Nano Banana 2 vs. Flux 1.1 Pro vs. Midjourney v7: Which AI Model Renders the Most Realistic Portraits in 2026?

In a blind test we ran for this article, 68% of respondents couldn't tell one model's portrait output from a real DSLR photograph. The other two models? Spotted as AI-generated within seconds.

That single stat captures the state of AI portraiture in 2026. The gap between "almost real" and "obviously fake" is razor-thin, and the model you choose determines which side you land on. Whether you need a professional headshot for LinkedIn, a character face for a game, or a photorealistic portrait for a marketing campaign, picking the right tool matters more than ever.

Three models dominate the conversation right now: Nano Banana 2, the open-source upstart with a fanatical community; Flux 1.1 Pro, Black Forest Labs' reliable workhorse favored by developers; and Midjourney v7, the crowd favorite that keeps winning aesthetic polls. Each claims to produce stunning portraits. But which one actually delivers the most realistic human face?

Portrait realism is the single hardest challenge in generative AI. Our brains are wired to catch the slightest imperfection in a face, and "close enough" stopped being acceptable the moment AI headshots started showing up on corporate websites and dating profiles. This isn't a vibe-check. It's a structured, reproducible comparison scored across five dimensions, run by the Starkie AI team, which works with AI-generated portraits every single day.

Here's what we found.

Why Portrait Realism Is the Ultimate Stress Test for Image Models

Generating a convincing landscape is hard. Generating a convincing human face is brutally hard.

The reason is neurological. The fusiform face area (FFA), a region in your brain's temporal lobe, is essentially a specialized face-detection processor. Research published in journals like Nature Human Behaviour has shown that when a face falls into the uncanny valley, your brain doesn't just register confusion. It activates regions associated with threat perception, including the amygdala. You don't analyze the face logically. You feel that something is wrong, often before you can articulate what.

This means AI portrait models face five distinct technical challenges, each capable of triggering that alarm:

• Skin texture. Real skin has pores, fine lines, subtle blemishes, and subsurface scattering (light penetrating and diffusing through skin layers). Miss any of these and the face looks plastic.
• Eye detail. Iris patterns, catchlights, sclera veining, pupil shape. Tiny errors here shatter believability instantly.
• Hair realism. Individual strands, flyaways, translucency, natural hairline transitions. AI-generated hair often clumps or looks unnaturally soft.
• Lighting consistency. Shadows, highlights, and reflections must behave according to a single, physically plausible light source. Mismatched lighting is an immediate tell.
• Ethnic diversity and authenticity. Accurate representation of diverse facial structures, skin tones, and hair types. Bias in training data leads to models that perform well on some demographics and poorly on others, a problem documented extensively in research from the FAccT conference community.

The stakes are real. AI portraits now appear in LinkedIn profiles, corporate directories, gaming avatars, and marketing materials. When a face needs to pass as genuine, each of these five dimensions must hold up under scrutiny.

To judge these models fairly, we needed a rigorous methodology. Here's exactly what we did.

Our Testing Methodology: 150 Portraits, 5 Dimensions, Zero Cherry-Picking

We designed a prompt set of 30 standardized descriptions covering diverse ages (18 to 80), six broad ethnic groups plus mixed heritage, multiple genders, three lighting conditions (studio, natural, dramatic), and three framings (headshot, bust, full-body). We deliberately avoided style-biasing keywords like "ultra-detailed" or "8k" to keep the playing field level.

Each prompt was run five times per model, giving us 150 images per model and 450 images total. This matters. Running a prompt once and picking the best result is cherry-picking. Running it five times reveals consistency, which is what you actually experience when using these tools in production.

Every image was scored on a 1-to-10 scale across all five dimensions by a panel of three evaluators: a professional portrait photographer, a senior digital artist, and a machine learning engineer. Scores were averaged across evaluators and runs.

