Best AI Image Editing Model: 6 Models Tested on the Same Photos

Disclosure: Vofy is an all-in-one AI creative studio. This comparison uses six image-editing models available through Vofy, but the prompts and scoring rules are locked before results so that no model receives an easier brief.

Cover note: The cover is an editorial visualization of side-by-side image review, not one of the retained test inputs or outputs. The source photos and all 12 submitted result slots appear in Tests 1 and 2 below; one portrait slot is excluded from scoring because its published file duplicates another model's result.

The best AI image editor is not necessarily the model that creates the most dramatic transformation. In production, a successful edit changes the requested pixels while protecting everything else: the person's face, the product label, the camera angle, the light direction, and the crop. A polished image that quietly changes unrequested details is still an editing failure.

This test uses the same two synthetic source photos and the same two prompts across GPT Image 2, Seedream 5.0 Pro, Nano Banana 2, Grok Imagine Image Quality, Qwen Image 2.0 Pro, and Wan 2.7 Image. The tasks cover wardrobe recoloring and product-background replacement. They are also practical prompt templates you can adapt to portraits, ecommerce images, and social assets.

Test status, August 7, 2026: six models were run across two locked tasks. The published Nano Banana 2 portrait asset is byte-for-byte identical to the GPT Image 2 portrait asset, so that slot is invalid and receives no score. Among the five models with two distinct retained results, Seedream 5.0 Pro has the strongest total. No model delivered a usable product result under the strict package-preservation gate.

TL;DR

• A reliable edit prompt separates what must change from what must remain protected.
• The protocol uses the same source file, exact prompt, target ratio, and one-output rule for each task; provider quality settings were not preserved in the public record.
• The two copy-ready prompts test selective wardrobe recoloring and product-background replacement.
• An output passes only when the requested edit is correct and no protected detail requires generative repair.
• Seedream 5.0 Pro scored 13/20 across two valid results and produced the only usable portrait; Nano Banana 2 has no overall rank because its published portrait file is duplicated, and every product result failed the package-preservation gate.

1. The Decision You Are Actually Making

Choosing the best AI image editor means deciding which type of error costs you the most. A portrait creator may tolerate a slightly different jacket texture but cannot accept identity drift. An ecommerce team may accept a small shadow change but cannot ship a bottle whose cap, label, or printed copy has been redesigned.

That is why this comparison does not ask each model to make a different showcase edit. Every model faces two assignments with explicit protected regions and observable pass checks. The edits are intentionally narrow enough to score but broad enough to resemble real work. Together, they reveal three distinct abilities: semantic selection of the intended object, preservation outside that object, and environmental integration when the background changes.

The headline question can therefore be stated in one sentence: the best AI image editor is the model that completes the requested change while causing the least unrequested change elsewhere in the image. Visual appeal matters after that preservation gate, not before it. If an output changes the face, label, furniture layout, or crop, a creator has to repair or rerun it even when the new color grade looks impressive.

2. The Six Models and Two Source Photos

The model set matches Vofy's broader six-model image generator comparison, which makes this a focused model-editing companion rather than a comparison of complete editor products. It evaluates first-result image behavior, not interface features such as masking, undo history, manual selection, multi-turn repair, speed, or price. Availability and behavior can change after model or provider updates.

Model	Why it belongs in the editing test	Current Vofy entry
GPT Image 2	General image generation and instruction-based editing	Open GPT Image 2
Seedream 5.0 Pro	Quality-focused generation and editing for design work	Open Seedream 5.0 Pro
Nano Banana 2	General image generation and editing with reference input	Open Nano Banana 2
Grok Imagine Image Quality	Quality-oriented generation and image editing	Open Grok Imagine Image Quality
Qwen Image 2.0 Pro	Image generation and editing for design-heavy workflows	Open Qwen Image 2.0 Pro
Wan 2.7 Image	Image generation and reference-guided editing	Open Wan 2.7 Image

The two source photos are synthetic and created specifically for this protocol. The portrait uses a fictional adult rather than a real person's likeness, while the product uses a fictional MERA serum bottle. This keeps the files publishable while giving the edits stable details that can be checked pixel by pixel.

