We gave three AI models the exact same prompt: "a 35-year-old South Asian woman, professional headshot, soft studio lighting, neutral background." The results ranged from photorealistic perfection to uncanny valley nightmare. One model nailed the skin texture but mangled the earrings. Another produced flawless hair but gave our subject three rows of teeth. The third looked like it was shot on a Hasselblad.
Here's the thing. In 2024 and into 2025, AI face generation crossed a critical threshold. For the first time, multiple models can produce faces that fool human observers more than 50% of the time in blind tests. A February 2026 study published by Neuroscience News found that modern AI-generated faces tend to be "too good to be true," so unusually symmetrical and well-proportioned that viewers actually rate them as more trustworthy than real photographs.
But "good enough to fool someone scrolling Twitter" and "good enough for a professional headshot on your company's About page" are two very different bars.
This post puts three of the most talked-about models, Nano Banana 2, Flux 1.1, and DALL-E 3, through a rigorous, apples-to-apples face generation gauntlet. By the end, you'll know which engine best fits your project, whether that's game assets, marketing visuals, or AI-powered headshot tools like the one we build at Starkie AI.
Why Face Generation Is the Hardest Test for Any AI Image Model
Your brain is a face-detection machine. You have dedicated neural circuitry called the fusiform face area whose entire job is reading faces. It helped your ancestors identify friend from foe in a fraction of a second. And it makes you extraordinarily sensitive to even micro-errors in facial geometry.
According to analysis published on Medium, humans can be "bothered" when an AI-generated face shifts by even 5% from expected proportions. A landscape with a slightly wrong tree? You won't notice. A portrait with slightly wrong pupils? You'll feel a visceral, gut-level discomfort before you can even articulate what's off.
This is why faces are the ultimate stress test for generative models. The uncanny valley isn't a gradual slope. It's a cliff. And every AI image model has to cross it.
The Three Contenders
Nano Banana 2 started as a "playful internal placeholder" name at Google DeepMind before gaining unexpected traction. It was officially released as Gemini 3.1 Flash Image in February 2026. It blends Pro-level quality with Flash-level speed and can maintain character consistency for up to five subjects across iterative workflows.
Flux 1.1 by Black Forest Labs is the Stable Diffusion successor known for exceptional prompt adherence. The family was significantly updated with FLUX.2 in November 2025, pushing from experimental generation into production-grade territory with a 32-billion parameter open-weight model.
DALL-E 3 by OpenAI is tightly integrated with ChatGPT and evolved into what MindStudio describes as a native multimodal architecture (GPT Image 1.5), where the same neural network processes both text and image tokens.
How We Structured the Test
We used 12 identical prompts covering diverse ages, ethnicities, lighting conditions, and angles. Each output was scored on five dimensions by two independent evaluators. All outputs were generated at default settings with no cherry-picking. The first output from each prompt was the one we scored.
The Five-Dimension Scoring Rubric (Steal This for Your Own Tests)
Before we get to results, let's talk methodology. Several organizations use structured rubrics for evaluating AI-generated imagery. OpenAI's developer documentation recommends a 0-to-5 graded metric covering realism, layout, and artifact severity. Deccan AI's 2026 taxonomy evaluates Attribute Fidelity and Object Layout Fidelity. We synthesized these frameworks into five face-specific dimensions.
Dimension 1: Skin Texture & Realism
Does the skin look like skin? Are pores visible at appropriate zoom? Is there realistic subsurface scattering, or does the face look like airbrushed plastic? We score from 1 (obviously computer-generated) to 5 (indistinguishable from a DSLR photo).
Dimension 2: Eye Detail & Symmetry
Correct iris reflections. Consistent catchlights. Proper pupil shape. Symmetric positioning. UX Planet's expert analysis notes that the gaze in AI images often seems "empty, asymmetrical, lacking natural depth or emotion," with misaligned pupils and irises of different sizes. Eyes are where most AI faces fall apart.
Dimension 3: Hair Realism
Individual strand rendering. Realistic flyaways. Proper interaction with lighting. Does hair look like a solid mass or like actual hair? We paid special attention to how each model handles different hair textures: coily, straight, and wavy.
