Which AI Image Generator Fits Your Workflow?

Start with the deliverable

An image for a mood board, an editable illustration, and a feature inside your own app are different jobs. Start with the file you must deliver, how you will revise it, and where the work needs to happen. Those requirements eliminate more unsuitable options than a gallery of impressive examples.

For browser-based exploration, consider Midjourney. For editable vector output, start with Recraft. For an application that generates and revises images, examine GPT Image, FLUX, or Gemini. Firefly and Stable Diffusion become particularly relevant when your production environment or deployment requirements drive the decision. Choose the starting point that matches your deliverable.

Eight options at a glance

Use external evaluations to narrow the shortlist

For an app that must generate an image and then revise it, put GPT Image 2.5 Sunburst on your first trial list. Its documented generation and editing APIs fit that workflow, and the evidence is encouraging in both tasks: the max variant scored 1222 (95% interval 1212–1232) in AA-Image-T2I v2.1 when checked October 21, while Arena’s October 19 single-image editing table places the named model first at 1514±6, still preliminary. Use it for a quality-first editor trial, then measure the cost and corrections needed on your own assets.

If you need inexpensive 1K drafts before committing to a final composition, Nano Banana 2.1 is a sensible starting candidate. Google’s listed image-output cost is $0.0336 per 1K image output on Standard billing, before input, text/thinking output, and any billable search grounding. Its text-only generation result was 1176 Elo (95% interval 1164–1188), fourth in the AA v2.1 pool checked October 21; Arena’s October 7 table separately records 1328±9, preliminary. These results justify trying it for draft composition. For a reference-heavy brief, use the documented reference inputs and judge whether the exact people, products, and details survive.

See the dated image evaluation snapshot for model labels, samples, settings, and source links. Keep SVG production on the Recraft route when editable vector output is the deciding requirement.

1. Exploring visual directions: Midjourney

Put Midjourney on your shortlist when you want to work through prompts and image or style references in a first-party interface. Its web and Discord access make this a different proposition from integrating a generation endpoint into software. The version documentation also shows why the selected model matters: editing and output-size behavior vary.

The practical gate is confidentiality. Creations are public by default, including work made in private Discord servers or DMs. Stealth has plan and surface limitations. Read the privacy notes before uploading unreleased work. This is a workflow recommendation, not a claim that its images look better.

2. Delivering editable vectors: Recraft

If the handoff requires SVG rather than a flattened picture, examine Recraft’s vector variants. The V4.1 reference documents editable vector output alongside raster options. That is a concrete reason to shortlist it for an illustration or asset workflow where the file structure matters.

Select the exact model before designing the process. Flash is raster-only, while the documented inpainting endpoint supports V3 and V3 vector, not every newer generation model. Also check the usage conditions: free UI assets are not licensed for commercial use; paid UI and API terms differ. An SVG output still needs inspection in your intended editor.

3. Building an image editor into an app: GPT Image

GPT Image is worth examining when generation and revision belong inside your product. OpenAI documents direct generation/editing through the Image API and conversational image tooling through Responses. The image-generation guide also documents PNG, JPEG, and WebP output; transparency requires PNG or WebP.

Budget for input and output tokens rather than assuming a flat image fee. Responses workflows can add main-model usage costs. Treat precise typography, placement, and repeated-character consistency as acceptance tests: the documentation acknowledges failures in those areas. This guide covers the API, not a verified mapping of ChatGPT subscription plans to image models. Commercial-use terms remain unverified in our catalog.

4. Working from several references: FLUX or Gemini

For a brief combining several source images, compare the documented reference workflow first. FLUX 3 Image accepts up to ten references. Nano Banana 2.1 documents up to fourteen, with distinctions between character and object references, plus text, image, video, and PDF inputs.

These limits establish what you can submit, not how faithfully the result will preserve a person or product. Test the details your brief cannot lose. For FLUX, retrieve generated results promptly as part of the workflow: its documented download window is one hour. For Gemini, check paid versus unpaid data handling before submitting confidential references. The Imagen shutdown recorded here applies to the Gemini API, not every Google service.

5. Building an Adobe-based production pipeline: Firefly

Consider Firefly when the job extends beyond a single generation into instructed edits, compositing, upscaling, or custom-model operations. Those services are described in the Firefly API overview. Custom-model access is managed separately, so verify the route you actually need.

Keep the application and the model distinct. The Firefly app also hosts partner models; choosing one does not make it an Adobe-owned Firefly model. A consumer subscription does not establish API entitlement. Our research did not verify API unit prices or all endpoint export formats, and Image Model 5’s lifecycle label differs between documentation surfaces. Resolve those details before committing a pipeline.

6. Running the model yourself: Stable Diffusion

Shortlist Stable Diffusion when operating the model is part of the requirement, rather than simply requesting an image from a hosted service. Stability’s licensing page describes community eligibility and enterprise requirements for covered models. Self-hosting still brings compute, maintenance, and operational responsibilities.

If you prefer hosted access, the SD 3.5 API documents text-to-image and image-to-image generation. Keep separate Stable Image editing, control, and upscaling services distinct from base-model capabilities. Do not assume that one family name gives every variant the same license, features, or privacy properties.

Compare the cost of a finished asset

A subscription, an API credit, a megapixel, and an output token measure different things. Compare them only after fixing the model, input references, output size, and expected number of revisions. UI credits generally should not be treated as API credit balances.

For a small evaluation, record cash spend, attempts, accepted files, and correction time separately. Include required edits and upscaling. Compare spend per accepted deliverable, not the cheapest advertised generation. We have omitted currency prices because incomplete tables, promotions, and different billing scopes would imply a precision this comparison cannot support.

Frequently asked questions

Methodology and review date

This guide draws on eight image-family records in our reference catalog, using provider-owned sources checked on October 21, 2026. That date records source review, not model release. The selections focus on documented capability fit. Recheck availability, billing, and terms before adoption.