The right AI image model depends on the work around the image. A campaign may need a reliable product reference. A design system may need editable typography. An API workflow may need a fixed model version. A local pipeline may need weights that its license actually permits it to run.
That is why a row of stars for “image quality” is not useful. A score hides the prompt, model version, reference material, evaluation method, and the constraint that decides whether an output can ship. This guide compares current model families by documented capabilities, access path, and terms that deserve checking before a real project starts.
- Choose a model against a representative brief, not a universal quality score.
- Record the exact model ID or version, prompt, inputs, date, and settings when an asset must be reproduced.
- Treat pricing, licensing, and commercial-use terms as current provider documents, because they can change independently of model quality.
How to compare AI image models without fake precision
A useful comparison starts with a job to be done. Write one brief that could plausibly go into production, including the required dimensions, product or character references, copy that must be legible, and the rounds of editing you expect. Run it through the exact models and plans you could actually use.
Then separate four questions that are often mixed together:
- Task fit: Can the model handle the composition, text, reference images, and edits the brief requires?
- Workflow fit: Is it available through the API, web app, local weights, or the design software your team already uses?
- Reproducibility: Can you pin a version or endpoint and retain the inputs needed to run the job again?
- Rights and cost: Do the current plan, license, and provider terms cover the intended use at the required volume?
Documented historical sample gallery
The four comparisons below preserve a documented run from August 14, 2026: GPT Image 2, API model ID openai/gpt-image-2, against Nano Banana 2, API model ID google/flash-image-3.1. Each pair uses an archived prompt, one request per model, and the first returned output. The details section on every image exposes the provider, settings, generation time, source URL, protocol, and file hashes.
These images remain visible because their method and model mapping are traceable. They are historical visual evidence for those exact API model IDs, not a scorecard or a proxy for newer model releases.
Largely standardized comparison protocol. Both models receive the same archived prompt, exactly one request, and the first result counts. Files are normalized to 1,024 × 1,024 pixels without cropping. Provider resolution, randomness, quality tier, and prompt processing can still differ. These images are a practical visual test, not a reconstruction of the arena configuration.
Character and detail
Anatomy, clothing, and spatial consistency
Prompt and settings
Create a square cinematic full-body portrait of a fictional bicycle courier waiting under a transparent umbrella at a rainy tram stop in Hamburg at blue hour. She wears a mustard raincoat, navy trousers, red sneakers, a silver helmet, and carries a teal messenger bag. Keep both hands visible, render the bicycle correctly, use realistic wet-street reflections, and include no readable brand names.
GPT Image 2. Provider Together, 1024x1024, n=1, seed=provider random, provider default steps, model ID openai/gpt-image-2, generated Aug 14, 2026, 12:13 PM. Source
Nano Banana 2. Provider Together, 1024x1024, n=1, seed=provider random, provider default steps, model ID google/flash-image-3.1, generated Aug 14, 2026, 12:15 PM. Source
Protocol gradually-image-comparison-v1, selection rule: first result.
Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6
GPT Image 2, image SHA 256: 82f6e2369ab4fb6efb0ef48feeff0d14f1d6087c64c35113a983456f0f0879ac
GPT Image 2, request SHA 256: db4ca97dc43effd578430b77f8ca559c7dd5cbe60e663ba9ecc2e89a33480b0d
Nano Banana 2, image SHA 256: 5d3a70ed3fe3328484725565d9c91fb25fc7f745bd66872dae432444493a83de
Nano Banana 2, request SHA 256: 406e70dda817fa504900359d5c374ebb06fc8ea29f34c8a15f630e9a10489aec
Infographic
Numbers, labels, and visual organization
Prompt and settings
Create a square German infographic titled SO FUNKTIONIERT PHOTOSYNTHESE. Show exactly four numbered steps in this order: 1 LICHT, 2 WASSER, 3 CO₂, 4 ZUCKER + SAUERSTOFF. Use a clean editorial science style with one plant cross-section, simple arrows, high contrast, legible labels, and no other text.
GPT Image 2. Provider Together, 1024x1024, n=1, seed=provider random, provider default steps, model ID openai/gpt-image-2, generated Aug 14, 2026, 12:14 PM. Source
Nano Banana 2. Provider Together, 1024x1024, n=1, seed=provider random, provider default steps, model ID google/flash-image-3.1, generated Aug 14, 2026, 12:15 PM. Source
Protocol gradually-image-comparison-v1, selection rule: first result.
Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f
GPT Image 2, image SHA 256: 6c15466e258e2b14a8e862959cb6a67b4f7a23896ea5d42c5a5618d848847711
GPT Image 2, request SHA 256: ed741d3d120188b9286d3576e7dcd3ace9dd7792978d4d5426022f32ed7ca381
Nano Banana 2, image SHA 256: 66123dd4e8cd5d22ecdca9cca8e4c5853a8046370aa4861ffee807a8c122c769
Nano Banana 2, request SHA 256: 10e973caab6f314ec731d9d377b19f5d51f279e9e2962133c49682bfbba3fe3d
Product photography
Materials, reflections, and fine details
Prompt and settings
Create a square premium product photograph of a brushed titanium wristwatch standing upright on dark green marble. The watch has a cream dial, thin black hands set to 10:10, twelve distinct hour markers, a realistic crown, and a dark brown leather strap. Soft window light from the left, controlled reflections, shallow depth of field, no text, no logo, no extra objects.
GPT Image 2. Provider Together, 1024x1024, n=1, seed=provider random, provider default steps, model ID openai/gpt-image-2, generated Aug 14, 2026, 12:13 PM. Source
Nano Banana 2. Provider Together, 1024x1024, n=1, seed=provider random, provider default steps, model ID google/flash-image-3.1, generated Aug 14, 2026, 12:15 PM. Source
Protocol gradually-image-comparison-v1, selection rule: first result.
Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3
GPT Image 2, image SHA 256: 471c71b30618afb1a8a67ab8363308d67f99c65ef6740ca72417806d06d3c346
GPT Image 2, request SHA 256: 7b27ff2c9b64529a36bebf2d572c3448f26e68f3e9d1ac6a3f2b5ead8698f60f
Nano Banana 2, image SHA 256: 509871921c450f6f07ec28f91a380992db1b3017108fd5928fe0aa9670a1ecdc
Nano Banana 2, request SHA 256: 631ce921c9772c7c2109900c8662471564a871def8e70f50ce29ce83ab71daab
Typography and layout
Legible text, hierarchy, and composition
Prompt and settings
Create a square editorial poster for an imaginary night train called MONDFALTER. Show the exact German headline MONDFALTER and the exact subline BERLIN NACH LISSABON. Use a restrained midnight-blue and warm-cream palette, one stylized moth, strong Swiss-grid typography, generous negative space, and no additional words or logos.
GPT Image 2. Provider Together, 1024x1024, n=1, seed=provider random, provider default steps, model ID openai/gpt-image-2, generated Aug 14, 2026, 1:59 PM. Source
Nano Banana 2. Provider Together, 1024x1024, n=1, seed=provider random, provider default steps, model ID google/flash-image-3.1, generated Aug 14, 2026, 1:59 PM. Source
Protocol gradually-image-comparison-v1, selection rule: first result.
Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419
GPT Image 2, image SHA 256: 434cf9939b9be2a2c2711e13d027c9438c47038bb32229ceeaf405d2e89d2afc
GPT Image 2, request SHA 256: 473a774dfe9f9b7bb597e9e8b63af4e770b6bc4e7f8bc90682636db3516e7353
Nano Banana 2, image SHA 256: 06ae79c07d1604d3da3bbfa80bac73ac15c88af5cb8c013faaf5c9ea93092305
Nano Banana 2, request SHA 256: 6f1909c093d741b97bc2b4fe76e889009776dbe8635d2a14dbedd89bb4cae8fb
For a constrained output comparison, use the AI image model comparison hub and keep its individual methodology with the result. The model selection below is not a leaderboard. It is a starting point for a test that matches your work.
Current image model families at a glance
Selection guide. Provider documentation is the source for every stated fit and access path.
Current AI image models, explained
GPT Image 2.5 Sunburst
OpenAI's model catalog identifies GPT Image 2.5 Sunburst as its most capable model for image generation and editing. It is the sensible candidate when an image workflow belongs in an OpenAI API integration and needs text or image input alongside generated output. The image-generation guide is the place to verify supported operations, sizes, safety requirements, and current pricing before implementation.
Do not transfer an older GPT Image result to this release. Pin the API model ID in a pilot, retain the request parameters and inputs, and rerun the brief when the implementation changes.
Nano Banana 2
Google calls Nano Banana 2, the Gemini 3.1 Flash Image model, its versatile generalist for image work. Its documentation describes 4K generation, world knowledge, reliable text rendering, multiple reference images, and conversational image processing. That combination makes it a practical shortlist candidate when one workflow needs generation and iterative edits, rather than a single text-to-image call.
Google also documents Nano Banana 2 Lite for speed and cost-sensitive jobs, and Nano Banana Pro for more complex visual tasks. Imagen models are deprecated and scheduled to shut down on August 17, 2026, so a new Google workflow should plan around the Nano Banana family instead.
FLUX.2
Black Forest Labs divides FLUX.2 by operational job. Its documentation puts [max] with maximum quality and grounding search, [pro] with production work, [flex] with fine-grained control and typography, and [klein] with fast, high-volume generation. The family also supports multi-reference editing, which is relevant when product, character, or style consistency is part of the brief.
