DEV Community: alex steve

5 AI Image Models for Developers: Choose by the Asset You Need to Ship

alex steve — Thu, 29 Oct 2026 06:07:44 +0000

GPT Image 2, Nano Banana 2.1, FLUX 3 Image, Midjourney V8.2, and Seedream 5.0 Pro offer different ways to create and revise visuals. For developers making landing-page illustrations, onboarding images, or help graphics, the choice starts with what the asset must preserve. Selecting an API for a user-facing generation feature requires a separate integration evaluation.

A polished image can still be wrong for an application. The subject may sit behind the headline. Three onboarding illustrations may look unrelated. A diagram may contain an arrow that describes a transition your system never makes.

A rough draft gives you something concrete to revise. In Flux3, you can describe an illustration to generate a starting composition, or upload an existing image and request a change. Keep the brief and original image: they will help you compare candidates on the same assignment instead of judging unrelated showcase images.

Define the asset before choosing the model

Suppose you are building a task-management app. You need a landing-page illustration, three onboarding images, and a graphic explaining how a task moves through the system.

The landing-page image needs room for a headline. The onboarding set needs a consistent mascot and style. The explanatory graphic needs the right labels and relationships. These requirements lead to overlapping shortlists:

Your hardest requirement	Candidates to compare	What narrows the choice
A hero illustration with reserved space for copy	GPT Image 2 and FLUX 3 Image	Start with a written composition brief. If you need to specify object regions explicitly, investigate FLUX's bounding-box workflow and whether your interface exposes it.
A consistent set built from approved references	Nano Banana 2.1, FLUX 3 Image, and Midjourney's Edit Model	Check whether the workflow accepts the references you actually need, then compare subject preservation across the full set. Input capacity alone does not establish consistency.
A visual style you have not settled on	Midjourney V8.2 alongside a candidate already on your shortlist	Personalization is a reason to investigate Midjourney. Compare treatments using the same subject and layout; other models can explore styles too.
An explanatory graphic with fixed labels and relationships	Seedream 5.0 Pro and GPT Image 2	Compare the finished diagram against your source. Sketch-led or spatially annotated revisions are a reason to investigate Seedream; neither model's text capabilities establish factual correctness.

If you need both a consistent mascot and precise placement, look for a workflow that accepts your reference and exposes the placement control you need. FLUX 3 Image supports both multi-reference input and bounding boxes, so it belongs on that shortlist. You still need to check the resulting mascot and layout together. The sections below explain reasons to investigate each model, rather than rank their output quality.

1. GPT Image 2: A detailed brief with several constraints

GPT Image 2 combines generation and editing with high-fidelity image inputs. OpenAI's guidance also covers text rendering and structured visuals, making it a candidate for both the hero illustration and the explanatory graphic.

For the hero, imagine three work cards arranged toward one completed stack. The cards should remain on the right, with quiet space on the left for a headline. A written brief can describe the objects, their relationship, palette, and empty region in one request.

If the scene is right but crowds the headline area, revise that existing image and check whether the edit preserves the cards and palette. This tests whether a text-led workflow gives you enough control for the layout. If regional placement remains the blocking requirement, compare it with the explicit box controls described in the FLUX section.

For a diagram, the test changes: preserving all requested objects is not enough if an arrow connects the wrong states. Use the diagram assignment below rather than choosing from a successful hero illustration.

2. Nano Banana 2.1: A workflow built around reference images

Nano Banana 2.1 supports conversational editing and up to 14 reference images, with output options at 1K, 2K, and 4K. Its reference workflow is relevant when you already have approved visual material.

For the onboarding set, supply an approved mascot, a palette reference, and a sample illustration. Give each input a role: preserve the mascot's shape, use the palette for color, and borrow the sample's rendering style. Then request separate scenes using that same reference set.

The 14-image allowance is useful only if those inputs resolve distinct requirements. With three references, capacity does not distinguish it from Midjourney's four-reference Edit Model. If your assignment genuinely needs more than four separate references in one request, that limit becomes a practical selection condition. It is not evidence that one model preserves details better.

