronger reference control;
3. high-quality generation only after motion and layout are approved;
4. conventional editing, typography, grading, and audio finishing.

Do not pay for maximum output quality while the idea is still changing.

Build a small evaluation prompt set

Test candidate models with the same three to five tasks that represent your work. Avoid a single spectacular prompt.

For an image workflow, include:

• a clean subject on a simple background;
• a structured layout with short exact text;
• a reference edit that changes one attribute only;
• a scene with multiple objects and spatial relationships;
• the most difficult material or identity your team uses.

For video, include:

• one locked shot with subject motion;
• one slow tracking or push-in shot;
• one image-to-video identity test;
• one interaction involving hands or objects;
• one clip with a defined final frame or audio requirement.

Review outputs blind when possible. Save prompts, settings, time, cost, and failure notes.

Use a weighted scorecard

Weights should follow the project. A music video may give motion and audio more weight; a product catalog should heavily weight geometry and label fidelity.

Read provider labels carefully

Aggregators can expose models from multiple providers or routes. Record the model name and provider displayed when the task is created. Similar labels do not guarantee identical versions, settings, moderation, pricing, or availability.

Check official model documentation for supported modes and limits. In the current market, those details change frequently, so avoid building a permanent workflow around a remembered specification.

Include the rest of the production system

Model output quality is only one part of total quality. Consider:

• task history and reproducibility;
• reference-file handling;
• storage and expiration of generated assets;
• team review and permissions;
• safety rules and consent;
• metadata and provenance;
• reliability during peak production;
• support for the final export format.

A slightly less impressive single output may be the better production choice if the workflow is repeatable and auditable.

Decision template

Project:
Final channel and format:
Must-have input modes:
Must-preserve details:
Allowed changes:
Duration/resolution requirements:
Audio requirement:
Preview budget and deadline:
Final-quality budget:
Rights/disclosure constraints:
Evaluation prompts:
Selected model/provider:
Reason for selection:
Fallback route:

Explore the available routes in the Seedream AI Image Generator and AI Video Generator. Then validate the selected workflow with the pre-publish quality-control checklist.