Choose an AI Image Model by Job: A Practical Guide

You write a prompt for a sale banner: Summer sale, 30% off, bold white headline on a coral background. The image looks lovely, but the headline reads "Sumer saIe, 3O% ofF". You rerun it five times and spend credits on five slightly different typos.

The prompt wasn't the main problem. You picked a model for the wrong job. This guide is about how to choose an AI image model by asking what the image has to do, not which model is newest.

Start with the job, not the model name

Model lineups change every few months. Names get retired, and new versions replace old ones. Even OpenAI's own docs now carry a shutdown date for an earlier GPT Image model (they list gpt-image-1 as shutting down on October 23, 2026, per the OpenAI image generation guide). A guide built around "the best model" goes stale fast. A guide built around jobs doesn't.

So ask five questions before you open a model list:

1. Is this a draft to explore ideas, or a final I'll publish?
2. Does it contain text that must be spelled correctly?
3. Am I editing an existing image, or starting from nothing?
4. Does it need to match something: a product, a face, a brand look?
5. Where will it end up: a phone screen, a print file, a video start frame?

Your answers narrow the field more than any leaderboard will.

Job 1: Fast drafts and idea hunting

When you don't yet know what you want, you need volume, not polish. Use a fast, cheap model and generate many variations of the composition.

Black Forest Labs' FLUX.1 schnell model card describes a 12-billion-parameter model that produces images in 1 to 4 steps and is released under Apache-2.0. That's the design goal: speed. The same card is honest about limits. It may fail to match the prompt, and prompt following is heavily influenced by prompting style.

In Mediafy Studio, the cost of each run shows on the Generate button before you run it, so you can check what a draft costs.

How to run a draft round:

• Write one short prompt, such as a ceramic mug on a wooden desk, morning window light.
• Generate 6 to 10 variations.
• Pick the composition you like. Note what you'd change.
• Only then move to a stronger model for the final.

Don't judge fine detail at this stage. Hands, small text and skin texture are exactly what draft models do worst.

Job 2: Finals that follow a detailed prompt

Once the idea is settled, a larger model earns its cost. This is where you write a longer prompt with subject, setting, light and lens. Our prompt structure guide covers that, and the Enhance button in the Studio can expand a short idea for you (see Prompt Enhancer).

Models such as Nano Banana 2 and Seedream sit in this tier in the Studio. Google's developer page describes Nano Banana 2 (Gemini 3.1 Flash Image) as the "high-efficiency counterpart" to its Pro image model, tuned for speed and volume, and points to Gemini 3 Pro Image when you want the highest fidelity. It lists 0.5K, 2K and 4K outputs (1K by default), plus extra aspect ratios such as 4:1 and 8:1 (Google AI for Developers, Gemini 3.1 Flash Image). For more on it, read Nano Banana 2 Explained. For the FLUX tiers, see FLUX Models Compared.

Resolution options on a vendor page are the maximum the API offers, not what you'll always get in a given app. Check the output size in your tool before you plan a print job.

Job 3: Images with text in them

Posters, banners, packaging mockups, thumbnails. Text is the failure that sends people back to rerun the same prompt.

Two honest points. First, vendors say it's improving: Google lists better multilingual text rendering for Nano Banana 2. Second, nobody says it's solved. OpenAI's guide states that text placement and clarity are improved but "still imperfect," and that layout-sensitive compositions can be hard to place precisely.

What to do:

• Choose a model marketed for text, such as Ideogram or Recraft, or a recent GPT Image or Nano Banana model, and test it on your actual wording.
• Keep the text short. Three words survive better than a sentence.
• Put the exact text in quotes in the prompt.
• If the text must be perfect, add it afterwards in a design tool. That's often the most reliable route.

Full techniques are in How to Get Readable Text Inside AI Images.

Job 4: Editing an image you already have

Editing is a different task from generating. You want the model to change one thing and leave the rest alone.

OpenAI's guide says its edit endpoint can combine several input images and that editing can happen iteratively across turns. It also warns that masking is entirely prompt-based, so the model may not follow the mask's exact shape. Treat any "change only the background" instruction as a request, not a guarantee. Check the edges and the parts you wanted untouched.

A practical edit prompt: Keep the product, label and lighting exactly the same. Replace the background with a light grey studio sweep. Then compare against the original at full size.

If the edit drifts, make the change smaller. Ask for one change per round.

Job 5: Products, characters and brand consistency

This is where people lose the most time. "Consistent" means two different things, and models handle them differently.

• Same product: Start from a real photo of the product as a reference, then generate scenes around it. Expect small label and logo errors. See AI Product Photography.
• Same person or character: This is a known weak spot. OpenAI's own guide says its model may struggle to maintain visual consistency for recurring characters. Don't rely on a prompt alone.

Mediafy Studio handles this with saved characters and Seeds rather than model training. You design a character once, reuse the same face, and type @ in the Studio to bring it in. Seeds are saved sets of reference images for a consistent look. They're references, not custom-trained models, so some variation between runs is normal. The full method is in Keep a Consistent AI Character Across Images and Video.

Job 6: Images that will become video

If the image is a start frame, the best model is the one that gives a clean, uncluttered frame with room to move. Busy backgrounds and tiny faces tend to animate badly. Pick the aspect ratio first, because it has to match the video (our aspect ratio guide covers the common formats). Then read Image to Video: Pick a Start Frame That Animates Well.

A quick decision table in words

• Exploring ideas: fast draft model, many runs, low cost.
• Final hero image: a larger model, long prompt, one or two runs.
• Text in the image: a text-capable model, short words, plan to fix in a design tool.
• Editing: a model with image-edit support, one change per round.
• Same character across a series: saved character or Seed plus a capable model.
• Start frame for video: clean composition, correct aspect ratio.

A 10-minute test routine for any new model

Reviews and leaderboards can't tell you how a model handles your subject. A short test can.

1. Write three prompts from your real work: one simple, one detailed, one with text.
2. Run each on two or three models. Use the same prompt wording.
3. Run each combination twice, because results vary run to run.
4. Score on only what matters to you: spelling, hands, product accuracy, mood.
5. Note the credit cost next to each result. A model that's 4 credits and right the second time can beat a cheaper one that needs six tries.

Keep the winners in My Prompts so you don't repeat the test. The credits guide explains costs and the automatic refund on failed generations.

Common mistakes

• Rerunning a failed prompt on the same model. If a model keeps misspelling, switch jobs or models.
• Judging a draft model on detail. It isn't built for that.
• Asking for everything in one image. Text, a face, a product and a complex scene together multiply the failure points.
• Trusting spec sheets. A resolution or reference-image limit tells you what's possible, not what looks good.
• Ignoring negative prompts' limits. Not every model respects them. See Negative Prompts.

If you want a structured way to build these habits, the AI Creator Lab course is there. Otherwise, the quickest route is the test routine above with your own prompts.

You can try several image models side by side in Mediafy Studio: sign in with Google and run your first test.

Sources

• OpenAI, "Image generation" (OpenAI developer docs): https://developers.openai.com/docs/guides/image-generation
• Google, "Gemini 3.1 Flash Image" (Google AI for Developers): https://ai.google.dev/gemini-api/docs/models/gemini-3.1-flash-image
• Black Forest Labs, "FLUX.1-schnell" model card (Hugging Face): https://huggingface.co/black-forest-labs/FLUX.1-schnell