Best AI image-editing models in 2026

FLUX.1 Kontext [pro] is the best all-around AI image-editing model in 2026: it holds a subject's identity across 5-plus sequential edits, accepts an optional mask, and costs $0.02/image on Infer, the cheapest of the four models here. GPT Image 1.5 is the runner-up when an edit needs to composite something that was never in the original photo, at $0.04/image. Nano Banana 2 is the fast, cheap option for lighter touch-ups, and SAM 3.1 isn't an editor at all, it's the segmentation step that feeds a mask into whichever editor runs next. Match the job to the model below; none of these four does every editing task equally well.

Ranked comparison table

For multi-step edit chains: FLUX.1 Kontext [pro]

The job this model owns: a product photo that needs several edits in sequence, not one, without the subject drifting a little further from the original with each pass. Infer's own listing for FLUX.1 Kontext [pro] states it maintains identity across "5+ edits" in a chain, and Black Forest Labs' product page backs the mechanism up directly: the model is built to "precisely preserve identity (of e.g. a reference character or object) across multiple scenes and environments." That's the documented use case behind Infer's own list of applications for the model: e-commerce background swaps, virtual try-on, photo restoration, brand-asset variations, and style transfer, all tasks that assume a real starting photo and an edit instruction, not a blank canvas.

Local edits are the other half of the story. Black Forest Labs describes the model's targeted-modification mode as enabling "targeted modifications of specific elements in an image without affecting the rest," which is the mechanism behind a background swap that doesn't also relight the product itself. Kontext also takes an optional mask on Infer, and it's rate-limited to 60 requests/minute by default (burst to 120), with safety filtering specifically on identity-altering edits and explicit content.

The honest catch, and the reason it isn't the only model on this list: Kontext can't invent a product from a text description the way GPT Image 1.5 or a pure generator can. It needs a real photo to start from. It's also the one model here with a pricing wrinkle worth flagging before you budget a job around it: Infer's own page shows $0.02/image in the primary pricing block but references "$0.04/img" elsewhere on the same page, unreconciled as of this writing. Confirm the live rate in the console before quoting a client. FLUX.1 Kontext [pro] is live on Infer — try it →

For editing plus new elements: GPT Image 1.5

The job: an edit that isn't just "change the background," it's "add a person holding this product" or "put this bottle on a marble counter next to a towel that was never in frame." GPT Image 1.5 is Infer's #2 overall image model (1271 Elo), and its documented use cases include product staging and complex multi-subject composition, up to 8-plus elements without objects dropping out.

It accepts a mask, too, a transparent-alpha region passed to OpenAI's edit endpoint, but OpenAI's own documentation is specific about the limit: "masking with GPT Image is entirely prompt-based. The model uses the mask as guidance, but may not follow its exact shape with complete precision." That's the one honest weakness worth planning around: if a client needs the mask boundary respected pixel-for-pixel, Kontext's more literal local-edit behavior is the safer bet.

At $0.04/image it's double Kontext's rate and, at roughly 5 seconds average latency, the slowest model on this list against Nano Banana 2's 1.4 seconds. OpenAI's safety filters (CSAM detection, public-figure restrictions, NSFW) also apply, and can flag a legitimate edit involving a real person's face. Try GPT Image 1.5 in the Infer playground →

For fast, cheap touch-ups: Nano Banana 2

The job: a quick background cleanup or color tweak on a batch of images where turnaround matters more than editing precision. Nano Banana 2 is the fastest model on this list, a 1.4-second median latency against GPT Image 1.5's roughly 5 seconds, and it's the cheapest full generation-and-editing model here that isn't Kontext, at $0.039/image.

The number worth sitting with is the gap between its two documented Elo scores: 1262 on Infer's general Arena ranking against 1065 specifically on editing. Artificial Analysis, the leaderboard Infer cites, runs its editing arena as a distinct benchmark from text-to-image generation, having "users compare edited outputs from the same input image and editing instruction, choosing the result they prefer." A model can rank well as a generator and still trail as an editor, and that's exactly the pattern Nano Banana 2's own two numbers show: it's a stronger generator than an in-place editor. For a fine, targeted edit on an existing photo where identity has to hold exactly, Kontext is the better tool; Nano Banana 2 is the one for volume and speed, holding up to 5 objects consistent across a scene. Nano Banana 2 is live on Infer — try it →

For isolating objects before anything else runs: SAM 3.1

SAM 3.1 doesn't edit an image, it prepares one for editing. Its Object Multiplex capability, per Meta's own November 2025 update, lets the model "track up to 16 objects in a single forward pass" instead of one API call per object , and the same update doubled video throughput on a single H100 GPU from 16 to 32 frames per second. That's the concrete reason it's the volume pick for isolating products, subjects, or scientific-imagery elements: a 16-item flat-lay is one call instead of sixteen.

