Best AI for Generating Pictures of People in 2026

Generating a hundred portraits costs $3.36 on Google's Nano Banana 2.1 and $21.10 on OpenAI's GPT Image 2 at high quality, before you retry a single face.

That sixfold spread is the cheap part of the decision. The expensive parts are whether one face survives from picture to picture, whether you may use the result commercially, and whether the file admits it was made by a machine. This guide ranks the options on those three tests, for a reader who needs people in ads, product pages or course material, not novelty filters. Treat it as a map of the AI image generator category, cut by one job.

HokAI did not generate a test set of portraits for this piece. Every price and limit below comes from a vendor page opened on 26 October 2026 or from the dated HokAI record linked beside it, and a handful of claims are marked as gaps.

How to choose: three tests that matter more than realism

Realism stopped separating these tools some time ago. Each one on the list can produce a believable stranger on a good day. The difference shows up on day two, when you need the same person in a new pose, or when a lawyer asks where the picture came from.

Run each candidate through four checks, in this order:

• Identity hold. Can you feed it photos of one person and get that face back in a new scene? Google allows 14 reference images, 4 of them characters on Nano Banana 2.1.
• Rights. Does your plan include commercial use? Qwen-Image-2.1 weights are free for non-commercial use only.
• Provenance. Does every file say it is synthetic? Google states that all generated images include a SynthID watermark.
• Cost per keeper. Price per image divided by the share you keep.

The cost table below assumes three attempts per usable frame. That is our assumption, not a vendor figure, so swap in your own rate.

A reader who ignores the shortlist can still use these four checks on any tool, including ones released next month. The model leaderboard is a fair place to start a long list, but it ranks general quality and says nothing about faces.

The shortlist: one pick per job

Most roundups name one winner. People images are not one job, so the picks below split by what you are making.

The rest of this guide explains each row, then prices them side by side. Smart Match can narrow the list further if you describe your job in a sentence: the Smart Match tool filters the same directory by budget and use case.

Strangers at API scale: Nano Banana 2.1 and GPT Image 2

If the face does not need to belong to anyone in particular, you are buying a stock-photo replacement, and the two cheapest serious options are pay-per-image APIs.

Google's pricing page, read on 26 October 2026, bills Nano Banana 2.1 image output by the token. The Batch rate halves every line, and Google describes the model as built for conversational editing, so you can ask for a different expression on the same frame instead of rerolling.

OpenAI's GPT Image 2 is billed by token too, from $0.006 for a low-quality 1024-pixel square in HokAI's record of 30 August 2026, with no free tier. OpenAI's developer docs, read today, list a pinned snapshot dated 21 April 2026 and show the Build tier at 250,000 tokens per minute and 20 images per minute. That cap matters for batch work.

Its newer sibling, ChatGPT Images 2.5, tops Artificial Analysis's blind text-to-image arena as of September 2026, per its HokAI record. The record puts GPT Image 2 at high quality on 1171 Elo against 1179 for the 2.5 Sunburst snapshot, a small gap. Both 2.5 snapshots embed C2PA metadata and a SynthID watermark.

Our pick for a first batch of strangers is Nano Banana 2.1. It undercuts GPT Image 2 at medium and high quality, and Google recommends it for all new projects. GPT Image 2 wins only if you can live with low-quality drafts.

For a face you must keep: references and trained identities

The hardest request in this category is not a pretty face. It is the same face, forty times, in forty scenes.

There are three ways to get it, and they trade effort for fidelity:

1. Reference images in the prompt. Google's documentation lets Nano Banana 2.1 hold 4 characters and 10 objects. Nano Banana Pro holds 5 characters, 6 objects and 3 style references. No training step.
2. A trained identity. Higgsfield sells Soul ID and, per its HokAI record of 24 September 2026, asks for 20 or more photos of one person. Krea offers unlimited LoRA training on its Max plan.
3. Open weights you adapt yourself. Qwen-Image-2.1 takes several reference images and supports local edits by mask.

Method one is enough for a small cast of recurring characters, such as a mascot family or a course instructor persona. Methods two and three suit a brand ambassador who must look right across a year of posts.

Krea's Max plan includes unlimited LoRA training at $105 a month. Higgsfield's Basic plan costs $9 a month. We found no vendor publishing a measured identity-drift rate, so treat every "consistent" claim as unverified until you run your own ten-image test.

For ad-style people on a flat plan

Some teams do not want a meter running. They want a monthly fee and a library of looks. HokAI's records, which carry the dates shown, give the entry points in one place.

Firefly is the safest choice for brand work. The Firefly models are trained on licensed Adobe Stock material, per the same record, and files carry C2PA Content Credentials. Ideogram suits people holding signs, since its strength is legible text.

Midjourney's record describes its output as painterly and strong on anatomy. That is an editorial description, not a test result. Pick it for stylised portraits and character art, not for a passport-style headshot.

Enhancing a real photo is a different product

A large share of searches for "pictures of people" are really about fixing an existing photo. That is enhancement, not generation, and the risks differ.

