AI Models for Photorealistic People in 2026: A Practical Comparison

There is no honest universal winner for photorealistic people. A model that produces an excellent close portrait can still struggle with a full-length pose, readable clothing details, or the same person across several scenes. The useful question is narrower: which model is best for this image, under these settings, at an acceptable cost and speed?

This guide gives you a repeatable test you can run inside Lucidpic as models change. It deliberately avoids a permanent leaderboard. Image models, safety systems, and default prompt processing are updated too often for a one-off ranking to remain reliable.

Contents

• Test prompt and settings
• What to inspect
• Observed strengths
• Choosing for a workflow
• A three-prompt benchmark
• How to record results

The identical test prompt

Use the same prompt for every model:

Editorial full-body photograph of an adult woman in her early thirties, short dark wavy hair, olive linen suit and white trainers, standing outside a quiet modern gallery after light rain, natural overcast daylight, realistic skin texture, relaxed posture, 50mm photography, vertical 4:5 composition.

Keep the aspect ratio at 4:5 and use the same number of outputs per model. Leave seed handling at the model's supported default unless every model exposes comparable seed control. Disable prompt rewriting where the interface allows it. Record the exact model version and test date. Do not cherry-pick ten generations from one model and compare the best of them with a single generation from another.

What to inspect

Evaluate each output at thumbnail size and full resolution:

Area	Question
Person	Does the age, hair, wardrobe, and overall presentation match?
Face and skin	Do features, pores, hair edges, and expression feel coherent?
Full body	Are posture, hands, feet, and clothing construction plausible?
Scene	Did the model include the gallery, wet ground, and overcast light?
Photography	Does the image read as a 50mm editorial photograph rather than illustration?
Editability	Is there enough clean structure for refinement or image-to-video?

Score each area from 1 to 5, but keep written notes. A single number cannot explain whether a low score came from an extra finger, the wrong jacket, a waxy face, or a background that ignored the prompt.

Research note: realism is not the same as correctness

Human viewers are not dependable synthetic-image detectors. In a 2022 PNAS study, 315 participants classified real and AI-synthesized faces with an average accuracy of 48.2%, slightly below chance. Training with feedback improved accuracy to 59%, but did not make the task reliable. The authors' title states the result plainly: “AI-synthesized faces are indistinguishable from real faces and more trustworthy” (Nightingale & Farid, 2022).

That finding is a reason to separate looks real from is correct. A face can appear convincing while the image still misses the requested age, wardrobe, product, relationship, or setting. Recent benchmark research makes the same distinction: “semantic alignment ... remains challenging, especially under compositional prompts with multiple entities, attributes, and relations” (Chen et al., 2025). This is why the checklist tests individual requirements instead of relying on one realism score.

Document observed strengths, not a fake leaderboard

For each model available in Studio, record observations in plain language. One may produce natural-looking skin but simplify the suit. Another may follow the wardrobe and architecture closely but create a conspicuously polished finish. A third may be better at preserving a reference image while being less imaginative from text alone.

Those are task-specific observations, not permanent rankings. Even first-party documentation describes different models in terms of intended trade-offs. Google's current Gemini image documentation, for example, distinguishes its image models by speed, production quality, and cost rather than declaring one model best for every job. It also states that its generated images include SynthID. See Google's image-generation documentation for the current model descriptions.

Use more than one prompt

A portrait-only test rewards portrait specialists. Add at least two more prompts before choosing a default:

1. Full body: an adult subject walking, visible hands and footwear, layered clothing, and a specified environment.
2. Two people: two clearly described adults interacting naturally, with distinct wardrobe and positions.
3. Reference edit: retain the subject and framing while changing one controlled variable, such as the background or jacket colour.

Use the same evaluation sheet for all three. This exposes a common pattern: a model may lead on facial texture yet fall behind on composition, multi-person scenes, or precise edits.

Choose the model for the workflow

For a one-off portrait, prioritize face quality and photographic texture. For the AI person generator, consider whether the result is a strong foundation for a reusable character. For full-body images, inspect hands, footwear, clothing, and stance. For an AI photoshoot, test whether the model follows a repeatable art direction across several prompts.

If the still will become video, leave room for motion and avoid fragile small details. The strongest single image is not always the most stable source for image to video. A clean silhouette, coherent hands, and simple background geometry are often worth more than extra surface detail.

Record enough detail to repeat the test

Save the prompt, model name and version, aspect ratio, quality setting, reference images, seed where supported, generation date, and unedited outputs. Keep failures as well as winners. Without the failures, you cannot tell whether a result was repeatable or lucky.

When reference images contain a real person, use images you have permission to process. The U.S. Copyright Office's report on digital replicas describes the legal and policy risks created when realistic images or recordings falsely depict an identifiable person. Laws differ by location, but consent is the sensible baseline everywhere.

The repeatable conclusion

Run the same prompt set, save the settings, inspect the same criteria, and choose the model whose weaknesses are easiest to manage for the current job. Re-run the benchmark when a model version or important setting changes. “Best” should be the conclusion of a documented test, not a label copied from a launch announcement.

Research cited

• Chen, Z., Ni, T., & Yang, Y. (2025). CritiFusion: Semantic critique and spectral alignment for faithful text-to-image generation. https://doi.org/10.48550/arxiv.2512.22681
• Nightingale, S. J., & Farid, H. (2022). AI-synthesized faces are indistinguishable from real faces and more trustworthy. Proceedings of the National Academy of Sciences, 119(8). https://doi.org/10.1073/pnas.2120481119