Judge the three onboarding images together. A correct mascot in one image does not establish consistency across a set; changing proportions or line weight may require revisions even when each image looks good alone.

3. FLUX 3 Image: Composition you can specify explicitly

FLUX 3 Image offers bounding-box controls for placing elements and targeting edits, alongside written instructions and multiple reference images. Those controls matter when your design already says where things belong.

Suppose the hero reserves its left side for copy and its right side for the illustration. For a first draft, describe that arrangement in Flux3 and choose the aspect ratio used by your layout. If you already have an illustration, upload it and request a specific change, such as moving the stack right while preserving the cards and colors.

That browser workflow uses text and uploaded images. If the task requires explicit source and destination boxes, use an interface or integration that exposes the model's bounding-box controls. A prompt field and aspect-ratio selector alone do not provide that control.

A bounding box lets you name a region more explicitly than “slightly farther right.” It does not remove the need to inspect the surrounding image: Black Forest Labs notes that nearby shadows, reflections, or lighting may change after an edit. Compare those areas as well as the moved object.

4. Midjourney V8.2: A visual direction you still need to discover

Midjourney V8.2 focuses on aesthetics, image quality, and Personalization. Investigate Personalization when exploring a visual direction around your preferences, then judge whether that treatment fits the rest of the product.

For the task-management app, compare a paper-cut illustration, a restrained 3D scene, and a hand-drawn treatment. Keep the subject and intended placement stable. Otherwise, a clearer composition can make one treatment look preferable without telling you much about the style itself.

V8.2 also works with the Edit Model, which supports written changes, up to four reference images, and targeted editing or canvas expansion through the Editor. Include a revision in your trial: ask the chosen treatment to accommodate a changed object position without losing its look.

If the visual direction is already approved, style exploration is no longer your main selection criterion. Compare reference preservation and editing instead, using the same assets you would give Nano Banana or FLUX.

5. Seedream 5.0 Pro: A graphic that has to explain something

Seedream 5.0 Pro targets text-rich graphics and supports editing guided by spatial annotations and sketches. ByteDance also describes layer separation as part of its capabilities.

For the task-management graphic, a rough diagram can establish the order and grouping before you request a visual treatment. If a connection is wrong, marking the offending arrow on the source is a concrete revision method to investigate. Test whether those annotations make your corrections easier to specify and whether the result follows them.

Compare it with GPT Image 2 on the same labels, states, and connections. The technical meaning comes from your application: a polished extra arrow can make the graphic wrong, even if all the words are readable.

Choose the final format before investing in revisions. A static illustration may suit a help article, while a frequently changing process diagram may be easier to maintain as SVG or diagram source. Layer separation does not establish that the generated output contains editable text, semantic connections, or the application structure you need.

Compare the work left after generation

Choose the assignment that matches your asset, then give it to two plausible candidates. Keep the source images, required content, and target placement fixed. Adapt syntax and supported controls to each workflow; you are evaluating the way you would actually use it.

Hero illustration: composition and revision

Use this shared brief:

Create a wide illustration for a task-management app. Show three simple work cards arranged toward one completed stack. Keep the objects on the right, with an uncluttered area on the left for a headline added later. Use navy, cream, and one orange accent. Do not include text or logos.

Then request the same change: move the stack farther right while preserving the three cards and palette. Check that the headline area stays clear and that the revision does not replace details you wanted to keep.

If style is the open question, repeat this composition in the three treatments described above. Compare each at the same display size. Keep the actual headline and buttons in HTML so they remain editable.

Onboarding set: consistency across three scenes

Attach the same approved mascot, palette, and style reference to each candidate. Request three separate images: the mascot creating a task, assigning it to a teammate, and marking it complete. Keep the mascot's proportions, clothing, colors, and rendering style consistent.

Place the three results side by side inside your onboarding cards. Then change the pose in the second image while asking to preserve its established appearance. Compare both the original set and the revised set. This tests whether an individual correction creates new inconsistency elsewhere in the visual series.

Explanatory graphic: labels and relationships

Supply a simple source diagram with three nodes labeled “Queued,” “Assigned,” and “Complete.” Require exactly two directed arrows: Queued to Assigned, and Assigned to Complete. Ask for a navy-and-cream treatment without adding nodes or connections.