FLUX is especially useful for teams that need to choose between a managed API and a local route. [klein] 4B has Apache 2.0 weights and is documented for consumer GPUs at roughly 13 GB VRAM. [klein] 9B uses the FLUX Non-Commercial License. For repeatable API output, Black Forest Labs distinguishes preview endpoints from fixed endpoints, so a production workflow should record which one it uses.
Midjourney V8.2
Midjourney documents V8.2 as the current default model. The release focuses on aesthetics, image quality, personalization, and a new Edit Model. It is a strong candidate when the work is visual exploration and the team benefits from Midjourney's interactive web or Discord workflow.
Version defaults change. Midjourney still supports an explicit version parameter, which matters for an approved campaign or a client handoff. Check its commercial-use guidance and current plan terms before deciding that a subscription covers a particular business use.
Reve 2.1
Reve's July 2026 release describes 2.1 as a model for dense scenes, layout planning, regional editing, native 4K output, and multilingual text. Those are provider claims, not independent scores, but they make Reve worth a focused trial when a brief has a dense hierarchy, specific spatial relationships, or text in more than one script.
Reve provides both an app and an API. Run the same layout brief through both if the choice affects a production integration, then retain the source image and edit sequence with the result.
Adobe Firefly Image Model 5
Firefly is a workflow choice as much as a model choice. Adobe documents its Firefly Image Model 5 custom-model workflow for organizations that generate images from approved brand assets. That makes it relevant for Creative Cloud teams that need a controlled path from brand material into creation and review.
“Commercially safe” is too broad to be a useful label for any image model. Check the model, account entitlement, generative-credit plan, current terms, assets supplied to the model, and the use you intend to make of the output. Adobe's documentation and contract terms, not a comparison label, govern that decision.
Recraft V4.1
Recraft separates its V4.1 family by creative intent. Its main model is the expressive option. Utility favors predictable, front-facing, simply lit scenes for mockups and product shots. The vector variant addresses logos, typography, and illustration. That split is useful when an art director wants either surprise or control and wants to test those expectations explicitly.
Recraft documents access through both Studio and its API. Test the exact variant rather than treating the V4.1 family as one model, especially for a reusable visual system.
Ideogram 4.0
Ideogram released 4.0 with open weights under a commercial license. Its published capabilities include multilingual text rendering, bounding-box layout control, 2K photoreal output, and fine-tuning for a house style. That combination makes it a notable option for design teams that need typography and layout control without treating the output as a final flat image.
“Open weights” explains the access model, not every legal or operating condition. Review Ideogram's current license and deployment terms before moving weights, fine-tunes, or prompt data into a production environment.
Stable Diffusion 3.5 Large
Stable Diffusion 3.5 Large remains useful when a team wants a self-hosted route and the surrounding Stable Diffusion tooling. Stability AI publishes a model card, local instructions for Diffusers and ComfyUI, and API options. That gives technically capable teams more control over the pipeline than a browser-only tool can provide.
The weights are gated and released under the Stability Community License. The model card states that organizations or individuals below 1 million USD in annual revenue can use the Community License for commercial work, while larger organizations need an enterprise license. Check the current license, revenue definition, and acceptable-use policy before building on it.
Costs, licenses, and commercial use
A flat monthly price does not make two image models comparable. One provider may charge for API output by image or resolution. Another may sell a subscription with usage limits. A local model shifts cost to hardware, storage, setup, maintenance, and the time needed to operate it. Credits add another layer because their output allowance can vary by model and job.
Keep the selection record beside the creative brief. It should name the provider, exact model or endpoint, plan, applicable terms or license, retrieval date, project owner, and retained inputs. That record is more useful in a review than a claim that a model is universally free, unrestricted, or “safe.”
Models and image-generator products answer different questions
Model research asks what a named model can do, how it is accessed, whether a version can be fixed, and what its terms permit. Product research asks what a platform adds around one or more models: its editor, asset library, account controls, subscriptions, integrations, support, and team workflow.
Use the AI image generator comparison when the choice is between actual products and their day-to-day experience. Use a model comparison when the constraint is model behavior, an API, local deployment, or reproducibility. The two decisions can overlap, but they are not interchangeable.
A practical model-selection test
- Build one real brief: Include the actual brand, layout, aspect ratio, required text, and reference assets. Do not use confidential material until the provider and plan are approved.
- Choose two or three candidates: Select them because their documented access and capabilities match the brief, not because they have a generic score.
- Run the same job: Note the model ID, version, endpoint, plan, prompt, inputs, settings, date, and every manual edit.
- Score the work, not the model: Review the specific requirements with the people who approve the asset. Include the cost and turnaround time of getting from first output to usable output.
- Keep a fallback: Defaults, quotas, and terms can change. A second qualified model prevents a campaign from depending on a single moving target.
The result is a defensible choice for a real visual job. It is also easier to revisit when a provider updates a default model or a license changes.
