AI image model selection guide

There is no single best AI image model for every assignment. The useful question is which model gives you the controls, interpretation, and revision path required by the current job. A product layout, character edit, text-heavy poster, and loose concept sketch create different risks.

Wenimg brings several model families into one workspace, but a consistent interface does not make their behavior identical. Use the model directory to review available options and the generator to run a small comparison with the same brief.

Begin with the input you already have

The first decision is often the input format:

• Text only: useful for exploration, original scenes, visual directions, and early concepts.
• One reference image: useful when composition, identity, product shape, palette, or style needs continuity.
• Several references: useful only when the selected model supports them and each reference has a clear role.
• An existing image to edit: requires a model and workflow that accept image input and can follow change-versus-preserve instructions.

Do not select a model based on an impressive gallery example if it cannot accept the material required by your workflow. The active Wenimg controls are the practical source for what can be submitted through the product.

Turn the assignment into evaluation criteria

Before comparing models, write three to five conditions that define success. For a product hero, the list might be:

1. Product silhouette remains accurate.
2. Label and key details stay legible.
3. The left side remains clear for marketing copy.
4. Reflections look physically plausible.
5. The result works in a wide crop.

For a character illustration, identity, pose, clothing details, and style consistency may replace product accuracy. For a poster, layout hierarchy and rendered text may carry more weight. A written scorecard prevents the most visually dramatic result from winning when it fails the business requirement.

Compare with one shared brief

Keep the purpose, subject, composition, and constraints stable across the first comparison. Use the nearest equivalent aspect ratio and quality setting available for each model. Generate a small sample, then score results against the same criteria.

Record four things:

• instruction following;
• visual quality at the required size;
• consistency across repeated attempts;
• effort needed to reach an acceptable revision.

The fastest first result is not always the fastest production workflow. A model that needs repeated prompt rewrites may cost more time than one whose initial image looks less polished but responds predictably to edits.

Choose by workflow, not by brand name

Model families such as GPT Image, Gemini image generation, Seedream, and Grok can evolve quickly. Treat version names and provider descriptions as current configuration, not permanent traits. Match the available model to the task in front of you:

• choose a text-first route when broad ideation matters;
• prioritize reference handling when identity or composition must carry over;
• prioritize layout and typography evaluation for poster or ad work;
• prioritize controlled editing when an approved asset must be changed without rebuilding the scene;
• check output dimensions and aspect ratios before designing around a fixed placement.

See the prompt examples for visual directions, but remember that a gallery shows successful outputs, not the complete distribution of possible results.

Run a low-cost production check

Before committing a campaign or batch job, test the full handoff:

1. Generate at the intended ratio.
2. Inspect important details at final display size.
3. Try one realistic revision request.
4. Export and place the image in its actual layout.
5. Confirm that the process can be repeated by another team member.

This catches issues that a standalone preview can hide, including unusable crop, weak text-safe space, inconsistent details, or an editing loop that takes too many attempts.

Keep the decision current

Save the brief, references, settings, accepted output, and reason for selecting the model. Re-test when the model version, provider route, or project requirement changes. The goal is not to declare a permanent winner. It is to make a defensible decision with evidence from the actual job.

For current platform details, use first-party references such as the OpenAI image generation guide, Google Gemini image generation documentation, and Volcengine Seedream documentation.