Check spelling, arrow direction, and legibility at the help article's display size. Then ask to change “Assigned” to “In progress” while preserving the other labels and both connections. If you want to evaluate annotation controls, mark that same node on the source rather than changing the requested result.

Record what it takes to reach an acceptable asset

Generate the same small number of candidates in each workflow and include the same revision task. Avoid choosing one fortunate output while ignoring the others. Record:

Whether the initial asset met its required content, layout, or consistency conditions.
Whether the revision preserved the parts that were already correct.
The attempts and manual corrections needed to obtain an acceptable asset.
Whether that asset still works inside its actual page or component, including its crop and display size.

Save the model version, settings, prompt, and references beside each result. Separate generation time from correction time. For cost, divide the total spent on the trial, including unsuccessful attempts, by the number of accepted assets. If none meet the requirements, record that outcome rather than treating a cheap generation as a usable result.

Start with the hardest requirement

For a layout problem, compare text-led revision with explicit placement controls. For a visual series, compare the full set and one revised member. For an explanatory graphic, compare correctness before polish. If style is undecided, hold the composition fixed while exploring treatments.

Choose the workflow that meets your non-negotiable requirements with an acceptable amount of correction. If both candidates fail the same requirement, reconsider the input or the asset format before testing three more models. Keep the accepted result and its revision record as a baseline when your project or model version changes.

Topview T1: What It Is and How It Compares to Seedance 2.5

alex steve — Sun, 25 Oct 2026 01:43:00 +0000

Topview T1 is a video generation model introduced by Topview. It lets you build a scene from a written description or use an existing image as the starting point for motion.

If you are exploring Topview T1, start with a practical question: what does your next shot need to do? A short product clip, a character interaction, and an edit to an existing video can require very different controls.

That question also makes the comparison with Seedance 2.5 more useful. Check whether each service offers the inputs and editing options your shot needs, then compare the results and cost under matching conditions.

The essentials are straightforward:

Start with an image when you already have the subject and composition.
Start with text when you need to describe the scene from scratch.
Compare available controls before committing to a model.
Count the cost of retries and review the whole video before choosing a result.

What Is Topview T1?

Topview describes T1 as a model for video production and cinematic storytelling. Its official page lists 720p and 1080p output, generation up to 30 seconds, and support for image and audio references.

For a beginner, the most useful distinction is between two starting points. Text to Video asks you to describe the subject, setting, action, and camera. Image to Video starts with a visual you supply, so your instructions can focus on what should change.

Consider a photograph of a desk lamp. You already know what the lamp looks like and where it sits. Instead of describing its shape again, you could ask for a slow camera move while the lamp remains still. Without a photograph, you would also need to describe the lamp, desk, lighting, and background.

The model and the website you use are separate parts of the workflow. The walkthrough below uses topview-t1.net, an independently operated website rather than Topview's official site. Its selectable options, account access, and prices should be checked in its own interface.

How to Use Topview T1 for Your First Video

A useful first task is a short presentation shot of one object. For this example, imagine a desk lamp on a plain wooden surface, with the camera moving gently closer. The brief gives you two things to evaluate: whether the lamp keeps its shape and whether the camera follows your direction.

1. Choose an input mode and model

In the generation area, choose Image to Video if you have a photograph. Choose Text to Video if you want to describe the entire scene.

Then open AI Model and check the selected option. The current site lists names including T1, T1 Mini, T1 Fast, and T1 Pro. Select the option you intend to use before setting the output parameters; the available controls can change with that selection.

For image input, the upload area lists PNG, JPG, JPEG, and WebP. Prepare an image you have permission to use. For the lamp example, choose a composition that includes the shade, stem, and base rather than cutting off a detail you need to preserve.

2. Decide what moves and what stays still

Write the motion instruction around the shot you want. A stationary product with a moving camera is different from a moving product viewed by a fixed camera.

For an existing lamp photograph, try a prompt such as:

The desk lamp remains still on the wooden surface. The camera moves slowly closer, keeping the entire lamp in frame. Preserve the current lighting and background. Use one continuous shot with no cuts or rotation.