Dimension 4: Ethnic Diversity Accuracy
Does a prompt for "East African woman" produce a face that looks authentically East African, or a generic brown-skinned face? We tested for culturally accurate facial features, skin undertones, and hair textures across six ethnic prompts.
Dimension 5: Artifact Resistance
The classic failure modes. Asymmetric ears. Melted teeth. Jewelry that fuses with skin. Background objects bleeding into the face. Neuroscience News reports that early AI faces were frequently given away by "distorted teeth, glasses that merged into faces, ears that didn't quite attach properly." How gracefully does each model sidestep these pitfalls?
Head-to-Head Results: How Each Model Performed Across 12 Prompts
Here are the aggregated scores, averaged across all 12 prompts and both evaluators. Each dimension is scored 1 to 5.
Dimension | Nano Banana 2 | Flux 1.1 | DALL-E 3 |
|---|---|---|---|
Skin Texture | 4.1 | 4.4 | 3.9 |
Eye Detail | 3.8 | 4.2 | 4.0 |
Hair Realism | 4.3 | 4.0 | 3.7 |
Ethnic Diversity | 3.5 | 4.1 | 4.4 |
Artifact Resistance | 3.9 | 4.3 | 4.5 |
Overall Average | 3.92 | 4.20 | 4.10 |
Now let's break down what each model did well, and where it stumbled.
Nano Banana 2: The Speed-Quality Hybrid with a Fine-Tuning Edge
Nano Banana 2 excels at photorealistic skin rendering and hair detail. The model's character consistency feature, which Google's blog confirms can maintain exact resemblance for up to five characters across iterations, is a massive advantage for anyone building serialized content or storyboarding workflows.
Hair was its standout category. Flyaways caught light naturally. Individual strands were visible. Across straight and wavy textures, it consistently produced the most convincing results.
Where it struggled: ethnic diversity nuance. Prompts for specific ethnic backgrounds sometimes produced faces that felt generically rendered rather than authentically distinct. We also noticed subtle ear asymmetry in roughly a third of outputs.
Best for: Teams that need speed at scale and can invest in fine-tuning for their specific portrait style.
Flux 1.1: The Most Balanced Performer
Flux 1.1 was the most consistent model across all five dimensions. When we asked for "dramatic Rembrandt lighting on a 60-year-old man," we got exactly that. Prompt adherence was exceptional.
According to BentoML's March 2026 analysis, FLUX.2 [pro] delivers image quality "on par with top proprietary models." MindStudio noted that Flux dominates professional image generation in 2026, creating images "almost indistinguishable from professional photographs."
Its weakness? At certain angles, skin took on a slightly plastic quality. And hair textures for coily and tightly textured hair types lagged noticeably behind its rendering of straight hair.
Best for: Developers who need balanced realism and precise prompt control without heavy fine-tuning.
DALL-E 3: Cleanest Output, Least Photorealistic
DALL-E 3 won on artifact resistance and ethnic diversity accuracy, likely a result of OpenAI's extensive reinforcement learning from human feedback. Teeth were consistently clean. Ears were symmetric. Jewelry didn't fuse with skin. In a comparison by Deccan AI, the DALL-E lineage (GPT Image 1) scored highest on "Realism" and "Quality Adherence" among five leading models.
However, DALL-E 3 has a distinct visual signature. Outputs skew slightly over-saturated with a painterly quality that's beautiful but not always photorealistic. Hair often looks like a unified mass rather than individual strands. If you've seen enough AI images, you can spot a DALL-E output across the room.
Best for: Marketers who need quick, consistent, artifact-free headshots for blog posts or social media.
The "Teeth and Jewelry" Stress Test
Why did we isolate this particular test? Because teeth and jewelry are where AI face generation most visibly breaks down. A smiling portrait with earrings is essentially a nightmare prompt for current models. You're asking the AI to render multiple small, detailed, symmetric objects that need to interact realistically with skin, light, and each other.
Traditional diffusion models treat images as mathematical middle grounds. To these systems, a "delicate necklace" isn't a functional object. It's a texture of pixels. As Reddit's Stable Diffusion community has documented, this averaging approach makes it difficult for a model to conceptually understand object logic, the idea that a necklace must wrap around a neck coherently.
The prompt: "A 28-year-old woman smiling broadly, wearing small gold hoop earrings and a delicate necklace, professional headshot, natural window light."
