The best AI image generators, judged on creator work
The top models are now separated by a rounding error on blind-vote leaderboards. What still separates them is text in the image, character consistency, and whether you can legally use the output.
9 minute read
The leaderboard, and why it settles less than it looks
Artificial Analysis runs a blind image arena where people vote between two images made from the same prompt, and converts those votes into an Elo rating. Read in August 2026, the top of that board is crowded: OpenAI's GPT Image 2 leads, with Reve, Google's Nano Banana 2, the previous GPT Image release and MAI-Image sitting within about seventy points of it. Two things follow. First, the gap between the leading models is now small enough that picking on rank alone is close to arbitrary. Second, Midjourney does not appear in that top ten at all, which tells you the arena measures preference on generic prompts rather than the stylised, art-directed work Midjourney is used for. Use leaderboards to rule models in, never to rule them out.
The models worth knowing, and what each is actually for
GPT Image 2, which OpenAI shipped in April 2026, is the strongest all-rounder for instruction-following and layout. Google's Nano Banana line is built on Gemini and aimed at legible text inside the image, multi-language rendering, and output at 2K and 4K; Nano Banana Pro arrived in November 2025 and the faster Nano Banana 2 followed in February 2026. Midjourney, on its V8 series with plans from $10 a month, remains the choice when you want a look rather than a literal rendering of a prompt. Black Forest Labs' FLUX.2 offers open weights alongside hosted versions and conditions on up to ten reference images. Ideogram made typography its identity and released 4.0 as an open-weight model in June 2026. Adobe Firefly and Canva matter less for raw quality than for what surrounds them.
Thumbnails: the job worth getting right, and how to actually do it
A thumbnail is not an illustration, it is a piece of signage that has to survive being shrunk to the width of a thumb. That changes what you should ask a model for. The reliable pattern is composite rather than one-shot: generate the scene or background, bring in a real photograph of your own face rather than a synthesised one, and set the text in a design tool where you control the font, the kerning and the crop. Models have become good enough at in-image text that you can prompt for it, but you cannot easily edit it afterwards, and a thumbnail almost always needs a second pass. Ideogram's editable text layers, announced on its 4.0 roadmap, are the shape of the fix, not a shipped solution you should plan around.
Podcast cover art has stricter rules than any other creator image
Cover art is the one place where the platform constraints do most of your design thinking for you. Directories want a square file, 3000 by 3000 pixels as the working size with 1400 as the practical minimum, in JPEG or PNG. It then gets displayed at postage-stamp size in a podcast app, often next to a playback progress bar that eats the bottom edge. That combination rules out almost everything an image model gives you by default, because the models like detail and cover art needs a shape and two or three words. The productive use here is generating a background texture or a stylised portrait treatment, then setting the show name in a real type tool. Test the result at 200 pixels before you commit.
What they are still bad at
Three failure modes have survived every generation of these models. Text is much better and still not editable, so anything with more than a few words is faster to set by hand. Anatomy remains unreliable in ways that matter for anything instructional: a study in the Journal of Hand Surgery Global Online, published in October 2025, generated 1,500 images across six generators including DALL-E, Midjourney, Gemini and Stable Diffusion, and found that 99.8% contained at least some fabricated anatomy. And consistency is structurally hard, because each generation starts fresh. Reference systems help, but the bound is visible in the vendors' own claims: Google's pitch for Nano Banana Pro is that it holds resemblance for up to five people across as many as fourteen input images, which is an impressive number and also a ceiling.
Labels, watermarks and whether you can legally use the output
Two things travel with an AI image now. Every image Google generates carries an invisible SynthID watermark, and free and Pro tier outputs carry a visible Gemini sparkle as well, which matters if you are putting the result on a channel banner. Meta detects those industry-standard indicators and adds an AI Info label on Facebook, Instagram and Threads, and its advertising rules now require advertisers to disclose AI-generated creative. On the rights side, Adobe takes the opposite tack from most of the field: Firefly is trained on licensed and public-domain material, marketed on commercial safety, and Adobe offers IP indemnification to customers on qualifying plans. For a solo creator that matters little. For anything running as a paid ad or a client deliverable, it is often the deciding factor.
How to choose, in one paragraph
If you want one tool, take whichever frontier model is already bundled with something you pay for, because the quality gap at the top is now smaller than the workflow gap. Add a second only for a specific weakness: Ideogram or the Nano Banana line when the image has to carry words, Midjourney when you need a consistent look rather than a literal result, Firefly when the output is commercial and the provenance has to be defensible. Keep three things human regardless of tool. The concept, because models produce the average of their training data and the average does not stand out in a feed. The face, because a real one photographs better than a generated one. And the final text, because that is the part your audience actually reads.
FAQ
What is the best AI image generator in 2026?
On Artificial Analysis's blind-vote arena in August 2026, OpenAI's GPT Image 2 leads, with Reve, Google's Nano Banana 2 and MAI-Image close behind. The gap across that group is small, so the better question is fit: Nano Banana and Ideogram for images that carry text, Midjourney for a distinct look, Adobe Firefly when commercial rights matter.
Can AI image generators do text properly yet?
Much better than two years ago, and still not editable. Google positions Nano Banana Pro specifically on accurate, legible multi-language text, and Ideogram built its whole product around typography. The practical problem is revision: if one word is wrong you regenerate the whole image. For thumbnails and covers, generate the picture with the model and set the type in a design tool.
Why do AI images still get hands and anatomy wrong?
Hands appear less clearly and less often than faces in training data, and are frequently partly hidden, so models learn a blurred version of them. The scale of the problem is measurable: a 2025 study in the Journal of Hand Surgery Global Online generated 1,500 images across six generators and found 99.8% contained at least some fabricated anatomy. Avoid close-up hands, or shoot that frame.
Do I have to label AI-generated images on social media?
It depends on the platform and the content. Meta adds an AI Info label when it detects standard AI indicators or when you disclose, and requires advertisers to disclose AI-generated creative. YouTube's disclosure requirement targets realistic synthetic content that could be mistaken for real events, rather than stylised graphics. Google-generated images also carry an invisible SynthID watermark by default.
Sources
- Text to Image Leaderboard - Top AI Image Models · Artificial Analysis
- Nano Banana Pro: Gemini 3 Pro Image model from Google DeepMind · Google
- Limitations of Artificial Intelligence Generated Images for Hand Surgery Patient Education · Journal of Hand Surgery Global Online
- Adobe Firefly: comprehensive and commercially safe AI content creation · Adobe
- Labeling AI Content · Meta Transparency Center
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