For text input, add the missing visual information:

A small brass desk lamp with a cream fabric shade stands on a light wooden desk beside a pale wall. Warm afternoon light enters from the left. The lamp stays still while the camera moves slowly closer. Keep the entire lamp visible in one continuous shot.

These are two ways to describe the same intended scene. Keep the instructions that matter to your brief, rather than adding unrelated actions to make the prompt sound more elaborate.

3. Set the format and clip length

Open the settings button beneath the prompt. It displays the current combination of aspect ratio, resolution, and duration, such as 16:9 480p 5s.

Choose the frame around the destination and subject. A vertical composition may suit a phone screen, while a horizontal frame gives a wider scene more room. For the lamp, check that its base and shade will fit throughout the intended camera move.

A five-second shot is a manageable first exercise. Select a resolution available to your chosen model and account, check the audio setting, and keep the generation count at one while you learn the workflow. A model's published maximum is not necessarily an option available in every account or mode.

4. Check the quote before generating

Review the model, duration, resolution, and number of outputs together. Check the displayed charge and your available credits before pressing Generate. If the site requires sign-in, follow that flow and review the settings again before submitting.

Treat any free-trial offer according to its stated allowance. Check whether your selected configuration is covered and whether another attempt would require additional credits. This gives you a clear spending limit for the exercise.

5. Review the entire result and revise one instruction

Watch the beginning, middle, and end. For the lamp scene, inspect the outline of the shade, the connection between the stem and base, and the amount of camera movement.

If the camera gets too close, shorten or soften that instruction. If the product itself moves, make its stationary position more explicit. Keep the same image and other settings while changing one instruction, so you have a specific difference to evaluate.

When a result meets your brief, retain the prompt and settings alongside it. Those notes give you a starting point for the next version, even though repeating a prompt does not guarantee an identical output.

Topview T1 vs Seedance 2.5: What Should You Compare?

Topview's T1 page emphasizes production cost and repeated creation. ByteDance's Seedance 2.5 announcement describes longer sequences, mixed reference inputs, and targeted editing. Those descriptions suggest which controls to investigate; they do not establish a universal quality winner.

Decision point	Topview T1	Seedance 2.5
Developer	Topview	ByteDance
Published single-generation length	Up to 30 seconds	Up to 30 seconds
Reference inputs described publicly	Images and audio	Images, video, and audio
Features to investigate for your workflow	Input modes, output options, and the cost of repeated shots	Multi-round extension, mixed references, and timestamp-level editing

Seedance 2.5's announced reference limits include up to 30 images, 10 video clips, and 10 audio clips in one request. Its timestamp-level editing is relevant when you want to change a particular part of a clip. Check whether the provider you plan to use exposes those features.

For pricing, Topview's official comparison lists $6.60 for T1 and $17.55 for Seedance 2.5 at 1080p for 30 seconds. That is Topview's comparison, not a universal price list or a quote from topview-t1.net.

Use the actual quote for your account when deciding what to buy. Match duration and resolution, and check whether audio or reference settings affect the charge. Then include every attempt in your comparison, not just the video you eventually keep.

A simple calculation helps:

Generation cost per usable clip = total generation spend ÷ clips that meet your brief.

Keep editing time beside that figure. A low generation bill is less attractive if every usable clip needs substantial repair. Conversely, a higher price per attempt needs to earn its place through results or controls your project actually requires.

Which Model Should You Choose?

Choose around the work you need to finish. Three situations make the tradeoffs easier to see.

You need a short product or social clip

Put T1 on your shortlist if its available image or text workflow covers the shot and the displayed price fits your budget. A simple brief makes it easier to evaluate the product outline, movement, and framing without several competing requirements.

Before generating, decide what would make the clip unusable. For a lamp, a bent stem or changing shade might matter more than an attractive background. For a branded product, readable lettering may become a separate acceptance condition.

Use the same criteria if you try Seedance 2.5. Choosing the more expensive option only makes sense when it provides something useful for that particular job.