It takes three input modes, a click point, a bounding box, or a coarse mask, and it's Apache 2.0 licensed, open-weights, and self-hostable, unlike any other model on this list. The catch is real: text prompting is listed as roadmap, not shipped, so you can't yet tell it "the red bottle" and get a mask back; you point, box, or sketch first. And its own Infer page carries an unreconciled pricing split, $0.002/image against a separate $0.01/image "standard rate", confirm which applies before costing a job. Run SAM 3.1 on Infer →

Instruction editing vs mask editing: two different jobs

The four models on this list split into two families, and confusing them is the fastest way to pick the wrong tool. FLUX.1 Kontext [pro] and GPT Image 1.5 are instruction-based editors: you describe the change in a sentence, "swap the background to a marble counter," "add a shadow under the bottle," and the model decides which pixels to touch. SAM 3.1 is a segmentation model, not an editor at all; it draws the selection, a precise mask around one object or several, and hands that mask to something else to fill in or replace.

Instruction editing is the right call when the change is describable but the exact boundary doesn't need to be pixel-perfect. Black Forest Labs positions Kontext around exactly this: a single text instruction that can "precisely preserve identity" of the subject while everything around it changes , which is why it's Infer's pick for background swaps and virtual try-on, tasks where the model has to infer the edit region from context rather than a supplied shape. GPT Image 1.5 works the same way for compositing tasks, adding a person or object that was never in the source photo, and OpenAI's own documentation is explicit that even its optional mask input doesn't change this: masking is "entirely prompt-based," and the model "may not follow its exact shape with complete precision" . Both models are guessing at the edit region from language, and that's a feature when the brief is conceptual and a limitation when the brief is a hard boundary.

Mask and segmentation workflows exist for the opposite case: the boundary itself is the whole point. That's the job Meta built SAM 3.1 for, generating a precise selection from a click, a box, or a coarse mask so an inpainting model can fill or replace exactly what's inside that boundary and nothing outside it . A typical pipeline runs SAM 3.1 first to isolate the product, garment, or subject at pixel precision, then passes that mask into an editor like Kontext or GPT Image 1.5 for the actual fill or swap, rather than trusting either editor's own prompt-based region guess. That two-step pattern, segment then edit, is slower and costs an extra API call, but it's the only reliable way to guarantee the edit stops exactly at an object's silhouette, something neither instruction-based editor's mask support promises on its own.

How we ranked

This list is ordered by how directly each model's documented capability matches "editing an existing image" rather than a single leaderboard score, since two of the four (Kontext, SAM 3.1) don't carry a published Elo at all on Infer's site. Where a rank exists, it's Infer's own model-page figure, observed 2026-07-22; Infer's leaderboard page itself only server-renders the video-generation tab, so the image and editing tabs are cited from individual model pages, not the leaderboard table directly. Infer's margin on all four models is identical, so there's no financial reason to steer a reader toward one over another here: it follows documented identity-retention behavior, mask support, and price, in that order.

Which model for which job

• A product photo needs three or four sequential edits without the subject drifting → FLUX.1 Kontext [pro].
• An edit needs to add something that was never in the original photo → GPT Image 1.5.
• A batch of images needs a fast, cheap background or color touch-up → Nano Banana 2.
• A busy scene needs individual subjects isolated before any editing starts → SAM 3.1.
• The mask boundary has to be respected exactly, not just as guidance → FLUX.1 Kontext [pro] over GPT Image 1.5, per OpenAI's own precision caveat above.

What didn't make the list

Seedream 4.0 generates strong multi-reference and bilingual-typography images (#6 overall image, 1198 Elo), but its documented strengths are generation-side, fashion editorial, character design, concept boards, not editing an existing photo. It's covered instead in the best text-to-image models ranking.

Imagen 4 Standard is Google's photorealism specialist for product shots and stock-photo replacement, a generation task, and its full page was returning a server error at the time of this research, leaving its editing capabilities (if any) unconfirmed either way.

FLUX 1.1 [pro] supports image-to-image via an init_image and strength parameter, which is closer to restyling a photo than editing one in place, and Infer's own page doesn't claim identity retention across a multi-step chain the way Kontext's page does. It's the pick for high-volume generation, not targeted edits; see the product photography ranking for where it fits.

All four of these models run on one Infer API key. Run FLUX.1 Kontext [pro] on Infer →

Related reading

• Best text-to-image models in 2026
• Best AI models for product photography
• GPT Image 1.5 vs Nano Banana 2
• Best image-to-video models in 2026
• Every best-of ranking