Between 2022 and 2026, the number of AI image generators accessible to the general public has exploded. What was once reserved for researchers and insiders has become commonplace: a few seconds and a few words are all it takes to produce a photorealistic image of any imaginable scene.
But not all generators are alike. Each has its strengths, its weaknesses, and above all its visual signatures — those small details that, with practice, allow you to guess which tool produced a given image. Here is the lay of the land in 2026.
Midjourney — the king of artistic realism
Midjourney is probably the best-known generator among the general public since its explosion on Discord in 2022-2023. Its philosophy is clear: produce aesthetically flawless images with particular attention to cinematic rendering and composition.
Version V7, rolled out progressively in 2025, marked a significant milestone. Human portraits reach a level of anatomical consistency that is difficult to distinguish from professional photography. Hands — long the weak spot of every generator — are handled with remarkable precision in the majority of cases.
What characterises a Midjourney image:
- A pronounced "cinematic" look: carefully crafted bokeh, golden-hour lighting, compositions that feel like film stills
- Highly refined skin textures, with a slightly "high-resolution painting" quality
- A tendency to idealise facial features: faces are attractive according to very specific aesthetic standards
- Backgrounds that are sometimes too uniform or too "perfect" compared to real photographs
DALL-E 3 (OpenAI) — precision in the details
DALL-E 3, integrated into ChatGPT since late 2023, followed by the evolution towards native image generation in GPT-4o in 2025, has profoundly changed the user experience. OpenAI's key strength: the ability to follow very precise instructions, including for text within images.
Where Midjourney tends to "interpret" a prompt with creative liberty, DALL-E aims to faithfully execute what it is asked. This approach produces images that are less "glamorous" but often more practical: infographics, documentary illustrations, scenes with legible text.
What characterises a DALL-E image:
- A "cleaner," less dramatic style than Midjourney — fewer lens flares and cinematic effects
- Better handling of text in the image, though errors still occur with longer text
- Character proportions that sometimes differ slightly from standard photographic conventions
- A tendency to generate backgrounds that are more "informational" and less stylised
Stable Diffusion — the open-source contender
Stable Diffusion occupies a unique position in the landscape: it is an open-source model, meaning anyone can download, modify, and fine-tune it on their own data. This freedom has spawned an extremely rich ecosystem of specialised models and plugins.
The "base" SDXL (Stable Diffusion XL) model and its successors produce images of competitive quality alongside commercial solutions. But the great variability of derivative models makes detection more difficult: there is no single "Stable Diffusion style" — instead, there are hundreds of variants.
What can give away a basic Stable Diffusion image:
- On unrefined models, a slight tendency towards artefacts in fine-to-coarse grain transition zones
- Community "anime" or "hyper-realistic" models have very recognisable signatures for trained eyes
- The diversity of styles makes generalisation difficult — and that is precisely its strength
Flux, Adobe Firefly, and the new 2026 models
The landscape has expanded considerably since 2024. Black Forest Labs launched FLUX.1 (in its pro, dev, and schnell variants), which quickly established itself in professional creative workflows for its quality and flexibility.
Adobe Firefly occupies a particular niche: trained exclusively on licensed images, it positions itself as the "safe" alternative for professionals. Its style is often more "polished" and less organic than its competitors.
Google, for its part, integrates image generation into the Gemini ecosystem. The 2025-2026 models, sometimes referred to by the community codename "Nano Banana," represent the state of the art in terms of speed and coherence for rapid large-scale generation.
How to tell which generator created an image
Identifying the source generator is a difficult exercise, even for experts. A few leads:
Distinctive styles
Midjourney V5-V6 has a very recognisable "signature": that slightly painterly rendering, that cinematic quality. Platform regulars often spot it instinctively. DALL-E 3 leans towards a more "neutral" and factual style. Community Stable Diffusion models can mimic almost any style, which is precisely what makes them hard to identify.
Generator-specific artefacts
Every architecture leaves subtle traces. With Midjourney, look at how fine hairs blend into the background — there is often a distinctive treatment of these transition zones. With base SDXL images, certain high-frequency areas (fine fabric, wire mesh, dense vegetation) may exhibit repetitive patterns. These signatures evolve with each new version.
Nano Banana: the generation that defies experts
The term "Nano Banana" refers in the creator community to the very latest generation of models (2025-2026) capable of producing ultra-realistic images in just seconds. These models have virtually eliminated the obvious artefacts that characterised previous generations.
Faced with these images, even imaging professionals — photographers, retouchers, art directors — regularly fail to distinguish them from real photographs. It is precisely this level of difficulty that the Ultra Nano Banana mode on Fake or Real aims to recreate: images that push even the most seasoned players to their limits.
Can you tell a Midjourney image from a real photograph? The Ultra Nano Banana mode is waiting for you.
Test yourself on Fake or Real →Summary
Midjourney excels at cinematic and artistic realism. DALL-E 3 is precise and literal in prompt execution. Stable Diffusion offers open-source flexibility that the others lack. Flux and the new 2026 models push the boundaries of realism even further. Knowing each tool's visual signatures gives you an edge — but against the best current models, hands-on practice remains essential.