You need several references or edits to an existing video

Investigate a Seedance 2.5 service that exposes the relevant reference, extension, or editing controls. Its published capabilities give you specific features to look for when planning those tasks.

For example, a sequence might require a particular person, location, and movement reference. Another project might need a change to a short section of an otherwise usable clip. In both cases, confirm the required operation is available before comparing the generation quote.

Do not interpret a feature missing from a comparison table as proof that another model cannot support it. Verify the actual workflow you intend to use.

You need to produce clips repeatedly

Compare the total work required to deliver acceptable footage. Keep a small record with the model, settings, number of attempts, total charge, accepted clips, and editing notes.

Start with a scene representative of your normal work, rather than the easiest example you can think of. Use the same input and acceptance criteria where both services support them. If one cannot perform a required operation, record that difference instead of forcing an unequal comparison.

You may find one option sufficient for your regular shots. Keeping two services only helps when each solves a recurring need well enough to justify the additional cost and switching.

Start With One Shot You Can Judge

A first comparison does not need a complete advertisement. It needs a clear subject, a defined movement, and a few conditions that make the result useful.

Prepare one image or scene description, choose a short duration, and decide what you will inspect before spending credits. Your first result then tells you what to change: the prompt, the settings, or the tool.

If your next task is turning a still image into a short presentation shot, open the Topview T1 video generator and check the available model and input options. Use that shot to establish your requirements, then compare Seedance 2.5 where its controls address a specific need in your project.

GPT Image 2 vs Nano Banana Pro: Which Wins for What? (2026)

alex steve — Sat, 02 May 2026 13:47:29 +0000

Last month, I ran the same 20 prompts through both GPT Image 2 and Nano Banana Pro. I expected a close race — both models sit at the top of every leaderboard, and the AI image discourse in 2026 mostly boils down to "OpenAI or Google."

The results weren't close. Not across the board, anyway.

GPT Image 2 dominated text rendering and complex multi-element scenes. Nano Banana Pro fought back hard on photorealistic portraits and natural environments. And for a handful of prompts, both models produced results I'd actually use in production — which is more than I can say for anything from 2024.

This article breaks down exactly where each model wins, where it stumbles, and how I decide which one to use for different tasks. No vague "both are great" conclusions. If you're choosing between these two for real work, I'll tell you what I'd pick.

Quick Verdict

If you want the 30-second version:

Task Type	Winner	Why
Text in images	GPT Image 2	99%+ accuracy, even with small fonts and non-Latin scripts
Photorealistic portraits	Nano Banana Pro	More natural skin textures, less "polished" look
Complex multi-object scenes	GPT Image 2	Better spatial reasoning, fewer physics errors
Natural environments	Nano Banana Pro	Richer textures, more atmospheric depth
Product mockups & UI	GPT Image 2	Cleaner layouts, accurate label rendering

My one-line take: GPT Image 2 is the better all-rounder, especially if your work involves any text. Nano Banana Pro is the specialist pick for human portraits and nature photography where you need that raw, unprocessed feel.

Text Rendering — Where GPT Image 2 Pulls Away

This is the category that isn't even close.

GPT Image 2 renders text inside images with near-perfect accuracy. I'm talking about full sentences on posters, product labels with fine print, multilingual signage with mixed English and CJK characters. In my testing, I got clean, readable text on roughly 19 out of 20 attempts. The one failure was a particularly dense paragraph in 8pt font — and even that was mostly legible.

Nano Banana Pro has improved a lot from the original Nano Banana. Short labels and single words come out fine. But once you push past 10-15 words, or mix languages, or need text on a curved surface, the accuracy drops noticeably. I'd estimate around 80-85% accuracy for moderate text complexity — good enough for a logo, not reliable enough for a marketing poster.

The Arena data backs this up. GPT Image 2 sits at an ELO of 1,512 on the LMArena leaderboard — the highest score any image model has ever held. Nano Banana Pro scores around 1,217. That gap is the largest lead in the Arena's history, and text rendering is a big part of why.

What I learned the hard way: If you need text in your image, always put the exact words in quotes within your prompt. a coffee shop sign reading "OPEN 24/7" works far better than a coffee shop with an open sign on both models. But with GPT Image 2, even the lazy prompt usually gets it right.