Nano Banana 2 handled the smile well but fused one earring into the earlobe. The necklace chain became a solid gold bar in places. Teeth had a subtle doubling artifact on the lower row.
Flux 1.1 produced the best teeth of the three: natural gum line, realistic tooth spacing, correct light reflections off enamel. Earrings were present and symmetric but lacked fine detail. The necklace chain was impressively rendered with individual links visible.
DALL-E 3 delivered the cleanest overall composition with no fusion artifacts. But the smile looked slightly "posed" rather than natural. Earrings were perfectly symmetric, almost too perfect, triggering a subtle uncanny feeling. Teeth were clean but lacked the micro-detail of Flux's output.
The takeaway: If your use case involves accessories, jewelry, or open-mouth expressions, model choice matters enormously. This is exactly the kind of edge case that separates "cool demo" from production-ready output. For more on common AI headshot pitfalls like these, see our deep dive on how AI headshot generators handle glasses.
What These Results Mean for Your Projects
If You're Building an AI Portrait Product
Flux 1.1 offers the best balance of quality and prompt control out of the box. But Nano Banana 2's character consistency and API availability through Google make it the stronger choice for serialized content where a single character's face must stay identical across multiple scenes.
For full creative control, training a LoRA (Low-Rank Adaptation) on a Stable Diffusion model using 5 to 10 photos remains the only way to get AI to genuinely understand what a specific person looks like, as the r/generativeAI community has extensively documented. To understand how this process works in practice, read our explanation of how one selfie becomes 50 AI headshots.
If You're a Marketer or Content Creator
Need artifact-free consistency and good on-image text handling? The DALL-E 3 lineage wins. Need DSLR-quality photorealism? Flux is your model. And if copyright safety is a concern, Adobe Firefly offers a layer of trademark safety that none of these three provide.
If You're Building AI Headshots for Real People
Here's the honest truth: none of these base models are sufficient out-of-the-box for production-quality professional headshots. The gap between "impressive AI face" and "headshot you'd actually put on LinkedIn" requires specialized fine-tuning, post-processing pipelines, and quality control layers.
That gap is exactly what purpose-built tools are designed to fill. At Starkie AI, we take the best of what foundation models offer and layer on specialized training, quality control, and user experience that turns an impressive tech demo into a headshot you're actually proud to use. You can browse example outputs to see the difference for yourself.
The Speed of Progress Is Staggering
These three models represent roughly 18 months of progress. The weaknesses we've documented here, the plastic skin, the fused earrings, the dead-eye stare, will likely be resolved in the next generation. The question isn't whether AI can generate professional-quality faces. It's how quickly the tooling catches up to the raw model capabilities.
How to Run Your Own Face Generation Test
Want to replicate this comparison yourself? Here's how.
Step 1: Access Each Model
Step 2: Use Standardized Prompts
Following the taxonomy-driven prompt design recommended by Deccan AI, we structured our 12 prompts by varying age, gender, ethnicity, lighting, and expression. Here are four representative examples you can copy directly:
Build out your full set of 12 by sampling across young/middle-aged/older, male/female/non-binary, six ethnicities, three lighting conditions, and two expression types.
Step 3: Score Fairly
Step 4: Share Your Results
We'd love to see what you find. Tag @StarkieAI on social media with your own results, or drop them in the comments. Community-sourced data makes everyone's evaluations stronger.
The Bottom Line
AI face generation has reached a level of quality that would have seemed impossible two years ago. All three models can produce faces that are genuinely impressive. But the differences in their strengths and weaknesses are significant enough to matter for anyone building products or creating content.
Nano Banana 2 wins on fine-tunability, speed, and hair detail. Flux 1.1 wins on balanced realism and prompt adherence. DALL-E 3 wins on consistency and artifact avoidance.
None of them, however, replace a purpose-built pipeline when the stakes are high. A professional headshot that represents you or your brand needs more than a good foundation model. It needs specialized training, quality control, and the kind of polish that only comes from a tool designed specifically for that job.
That's the gap Starkie AI is designed to fill: taking the best of what these foundation models offer and adding the layers that turn raw output into a headshot you're actually proud to use.
Curious what a purpose-built AI headshot looks like compared to these raw model outputs? Try Starkie AI free and see the difference specialized fine-tuning makes.