Opening an image generator and seeing a long list of model names can feel like being asked to choose a camera before you know what you are photographing. Some names describe model families, others identify a particular release, and several options may appear capable of producing the image you want. The useful starting point is the job, not the newest label.

Names such as Nano Banana, Seedream, Flux, GPT Image, Ideogram, Recraft and Mystic can refer to different model families or offerings within the wider image-tool landscape. A family name is not a complete specification. Identify the particular version, host interface and task before deciding what an option can do.

Start by defining the required result. A photographic scene, a short illustrated phrase and a revision based on a product reference pose different problems. Listing those requirements first makes it easier to notice whether the available controls address the job or simply provide another way to produce an attractive demonstration.

Separate the platform from the model

The platform is the environment where you organize the task. The model is one of the systems used to generate or edit the image. Two workflows inside the same platform can behave differently because they use different models, versions or settings.

This distinction helps explain why a result may change when you switch an option in the interface. It also prevents a common mistake: assuming that every feature supported by one model is supported by all the others. Reference handling, image dimensions, editing behavior and credit requirements can vary.

Keep the model and relevant settings in your project notes. If a colleague needs to revisit an approved direction, that information gives them a better starting point. It will not guarantee identical results across every future update, but it is more useful than an image file with no production context. A host offering several AI image models makes the distinction practical: the interface may stay familiar while the selected model changes. Magnific is one example of that arrangement. Check which model actually produced each image before attributing a result to the platform as a whole.

Nano Banana: evaluate reference-led work carefully

For a Nano Banana reference-led experiment, begin by identifying what the reference must preserve. A product’s shape, a character’s hairstyle and a scene’s composition are different constraints. Check the documented inputs of the version you are using before building the experiment around a control that may belong to another release.

Use a simple assignment to evaluate the workflow. Take an original reference and ask for one controlled change, such as a different setting or a revised color treatment. Compare the result with the reference rather than judging it only on visual appeal.

Inspect details that are easy to overlook: labels, accessories, object proportions and the relationship between the subject and its shadow. A model can produce a convincing scene while changing something that matters to the project. The right choice is the one that fits your actual tolerance for those changes.

Seedream: distinguish the family from a specific release

Seedream is a model family whose releases should not be treated as interchangeable names. An older tutorial about Seedream 4.5 may describe a different set of options from a newer release displayed in an interface. Record the version alongside the task so comparisons do not mix distinct capabilities.

For a campaign involving recurring subjects, create a small reference test. Ask for the same fictional product or character in a few controlled settings, then compare identifying features. Keep the scene changes modest at first so the evaluation is about consistency rather than the complexity of the prompt.

Choose based on the complete production requirement. A result may look attractive but need too much correction to support a product catalog. Another may be less dramatic but easier to adapt into a coherent series. The model’s usefulness is connected to the work that follows generation.

Flux: pay attention to the variant and purpose

With Flux, the family name alone is not enough to describe a workflow. Read the current variant’s description and confirm which input types and settings the selected implementation supports. The interface hosting a model can also affect which controls are exposed, so compare the actual working setup.

A helpful evaluation is to use a brief with clear spatial relationships: a single object on a table, a specific light direction and a simple background. This makes it easier to see whether the result follows the composition you intended. Overly elaborate prompts can hide basic weaknesses behind attractive detail.

If you are testing editing, preserve a clear baseline. Make one change, compare the result and note any unintended alterations. Do not assume a model is unsuitable because one complicated prompt failed, or ideal because one image succeeded. Look for behavior that is useful across the tasks you perform regularly.

GPT Image: test how instructions translate into a composition

When evaluating a GPT Image workflow, look at how the result expresses relationships in the brief. Are the objects merely present, or are they arranged in the intended hierarchy? A useful test includes one clear spatial instruction and a composition requirement, then checks both in the output.

Describe a scene with a clear hierarchy. For example, a fictional workshop illustration might need one person at a central table, a small set of tools and an empty area for a heading. Check which element receives attention and whether the arrangement supports the intended message.

Use revisions to clarify rather than complicate. If the model misinterprets a relationship, rewrite that part in simpler language. Long prompts can contain conflicting instructions, and a model comparison is less meaningful when the brief itself is ambiguous. A clean brief makes both strengths and limitations easier to see.

Ideogram: inspect lettering as part of the design

For an Ideogram trial involving lettering, evaluate the entire composition: spelling, contrast, line breaks, placement and the relationship between words and imagery. A correctly spelled phrase can still be difficult to read. Include a short piece of real project copy rather than a decorative word chosen only because it is easy to render.

Begin with a short fictional headline and a restrained layout. This makes errors visible and keeps the exercise focused. Test the image at its intended size, because lettering that looks acceptable when enlarged may become unclear in a small social post.

For essential information, preserve an editable text workflow. Dates, prices and contact details should remain easy to correct. Generated typography can help establish a visual idea, while a layout tool provides dependable control over the final wording. Model selection does not remove that production responsibility.

Recraft: consider the asset’s final format

For a Recraft experiment, define the asset’s final format as carefully as its subject. An icon, a small illustration and a large decorative image need different levels of detail. Confirm the available output type in the selected workflow instead of assuming that every interface exposes the same design features.

Test related assets as a set. Ask for visual directions around several concepts and compare the consistency of shapes, colors and complexity. The evaluation should reflect whether the assets work together in a design system, not just whether each image looks polished on its own.

Verify the format available in the particular workflow you are using. An image with a vector-like appearance is not automatically an editable vector file. If a project requires scalable paths or other specific deliverables, confirm the export behavior before building the entire design around an assumption.

Mystic: include it in a brief-led comparison

Treat Mystic as another candidate for a brief-led image comparison. Give it the same intended subject, placement and visual constraints as the other options you are considering. An impressive alternative style may be interesting, but it is not necessarily a better answer to the assignment you need to finish.

Try a scene with a defined atmosphere, subject and composition. Compare how the result handles the parts of the brief that matter most to you. If your work centers on editorial imagery, assess clarity and mood. If it centers on product concepts, assess the relationship between form, material and light.

Keep the comparison fair. Use similar prompts and review at comparable sizes. Record what you would need to change before publishing. The most visually impressive first result may not be the one that reaches an approved final asset with the least friction.

Build a small selection process

Choose three representative briefs from your work and a small number of model options. For each result, note whether it follows the brief, what requires correction and whether the available editing workflow supports those corrections. Include unsuccessful attempts so the comparison reflects the process rather than only its highlights.

Avoid turning a small evaluation into a universal ranking. A model can be a good fit for one task and an awkward fit for another. Your aim is a short internal guide: which option to try first for an illustration, which to investigate for reference editing and which needs further testing for text-heavy designs.

Revisit the selection notes when requirements or available versions change. A short record of which setup handled a particular task well is easier to use than a permanent ranking. Include unresolved weaknesses too, so the next person knows when to begin with a different tool or production method.