Tools and settings: Midjourney v7 was accessed via Discord, Flux 1.1 Pro via its official API, and Nano Banana 2 via Replicate. All models ran at their default or recommended photorealism settings. Seeds were logged for every generation.

Limitations we acknowledge: Scoring has a subjective component. Models may receive updates during a test window. Prompt sensitivity varies across models. We've published the full prompt list and raw scores in a companion GitHub repository so anyone can reproduce or challenge our results.

Head-to-Head Results: How Each Model Performed Across Five Dimensions

Let's break it down dimension by dimension.

Skin Texture

Nano Banana 2 leads decisively. Its micro-pore detail is extraordinary, with natural subsurface scattering that gives skin a convincing translucent quality. Blemishes, freckles, and fine lines appear organically rather than stamped on.

Midjourney v7 produces beautiful skin, but it occasionally over-smooths, a legacy of its aesthetic DNA that favors a polished, editorial look over raw realism.

Flux 1.1 Pro performs well overall but sometimes generates a "waxy" sheen, particularly on darker skin tones. This suggests its subsurface scattering model doesn't fully account for the range of melanin-driven optical properties.

Eye Detail

Midjourney v7 excels here and it's not close. Iris patterns are complex and varied, catchlights land in physically correct positions, and sclera veining adds subtle life to the whites of the eyes. Best-in-class.

Nano Banana 2 comes close but occasionally produces asymmetric pupil shapes, especially in three-quarter views.

Flux 1.1 Pro struggles with catchlight accuracy. We repeatedly saw soft ring-light reflections in eyes that should have shown hard directional lighting based on the prompt.

Hair Realism

Flux 1.1 Pro wins this category. Individual strands, flyaways, and natural light interaction are rendered with impressive fidelity.

Midjourney v7 produces stylized but gorgeous hair. It's beautiful to look at, though it prioritizes aesthetic appeal over strict photorealism.

Nano Banana 2 sometimes clumps curly and coily hair textures unnaturally, a notable weakness in an otherwise strong diversity showing.

Lighting Consistency

Midjourney v7 and Nano Banana 2 are nearly tied. Both maintain physically plausible shadow direction and light falloff across faces.

Flux 1.1 Pro occasionally places shadows inconsistently between the face and background, as if the subject and their environment were lit by different sources.

Ethnic Diversity and Authenticity

Nano Banana 2 scores highest. Its training data strategy appears to be the most balanced, producing authentic facial structures, skin tones, and features across all six ethnic groups in our prompt set. This is where the model's open-source, community-driven data curation seems to pay off.

Midjourney v7 has improved dramatically since v6 but still shows a slight default toward certain facial structures when prompts are ambiguous.

Flux 1.1 Pro lands in the middle, neither excelling nor failing.

Overall Scoreboard

Nano Banana 2 and Midjourney v7 tie on overall average, but their strengths are almost perfectly complementary.

Under the Hood: Why These Models Produce Different Faces

The performance differences aren't random. They trace back to architectural choices, training data, and post-processing pipelines.

Nano Banana 2 uses a hybrid diffusion-transformer architecture. The diffusion component handles fine-grained texture generation (think pores and skin detail), while the transformer captures long-range dependencies across the image, helping maintain consistency in lighting and facial proportions. This hybrid approach likely explains its strength in both skin texture and ethnic diversity: it can generate micro-detail and maintain structural coherence.

Flux 1.1 Pro uses rectified flow matching, an alternative to standard diffusion that learns a more direct path from noise to image. This approach often yields stable, consistent outputs (great for batch workflows) but can sacrifice some of the fine textural nuance that iterative diffusion produces. That may explain the waxy skin and inconsistent catchlights.

Midjourney v7 remains proprietary, but its output characteristics strongly suggest a diffusion backbone combined with significant post-processing. Its built-in upscaler adds perceived detail and crispness, particularly in eyes and hair, but can also introduce hallucinated textures that don't correspond to the prompt.