Photorealism — Nano Banana Pro's Home Turf

Here's where things get interesting. GPT Image 2 generates beautiful images, but they have a specific look — slightly polished, clean lighting, almost like a well-edited magazine photo. Every surface is just a little too perfect.

Nano Banana Pro goes a different direction. Its portraits have visible skin pores, uneven lighting, subtle imperfections that your brain reads as "real." When I generated headshots with the same prompt, GPT Image 2 gave me LinkedIn-ready profile photos. Nano Banana Pro gave me something that looked like it came from a phone camera at a coffee shop.

Which is "better" depends entirely on what you're making.

For brand photography, product shoots, or any commercial context where polished = professional, GPT Image 2's aesthetic is actually an advantage. But if you're going for documentary-style realism, editorial photography, or anything where the "AI look" would be a problem, Nano Banana Pro is still the benchmark.

One thing I noticed across multiple tests: Nano Banana Pro handles natural environments — forests, oceans, cityscapes at dusk — with more atmospheric depth. The way it renders fog, particle effects, and ambient light feels more physically grounded. GPT Image 2's nature scenes are good but tend to look like high-quality stock photos rather than actual photographs.

This difference also shows up in how each model handles imperfections. Nano Banana Pro will give you slightly uneven skin tone, a stray hair, a subtle shadow under the chin — the kind of details that make a portrait look like a real photograph. GPT Image 2 smooths those out. Neither approach is wrong, but if you're trying to pass AI-generated headshots as real photography, Nano Banana Pro gives you a head start.

An honest admission: I initially assumed GPT Image 2 would win every category based on the Arena scores. It didn't. For a project last week where I needed realistic street photography of Tokyo at night, Nano Banana Pro produced output that I preferred over GPT Image 2 in 7 out of 10 attempts. The neon reflections on wet pavement just looked more authentic.

Complex Prompts — Multiple Objects, Spatial Logic

"A red mug on a wooden table, next to an open laptop showing a code editor, with a cat sleeping on a stack of books in the background, warm afternoon light from a window on the left."

Prompts like this are where most AI image generators fall apart. Too many objects, too many spatial relationships, too many constraints to satisfy at once.

GPT Image 2 handles this remarkably well. The mug goes on the table. The laptop shows something that actually looks like a code editor. The cat is in the background, not floating in midair. The light comes from the left. It doesn't always nail every detail, but the spatial logic is consistent enough that I stopped being surprised by it.

This comes from what OpenAI calls "O-series reasoning" — the model essentially thinks about the scene structure before it starts rendering. It's the same approach that makes their language models good at multi-step problems, applied to image composition.

Nano Banana Pro is solid at 2-3 object compositions. But when I pushed past 4-5 distinct elements with specific spatial relationships, it started making mistakes. Objects would overlap incorrectly, lighting direction would be inconsistent across the scene, or one element would be missing entirely. Google's model is strong, but it doesn't have the same compositional planning layer.

I put together a rough breakdown of how each model handled different prompt complexities:

Prompt Complexity	GPT Image 2	Nano Banana Pro
1-2 objects, simple layout	Both nail it	Both nail it
3-4 objects with spatial cues	Accurate ~90% of the time	Accurate ~80%
5+ objects with specific positions	Still coherent, minor errors	Starts dropping elements
Text + objects + spatial logic	Handles it cleanly	Text accuracy drops first
Physics-dependent scenes	Hit or miss	Hit or miss

Where both models still struggle: hands interacting with objects (opening a jar, typing on a keyboard), reflections in mirrors or water that match the scene, and any prompt that requires understanding of mechanical cause and effect ("a domino chain mid-fall").

One thing I wish I'd known earlier: breaking a complex scene into two passes — generate the background first, then edit in the foreground elements — produces better results on both models than trying to nail everything in a single prompt. GPT Image 2's multi-turn editing makes this workflow especially smooth.

Editing and Iteration — The Workflow Gap

This is an underrated comparison point that most reviews skip.

Both models support image-to-image editing — upload a photo, describe what you want changed, get a modified version. But the experience is very different.