Remini charges weekly in the US App Store, and its Pro plan at $9.99 a week comes to about $43 a month, per the 3 October 2026 listing. Its HokAI record warns that the face model can produce plausible detail instead of recovering the real one, which is a problem for any picture of a real person.

Topaz Photo AI gives more control over sharpening, and our photo editing and restoration roundup compares the two in depth.

Photoroom is the odd one out. Its Virtual Model feature puts uploaded clothing onto an AI-generated model, a direct fit for shops that need apparel on a body. If your real job is product photography, read our guide to AI image and video tools first.

Aging tools such as FutureFace are another corner. It is a white-label API sold to health and skincare brands at 299 GBP per 1,000 face transforms, not a general portrait generator.

Writing the prompt and reviewing the face

Prompt craft is cheaper than reruns, and the cheapest guidance is the vendor's own. Google's image-generation documentation, read today, gives a photorealistic template that asks for five things: the type of shot, the subject, the setting, the light and the camera angle with a lens. Adapted for a person, it looks like this:

A photorealistic head-and-shoulders shot of a woman in her 40s, laughing,
in a bright kitchen. Soft window light from the left.
Shot from eye level with an 85mm lens. Aspect ratio 4:5.

Specifics beat adjectives. A named lens and a named light direction constrain the output far more than "beautiful" or "professional". The docs show the same structure for a coral reef and a product shot, so it carries across subjects.

Then review every candidate against a short checklist. This is our own review habit, not a vendor feature, and it takes about 30 seconds per image:

• Hands and fingers. Count them. Five on each hand, no merged knuckles.
• Teeth and ears. Teeth should be individual, and both ears should match in shape.
• Jewellery and glasses. Earrings should be a pair, and frames should sit evenly on both sides.
• Text on clothing and badges. Zoom to 100 percent and read every letter.
• The background crowd. Faces in the distance often fail first, so crop them out or blur them.

Reject on the first failure and move on. Fixing a flawed face with repeated edits usually costs more than regenerating, because each edit is another billed image on the token-priced models above.

If the face must match a real person, the checklist changes. Compare against the source photos side by side, and have the person approve the result in writing before it goes anywhere public.

What a usable portrait costs

Sticker prices mislead because you pay for the rejects. The table assumes three attempts per keeper, our assumption, at the quality or size named.

Two rows deserve a note. Recraft V4.1 is priced per image in HokAI's record of 7 October 2026, which says all nine variants can be tested on a free plan. FLUX 3 comes from Black Forest Labs' pricing data, read on 18 October 2026.

Grok Imagine Image 2.0 has a flat $0.02 standalone tier, and the 2.0 model costs two to four times that depending on quality. xAI states that moderation stays active even where a permissive setting is exposed, per its record.

At a thousand keepers a month, Nano Banana 2.1 at 1K costs about $100 and GPT Image 2 at high quality about $633. That gap is real money for a stock-photo budget and noise for a campaign.

Data handling, consent and provenance for faces

Faces are personal data, and this is where a cheap tool becomes an expensive mistake.

Start with the training question. Google's pricing page lists Nano Banana 2.1 as not used to improve its products on the paid tier, which is the only tier that serves the model. The same table shows the opposite for the free tier column. Check the equivalent line on every other vendor before uploading a customer's photo.

Then consent. If your reference photos show a real person, you need their written permission for the likeness, whatever the tool's terms say. Microsoft's model cards for its MAI-Image-2.5 line describe filtering for depictions of real public figures, per HokAI's record, and the vendor's page now lists a newer 2.6 generation, so confirm which version you are buying. OpenAI published a system card for its 2.0 image model covering extra deepfake safeguards given its photorealism.

Finally provenance. SynthID and C2PA are different mechanisms. SynthID is an invisible watermark; C2PA is signed metadata that a screenshot or a re-save can strip. Google's docs state that all generated images include SynthID. Adobe's record lists C2PA Content Credentials on every Firefly asset. We could not confirm what, if anything, hosted output from Black Forest Labs or Alibaba Cloud embeds, so that stays a gap.

Who should pick which

The same four checks give different answers for different readers. Here is how we would route five common jobs.

A solo creator who needs a recurring host for a channel should start with reference images on Nano Banana 2.1, and move to a trained identity on Higgsfield only if the face drifts after a dozen outputs. The reason is simple: reference images cost nothing extra, while training adds a credit bill and a photo-collection chore.

An online shop that needs clothing on bodies should look at Photoroom's Virtual Model before any general generator, because it solves the specific job of placing uploaded apparel. A general model will invent a garment instead of showing yours.

A course author who needs stock-style classroom people can use the cheapest tokens on the table, since no one in the picture has to match anyone else. Spend the savings on checking hands and text on whiteboards.

An agency producing paid ads should default to Adobe Firefly, because licensed training data and Content Credentials answer the questions a client's legal team will ask in week one. A developer building people-generation into an app should prototype on a pay-per-image API and move to self-hosted weights only when volume justifies the engineering time.

Not sure which bucket you are in? The Smart Match tool asks about budget and use case, then returns directory entries ranked for that job.