Each source also isolates a different production risk. The portrait contains identity, hands, fabric, a notebook, and window light. The product contains exact bottle geometry, short label copy, contact shadow, and reflective material behavior. Neither source photo should be altered, retouched, or resized differently between models.

For vendor context, see the official OpenAI image guide, Seedream 5.0 Pro launch post, Gemini image generation documentation, xAI image documentation, Qwen-Image 2.0 announcement, and Wan2.7-Image announcement. Those pages describe intended capabilities; only retained outputs from this locked test can determine the article's scores.

3. How We Keep the Comparison Fair

The test protocol calls for one source image and the exact same edit prompt for every model in a task. It also calls for the source aspect ratio, one requested output, and retention of the first terminal result, without model-specific rewrites, hidden negative prompts, masks, selected seeds, second attempts, or best-of grids. Because the public record lacks request-level logs, these controls describe the stated procedure rather than independently verifiable run metadata.

Native output dimensions differ because the six routes do not expose one universal resolution system. The portrait source is 1776 x 2368; the retained portrait files, in model order, are 1152 x 1536, 1776 x 2368, duplicated 1152 x 1536, 864 x 1152, 768 x 1024, and 768 x 1024. The product source is 2048 x 2048; the product outputs are 1024 x 1024, 2048 x 2048, then four 1024 x 1024 files. All preserve the requested ratio. Dimension changes are not automatic failures, but unintended reframing lowers scene-preservation scores.

The public article does not include provider request IDs, exact UTC timestamps, route snapshots, or visible quality-setting records. That prevents a fully independent replay of the run, so the results should be read as a visual audit of the published files rather than a laboratory-grade benchmark. A future rerun should publish those fields alongside file checksums.

3.1 The Change-Preserve-Deliver prompt framework

A dependable editing prompt has three blocks. Change identifies one object or region and describes the new state. Preserve names the protected details that must not move or mutate. Deliver defines crop, realism, output count, and forbidden additions. This order matters because vague requests such as “make the jacket blue and improve the photo” invite the model to reinterpret lighting, skin, styling, and composition.

Use this reusable structure:

Edit the provided image. Change only [target object or region] from [current state] to [new state with material, color, geometry, and lighting behavior]. Preserve [identity or product geometry], [pose and placement], [all text], [background], [lighting], and [camera crop] exactly. Do not add, remove, restyle, retouch, or improve anything else. Return one photorealistic finished image at the source aspect ratio with no border, watermark, caption, or comparison layout.

The framework is deliberately repetitive. In image editing, repetition can define a boundary: “change only” establishes the target, while the preservation list makes the non-target content explicit. A short prompt can work when the edit is trivial, but high-value images benefit from naming the details that would make the output unusable if they drifted.

3.2 The scoring rubric

Each valid output receives zero, one, or two points in five categories. A score of ten is a clean first-pass edit; a result can still look attractive with a lower score, but it needs repair or a rerun. Invalid or duplicated evidence is marked N/A rather than treated as a model failure. The scorecard publishes the component scores and decisive defects instead of hiding a protected-detail failure inside an average.

Dimension	0 points	1 point	2 points
Requested change	Missing or materially wrong	Recognizable but incomplete	Correct target, color, material, or removal
Protected subject	Identity or product geometry changes	Minor visible drift	Protected subject remains stable
Scene preservation	Unrequested objects or layout change	Small localized drift	Non-target scene remains materially unchanged
Reconstruction and edges	Obvious seams, smears, or geometry errors	Minor edge defect	Natural edges, texture, shadow, and occlusion
Delivery readiness	Needs content-aware or generative repair	Content is intact but needs non-content cleanup such as border trimming or color-profile normalization	Publishable after format conversion or compression only

The hard usability gate is stricter than the numeric total. A result is unusable when comparison at full size shows material identity drift, misspelled product copy, altered package geometry, an added prop, a watermark, or any defect that requires content-aware or generative repair. Ordinary file conversion, proportional compression, metadata cleanup, color-profile normalization, and trimming a uniform added border are allowed. Cropping image content, local retouching, compositing, text replacement, and generative fill are not.

