The image generation conversation usually centers on which model produces the best output from a text prompt. Image editing is a different problem. Editing means starting from an existing image and changing something specific — swapping a background, replacing an object, fixing a face, restyling a scene, retouching a portrait. The models that lead on pure generation aren’t always the ones that lead on editing.
This is a working comparison of where each major model pulls ahead on image editing tasks, and where the remaining gaps sit.
Background replacement and scene transplantation
The category-leading task is background swapping: taking a subject from one scene and placing them in another while keeping the subject clean and the new context believable.
Flux Kontext and the newer Flux 2 family handle this well, with consistent edge handling and natural lighting integration. Nano Banana Pro is competitive for backgrounds with text or signage elements, since its text rendering preserves environmental details that other models would garble.
Seedream 4.5 produces particularly natural lighting integration when the background change involves significant color temperature shift (interior to exterior, day to night). The model adjusts the subject’s lighting cast to match the new environment automatically.
Where each tends to fail: high-contrast subjects with fine edge detail (hair, fur, lace) can show seam artifacts in any of these. The fallback for those cases is still hand-masking in Photoshop.
For a complete walkthrough of which model handles which editing task best, Pixel Dojo’s image editing model comparison guide maps tasks to models with example outputs.
Object replacement and removal
Object replacement (changing a red car to a blue one, swapping a couch in a room photo) is a different kind of editing than background work. The model needs to preserve everything else while changing one element.
Flux Kontext is the current leader on this. The model’s understanding of “what isn’t being edited stays the same” is more reliable than most alternatives. Subjects can be replaced without bleeding changes into surrounding elements.
Nano Banana Pro is strong on object removal specifically. Removing an unwanted person from a crowd, an obstructing object from a product shot, a logo from a brand-sensitive background — the model fills in the removed area more believably than older alternatives.
Seedream 5 Lite has improved object replacement for product photography contexts, where the replacement object needs to match a specific catalog style. Useful for e-commerce visualization workflows.
Style transfer
Restyling an image from one aesthetic to another (photo to oil painting, color photo to black and white, modern to vintage) is a category where the leaders shift depending on the style transformation.
For photo-to-painting transfers, Recraft V4.1 produces some of the cleanest output. The model respects the underlying composition while applying convincing brushwork and color palette shifts.
For era-shifting (modern photo to 1970s aesthetic, contemporary to film noir), Flux 2 handles the lighting and grain characteristics most accurately. The era cues read correctly without obvious overlay-style artifacts.
For anime/manga style transfer, Qwen Image 2 and Kling Image v3 produce more authentic stylization than the generally-strong photoreal models. The line work and shading conventions specific to Japanese illustration are better learned.
Face editing and portrait retouching
Facial editing (changing expression, age, lighting on a face, fixing minor issues) is the highest-stakes category because human eyes are unforgiving of identity drift.
Nano Banana Pro is the safest default. The model preserves identity better than alternatives when making small changes (slight expression shifts, lighting tweaks, color correction).
GPT Image 2 handles age-shifting (making a subject look older or younger) more believably, with the secondary changes (skin texture, hair color, posture) coordinating sensibly.
Flux Kontext is the best option for lighting changes to existing portraits. Adjusting from harsh overhead light to soft side light without regenerating the portrait works more reliably here than elsewhere.
For tight-control retouching where pixel-level accuracy matters, none of the AI options match traditional Photoshop work yet. Professional retouchers still use the AI models for first-pass setup and finish in traditional tools.
Text editing in images
Editing existing text within an image (changing the words on a sign, updating product packaging text, correcting a typo in generated output) used to be impossible without compositing. Several models now handle this.
Nano Banana Pro leads decisively on text editing. Replacing “Open” with “Closed” on a storefront, changing a product name on packaging, updating multilingual signage — all work reliably.
GPT Image 2 is competitive on English text and slightly weaker on non-Latin scripts.
Flux 2 handles text editing reasonably but tends to subtly change the typeface style when editing, which may or may not be acceptable depending on context.
Composition adjustments
Larger compositional changes (cropping that adds new image area, changing camera angle on a subject, expanding the canvas with believable new content) are uniquely challenging because they require the model to invent plausible new content.
Flux 2 handles canvas expansion (uncropping, outpainting) most cleanly. The new image area integrates naturally with the original, with consistent lighting and perspective.
Seedream 4.5 handles perspective changes (showing the same subject from a slightly different angle) better than other options, though this remains the least reliable editing task category.
Where to pick which
A simplified default by task:
Background swap or scene transplant: Flux 2 or Flux Kontext.
Object replacement or removal: Flux Kontext for replacement, Nano Banana Pro for removal.
Style transfer to painting: Recraft V4.1.
Style transfer to era: Flux 2.
Style transfer to anime: Qwen Image 2 or Kling Image v3.
Portrait retouching: Nano Banana Pro for safety, GPT Image 2 for age shifts.
Text editing in images: Nano Banana Pro.
Canvas expansion: Flux 2.
The right model for any specific job depends on what’s in the image and what’s being changed. Testing the top two options on a small sample of representative work is faster than committing to a single editor without verification.
What’s still missing
A few categories where AI image editing isn’t yet competitive with traditional tools:
Pixel-precision retouching for high-end commercial work. The AI models edit at a slightly impressionistic level; the precision-conscious editor still uses Photoshop.
Multi-image compositing with strict alignment. Stitching panoramas, aligning multiple exposures, precise focus stacking — these workflows still belong to specialized tools.
Vector-aware editing for logo and design work. The AI models edit pixels; design work that needs scalable vector output still happens in Illustrator or similar.
The category is closing gaps quickly. The next year of model releases will likely push AI editing into more of the territory that currently belongs to traditional tools. For now, the right approach is to know which model leads on which task and use the AI for the parts where it’s actually competitive.