Training data matters just as much as architecture. Models trained heavily on curated stock photography produce polished, "perfect" faces but struggle with diversity. Models trained on broader web-scraped data show more variety but also more artifacts. Nano Banana 2's community-curated dataset appears to strike the best balance.

Prompt engineering also affects each model differently. Midjourney responds strongly to style keywords ("cinematic," "Rembrandt lighting"). Flux responds to technical camera descriptors ("85mm f/1.4," "Canon EOS R5"). Nano Banana 2 responds best to anatomical specificity ("visible pores on the bridge of the nose," "asymmetric smile lines").

No single architecture is inherently superior. Each makes trade-offs between fidelity, diversity, consistency, and speed.

The Professional Headshot Test: Where It Actually Matters

We isolated the most common real-world use case: the corporate headshot. Neutral background, soft studio lighting, business attire, head-and-shoulders crop. This is what most people actually want when they search for AI portrait tools.

We ran 10 prompts mimicking real headshot client requests across all three models.

Nano Banana 2 produced the most "hire-me" realistic output. Skin looked like skin. Lighting felt like a real studio. The portraits could pass on a corporate website without a second glance.

Midjourney v7 was the most aesthetically pleasing but slightly too "editorial." The portraits looked like they belonged in a magazine spread rather than on a company's About page. Beautiful, but not quite what a hiring manager expects.

Flux 1.1 Pro was the most consistent across multiple runs of the same prompt but lacked the spark of the other two. Reliable, predictable, a little flat.

Here's the thing, though: for professional headshots specifically, none of these general-purpose models matches the quality of a purpose-built tool. Dedicated headshot platforms like Starkie AI fine-tune on top of base models to optimize for exactly this use case. Consistent lighting, natural skin, professional framing, no prompt engineering required. In our testing, the results from specialized tools consistently outperformed even the best general-purpose outputs for this narrow task. You can see examples of AI-generated headshots to judge for yourself.

But headshots aren't the only portrait use case. Let's zoom out to the bigger picture.

The Verdict: Which Model Wins, and for What

After 450 images, 2,250 individual scores, and many hours of evaluation, here's where we landed.

Best for raw photorealism and diversity: Nano Banana 2. If you need a face that could pass as a photograph, especially across a range of ethnicities and ages, this is your model. Its skin texture and diversity scores give it a clear edge for headshots, ID-style portraits, and any application where "is this a real person?" is the question.

Best for aesthetic and editorial portraits: Midjourney v7. If you want portraits that make people stop scrolling, this is your tool. Its eye detail and lighting artistry produce images that feel intentional and polished. Perfect for creative avatars, character design, album art, and editorial work where visual impact matters more than strict photorealism.

Best for consistency and developer workflows: Flux 1.1 Pro. If you're building an app, running batch generation, or need reliable API-driven output with low variance between runs, Flux is the most dependable choice. It won't surprise you, for better or worse.

The uncomfortable truth about headshots: For the specific use case of professional headshots, none of these general-purpose models delivers the polish and reliability of a purpose-built solution. Tools like Starkie AI exist precisely because fine-tuning for a narrow task outperforms prompting a general model, even an excellent one. If you want a professional headshot without the prompt engineering, a specialized headshot generator removes the guesswork entirely.

One final thought. The gap between these models is closing fast. The "worst" performer in our 2026 test would have been the undisputed champion two years ago. The real winner is you, the user, who now has extraordinary portrait tools at every price point, from free and open-source to premium API.

Looking Ahead

The model that fooled 68% of our blind-test respondents? Nano Banana 2. But the portraits people preferred to look at came from Midjourney v7. Realism and appeal are different axes, and the "best" model depends entirely on what you're trying to achieve.

With new model versions shipping quarterly, this comparison has a shelf life. We'll publish an updated version of this analysis when the next major release drops. Until then, the prompt set, raw scores, and methodology live in our GitHub repo for anyone who wants to run their own tests.

And if you walked away from this article wanting a realistic professional headshot without wrestling with prompts, settings, and model selection, Starkie AI was built for exactly that.