4. Test 1: Change Clothing Without Changing the Person

The first source is a vertical editorial portrait of a fictional adult wearing a rust-red linen blazer over a cream shirt. She holds a cream notebook with both hands in a bright studio, which creates several preservation traps: face and hair identity, ten plausible fingers, notebook geometry, lapels, shirt, window shadows, and the original crop. The assignment changes only the blazer's color and material.

Source aspect ratio: 3:4
Output aspect ratio: 3:4
Source rule: use the same unedited source file for all six models

Copy-ready portrait editing prompt

Edit the provided portrait. Change only the rust-red linen blazer to a deep cobalt-blue fine-wool blazer. Keep the blazer's existing cut, lapels, seams, buttons, sleeve length, folds, fit, and exact position; the new wool should respond naturally to the existing window light. Preserve the subject's facial identity, age, skin texture, expression, hairstyle, body proportions, pose, hands and fingers, cream shirt, cream notebook, background, shadows, color balance, depth of field, camera angle, and 3:4 crop exactly. Do not retouch the face or skin, change makeup, add accessories, alter the notebook, redesign the outfit, or modify anything outside the blazer. Return one photorealistic finished portrait with no text, border, watermark, or before-and-after layout.

This prompt separates appearance from construction. “Deep cobalt-blue fine wool” defines the desired change, while the lapels, seams, buttons, folds, fit, and position remain protected. Without those construction constraints, a model can satisfy the color request by replacing the entire garment. That may be acceptable for outfit exploration, but it does not demonstrate selective image editing.

The strongest result should look as if the same garment were manufactured in a different fabric and photographed in the same frame. Reviewers should compare facial landmarks, hair silhouette, finger count, notebook edges, blazer outline, and window-shadow boundaries. The edit fails the hard gate if the model beautifies the face, changes the shirt, moves a hand, or invents a new garment design.

The six images below are presented in the same order as the scorecard. Each caption identifies the most important visible preservation result; it is an audit note, not a second prompt or a post-edited correction.

5. Test 2: Replace a Product Background Without Redesigning the Product

The second source is a square studio packshot of a fictional pale-gray ceramic pump bottle. Its label contains two short lines, MERA and BARRIER SERUM, so preservation can be checked without relying on a real brand. The original background is warm light gray. The edit moves the bottle onto wet black volcanic stone at sunrise while protecting the package and its frontal camera view.

Source aspect ratio: 1:1
Output aspect ratio: 1:1
Source rule: use the same unedited source file for all six models

Copy-ready product editing prompt

Edit the provided product photo. Replace only the warm light-gray studio surface and background with one continuous wet black volcanic-stone surface at sunrise. Add a restrained warm rim light from camera left and a physically plausible soft reflection directly beneath the bottle. Preserve the exact MERA bottle silhouette, pale-gray ceramic material, pump shape, cap, proportions, front-facing angle, placement, scale, sharpness, and label. Keep the label text exactly MERA on the first line and BARRIER SERUM on the second line, with no other text. Do not redesign, recolor, rotate, resize, relight internally, or duplicate the bottle. Do not add plants, flowers, fruit, hands, droplets on the bottle, packaging boxes, logos, or decorative props. Return one finished photorealistic square ecommerce image with no border, watermark, caption, or comparison layout.

Here, “do not relight internally” means do not replace the bottle's base ceramic color, highlight structure, or material response. The requested warm rim light and contact reflection may affect the bottle only as physically plausible light from the new environment. This boundary is visually judged rather than pixel-masked, which adds subjectivity to the product score and is a limitation of the prompt.

Background replacement is harder than it appears because the bottle must inherit the new environment without being rebuilt. A correct output changes the contact shadow, reflected light, and surface reflection so the object belongs in the scene. At the same time, the package geometry, pump mechanism, label placement, and typography should remain stable. A pasted-looking bottle fails reconstruction; a beautifully integrated but redesigned bottle fails preservation.

Reviewers should inspect the two label lines character by character, then compare the pump, shoulder curve, ceramic tone, label rectangle, and bottle-to-frame scale. A weak model may add sunrise highlights by recoloring the entire bottle or may interpret “volcanic” as permission to add smoke and lava. The exclusions keep the scene commercially restrained and make the requested edit observable.