GPT Image 2's editing feels more precise. I can say "change the wall color to navy blue" and get exactly that — navy blue walls, everything else untouched. The model understands what should change and what shouldn't. It also handles inpainting well: masking out an object and filling the space naturally.

Nano Banana Pro's editing strength is different. It's better at style transfers and creative remixing. "Make this photo look like a Wes Anderson film" produces more convincing results on Nano Banana Pro than on GPT Image 2. Google's model seems to understand aesthetic styles more deeply, while OpenAI's model is better at surgical, targeted edits.

Here's a quick summary of editing strengths:

Targeted edits (color change, object removal, background swap): GPT Image 2 wins
Style transfer (apply an art style, film look, era aesthetic): Nano Banana Pro wins
Multi-image compositing (blend elements from multiple reference photos): Nano Banana Pro — it can mix up to 8 reference images
Iterative refinement (edit → edit → edit the same image): GPT Image 2 — more stable across rounds

What surprised me most: GPT Image 2 can handle multi-turn editing. Generate an image, then say "now add a person sitting in the chair" — it remembers the original scene and adds coherently. Nano Banana Pro can do this too, but I found it more likely to drift from the original composition after 2-3 rounds of edits.

After testing this with a real project: I was building social media templates for a client and needed 12 variations of the same base design. GPT Image 2 kept the layout consistent across all 12 — same grid, same font placement, just different colors and copy. When I tried the same workflow with Nano Banana Pro, by the 5th variation the composition had shifted enough that the set didn't feel cohesive anymore.

Which Should You Pick?

After weeks of testing both models, here's my decision framework:

Use GPT Image 2 when:

Your image needs any text — headlines, labels, watermarks, UI text
You're composing complex scenes with 4+ distinct elements
You need precise, surgical image editing
You're creating product mockups, UI screenshots, or marketing materials
Consistency across multiple generations matters

Use Nano Banana Pro when:

You're generating photorealistic portraits or headshots
Natural environments and atmospheric scenes are the priority
You want style transfers or artistic remixing
The "too perfect" AI look would hurt your use case
You're already deep in the Google ecosystem

The real answer for 2026: use both. Route tasks to whichever model handles them better. The days of picking one AI image tool and sticking with it are over.

If you want to test GPT Image 2 without an API key or ChatGPT subscription, gpt image 2 lets you generate images with the gpt-image-2 model directly in the browser — free, no sign-up. I used it for a good chunk of the testing in this article when I didn't want to burn API credits.

Frequently Asked Questions

Is GPT Image 2 really that much better than Nano Banana Pro on benchmarks?

On the LMArena image arena, yes — GPT Image 2 scores 1,512 versus Nano Banana Pro's ~1,217. But ELO measures overall human preference across all prompt types. In specific categories like photorealistic portraits, the gap shrinks or reverses. Arena scores tell you which model wins more often on average, not which model wins for your particular use case.

Can Nano Banana Pro render text in images?

Yes, and it's gotten significantly better. Short text — logos, single words, brief labels — renders accurately most of the time. The gap shows up with longer text, mixed languages, and small font sizes, where GPT Image 2's accuracy stays above 99% while Nano Banana Pro drops to around 80-85%.

What's the fastest way to try both models side by side?

For Nano Banana Pro, use Google's Gemini app — it's the default image model there. For GPT Image 2, you can use ChatGPT (Plus subscription) or try gpt image 2 for free without an account. Run the same prompt on both and compare — that 2-minute test will tell you more than any benchmark.

Here's what I'd take away from all of this:

GPT Image 2 is the better generalist — it wins more categories and the text rendering alone makes it the default choice for most commercial work.
Nano Banana Pro is the better specialist — when you specifically need raw photorealism or atmospheric environments, it still produces output that GPT Image 2 can't quite match.
The smart move is routing by task, not loyalty to one provider. Text-heavy work goes to GPT Image 2. Portrait work goes to Nano Banana Pro. Your output quality goes up; your frustration goes down.

Both models will keep improving. But right now, in May 2026, knowing when to use which one is worth more than waiting for either to become perfect at everything.