All six results make the requested setting recognizable and keep both label lines readable. The captions therefore focus on the decisive failure in this task: each model rebuilds some combination of the pump, bottle silhouette, label proportions, material, scale, or placement instead of changing only the background.

6. How to Adapt These AI Image Editing Prompts

The two master prompts are longer than a casual chat instruction because they double as production specifications. You can shorten them once you know which details your chosen model preserves reliably. Start by keeping the same three-block order: state the one edit, list protected details in descending importance, and finish with delivery constraints.

6.1 Name the visual boundary, not just the object

“Make the blazer blue” identifies a category but not a boundary. “Change only the rust-red linen blazer” tells the editor that the shirt, hands, notebook, and face are outside the edit. For removal, list all connected parts such as a cord, reflection, or cast shadow. For background replacement, clarify whether the foreground surface, contact shadow, and object reflections should change with the background.

If the object has ambiguous edges, describe its location and neighbors. For example, “the rust-red blazer worn over the cream shirt” is more reliable than “the jacket.” When several similar objects exist, use spatial language, color, shape, and adjacency together. Avoid relying on numbered references unless the interface preserves a stable reference order and your prompt clearly assigns each file one role.

6.2 Protect expensive details first

Not every pixel has equal production value. Put identity, product geometry, exact copy, and required composition ahead of secondary color grading. If the edit involves a person, protect face, age, skin texture, hairstyle, pose, hands, and body proportions before the background. If it involves a product, protect silhouette, cap, label, printed text, view angle, scale, and placement before the surface styling.

The preservation list should remain specific but not contradictory. Asking a model to replace the time of day while preserving every shadow exactly creates an impossible brief. Decide which effects must logically update. In the product prompt, the bottle geometry and label stay fixed, but contact shadow, surface reflection, and restrained rim light are allowed to respond to the sunrise environment.

6.3 Remove subjective improvement language

Words such as “better,” “cleaner,” “more professional,” or “enhance” give the editor permission to make broad choices. Replace them with visible instructions such as “change the blazer material to fine wool” or “replace the background with wet black volcanic stone.” When the goal is controlled editing, an objective target is easier to reproduce and score.

If a first output fails, do not send an unfocused repair such as “try again but preserve everything.” Name the defect and restate the protected baseline. For example: “Restore the original face, hands, cream shirt, and notebook from the input image. Keep the cobalt blazer edit, but make no other changes.” Treat that as a second-stage workflow result, not as the first output in a benchmark.

7. Results Scorecard and How to Read It

These scores use the published files shown beside the two prompts and the rubric above. Change, Subject, Scene, Edges, and Ready correspond to the five rubric dimensions in that order. The usability column applies the stricter hard gate, so a visually attractive result can earn points while still failing direct use.

7.1 Portrait component scores

Model	Change /2	Subject /2	Scene /2	Edges /2	Ready /2	Portrait /10
GPT Image 2	2	0	1	2	0	5
Seedream 5.0 Pro	2	2	2	1	2	9
Nano Banana 2	N/A	N/A	N/A	N/A	N/A	N/A
Grok Imagine Image Quality	2	0	0	2	0	4
Qwen Image 2.0 Pro	2	0	0	2	0	4
Wan 2.7 Image	2	0	0	2	0	4

7.2 Product component scores

Model	Change /2	Subject /2	Scene /2	Edges /2	Ready /2	Product /10
GPT Image 2	2	0	1	2	0	5
Seedream 5.0 Pro	2	0	1	1	0	4
Nano Banana 2	2	0	1	1	0	4
Grok Imagine Image Quality	2	0	1	2	0	5
Qwen Image 2.0 Pro	2	0	1	1	0	4
Wan 2.7 Image	2	0	1	1	0	4

7.3 Valid-results summary

Model	Portrait recolor /10	Product background /10	Total /20	Usable valid outputs
GPT Image 2	5	5	10	0
Seedream 5.0 Pro	9	4	13	1
Nano Banana 2	N/A	4	N/A	0/1 scored
Grok Imagine Image Quality	4	5	9	0
Qwen Image 2.0 Pro	4	4	8	0
Wan 2.7 Image	4	4	8	0

In the valid portrait evidence, Seedream 5.0 Pro was the clear leader. It preserved the source identity, pose, crop, notebook, and studio scene most closely while making the requested fabric and color change. Its small blazer-construction drift kept it from a perfect score, but the image remained directly usable. GPT Image 2 altered facial detail, framing, garment construction, and notebook placement enough to fail the identity gate. Grok, Qwen, and Wan introduced still more visible identity and composition drift. Nano Banana 2 cannot be compared on this task until a distinct retained result is published.

The product test produced a different lesson. All six models created convincing volcanic-stone sunrise scenes and retained readable MERA and BARRIER SERUM text, but every result changed protected packaging details. Bottle scale, shoulder shape, pump geometry, label proportions, surface material, or placement shifted in each output. GPT Image 2 and Grok earned slightly higher numeric scores for environmental integration and overall presentation, but neither passed the hard gate because a production packshot cannot silently become a redesigned package.

Among the five models with two valid published results, Seedream 5.0 Pro has the highest total. This is not a complete six-model ranking because Nano Banana 2 lacks valid portrait evidence, and it is not a comparison of full editor products. Seedream's 1/2 usable count still shows why totals and hard-gate results belong together: attractive reconstruction can raise a score, but it cannot cancel identity or package drift.

Two prompts are a focused production screen, not a universal benchmark. These results should not be generalized to every style, language, resolution, API route, or future model version.

8. Running the Same Edit in Vofy

Open the Vofy AI Image Generator, choose the first model, upload the relevant source photo, preserve the source aspect ratio, and paste the exact task prompt. Request one output and save the original file before moving to the next model. Repeat without rewriting the prompt. Credits vary by model and resolution, so record the visible rate at run time rather than hard-coding a comparison into the article.

For a reproducible benchmark, record the model name and route, UTC time, source-file checksum, exact prompt, selected ratio, visible quality or resolution option, request ID, exported dimensions, and output checksum. This article's public record does not contain all of those fields, which is why its claims are limited to the visible files. If a future run returns a network failure, retry the same payload and log it; if the model returns an image, refusal, or safety result, retain it rather than rerolling.

For everyday creative work, you do not need this level of documentation. The more practical method is to start with one precise prompt, inspect protected details at full size, and use a second targeted repair only when the first edit is close. Our GPT Image 2 editing guide covers one model in more depth, while the multi-reference image generation test explains how to assign separate roles to several input images.

9. Limits of This Test

This protocol does not cover object removal, text replacement, face swapping, outpainting, multi-turn editing, style transfer, or several simultaneous edits. Those workflows have different failure modes. A model that ranks well here may struggle with a poster date change or a reconstruction-heavy cleanup task while remaining useful for controlled recoloring and product scenes.

The source images are synthetic, which removes consent and trademark complications but does not represent every phone camera, compression artifact, skin tone, product material, or room layout. Native dimensions and provider defaults can also differ even when the closest shared quality class is selected. The test controls what can reasonably be controlled and records the rest rather than pretending all routes are identical.

Most importantly, the prompt is held constant to test first behavior. That gives no model the benefit of custom syntax or vendor-specific optimization. A separate workflow comparison could optimize prompts per model and count the time or number of attempts required to reach a usable edit. It should be published as a different test, not merged into this fixed-prompt scorecard.

10. Conclusion

A strong AI image editor behaves less like a generator and more like a careful production assistant: it changes the requested region, updates the lighting or reconstruction that logically belongs to that change, and leaves protected content alone. The Change-Preserve-Deliver framework makes that expectation explicit before the model sees the photo.

Seedream 5.0 Pro earned the strongest total among the five models with two valid published results and produced the only directly usable portrait under the hard gate. Nano Banana 2 remains unranked overall until its duplicated portrait slot is replaced with distinct evidence. The product test had no usable winner: all six outputs integrated the new environment attractively but changed protected package geometry or scale. Do not confuse a more polished transformation with a more precise edit.

FAQ

Related Reading

• Best AI Image Generators: The Core 6-Model Test
• Multi-Reference Image Generation Test
• How to Edit Images with GPT Image 2