Where the pricier Nano Banana Pro wins

The cheapest model is not the right pick for every portrait job, and the case against our own verdict is strongest for character work.

Google DeepMind positions Nano Banana Pro as the premium model for complex visual tasks and brand consistency. It holds 5 characters against 4 and takes 3 style references, which 2.1 does not. At $0.134 for a 1K or 2K image against $0.0336 for 2.1, it costs four times as much, but a production that needs five recurring people in one scene will save more in reroll time than it spends.

The objection to this guide's verdict is simple: if a single wrong face costs you a day of editing, the per-image price is irrelevant. That is true for hero images and brand ambassadors. It is not true for the bulk of social and catalogue work, which is why the 2.1 model stays our first pick. The Batch rate for Pro, $0.067 for a 1K or 2K image, closes half the gap when you can wait up to 24 hours.

Who should skip AI portraits and stay with a camera

Three groups should not use any tool here.

• Anyone depicting a named, real customer or employee as a testimonial. The picture implies a real endorsement. A generated face with a real name is a fabricated review.
• Teams in regulated advertising, such as finance and health, where rules on disclosing synthetic imagery differ by market. Ask counsel before the first campaign.
• Anyone who needs a verifiable record of a real event, including news, evidence and ID photos.

If you only need three or four portraits, a stock library or a half-day with a photographer is cheaper than learning a workflow and checking its terms. The image-to-video guide covers the free end if you just want to animate an existing photo.

What would flip our pick

Three developments would reorder the shortlist.

First, a vendor publishing a measured identity-drift rate. Until then, consistency claims are marketing. Second, a price move: Google's pricing page lists Nano Banana 2 at $0.067 per 1K image and its 2.1 successor at $0.0336, half the price, and a similar cut from OpenAI would change the table. Third, a provenance rule. If your market starts requiring visible disclosure of synthetic people, tools that embed C2PA by default gain an edge over those that do not.

Watch Microsoft's MAI line and the open-weight releases from Alibaba and Black Forest Labs in the next quarter, since both could undercut the API prices above. Prices move monthly, so check each one against the vendor page before you buy. Compare this list with our full image generator roundup before you commit, and rerun the ten-image identity test on whichever model you pick.

Frequently asked questions

What is the best AI for generating pictures of people?

For strangers at the lowest per-image price, Nano Banana 2.1 at $0.0336 for a 1K image is our first pick. For recurring characters, Nano Banana Pro holds 5 characters against 4. For licensed training data and Content Credentials, Adobe Firefly starts at $9.99 a month.

How much does a realistic AI portrait cost?

Per image, the range in this guide runs from $0.006 for a low-quality GPT Image 2 square to $0.211 at high quality. Nano Banana 2.1 sits at $0.0336 for 1K. Flat plans start around $9 to $10 a month, and you should budget for several attempts per usable face.

Can AI keep the same face across many images?

Partly. Google allows up to 14 reference images, with 4 characters on Nano Banana 2.1 and 5 on Pro. Trained identities, such as Higgsfield Soul ID, ask for at least 20 photos of one person. We found no vendor that publishes a measured identity-drift rate, so test ten images yourself.

Can I use AI-generated people commercially?

It depends on the plan and the model. Qwen-Image-2.1 weights are free for non-commercial use only, and commercial use needs a separate licence. Adobe says Firefly models are trained on licensed Adobe Stock material. If a real person is depicted, get their written consent whatever the tool allows.

Do AI-generated pictures of people carry a watermark?

Often. Google states that all generated images include a SynthID watermark, and the ChatGPT Images 2.5 and Adobe Firefly records list C2PA metadata. C2PA metadata can be stripped by a screenshot or a re-save, so it proves provenance only while the file stays intact.

Covered in this guide

• adobe-firefly: Adobe Firefly generates commercially safe AI images and video inside Photoshop and Illustrator, trained exclusively on licensed content for full commercial-use IP indemnification.
• alibaba-cloud: Alibaba Cloud, founded 2009, is China's top cloud provider (35% share) and builds the Qwen AI models, which passed 1 billion Hugging Face downloads in 2026.
• black-forest-labs: Black Forest Labs builds FLUX 3, a multimodal image, video and audio model family, plus open-weights Klein and Action models. Freiburg, Germany.
• chatgpt-images-2.5: OpenAI's ChatGPT Images 2.5, released September 8, 2026, adds Sketch reference drawing and comment-based edits across two API snapshots, Flare and Sunburst.
• flux-3: FLUX 3 is a video, image, and audio model from Black Forest Labs, launched July 2026, built on a shared Self-Flow architecture.
• futureface: FutureFace turns a photo into 10 and 20 year facial aging projections from 30+ lifestyle inputs, used by 100+ partner brands.
• gemini-3-pro-image: Nano Banana Pro (Gemini 3 Pro Image), released Nov 20, 2025 by Google DeepMind, generates 4K images with 94% text accuracy and Search-grounded facts.
• google-deepmind: Alphabet's AI research lab, created from DeepMind and Google Brain. It makes the Gemini, Gemma and AlphaFold models, and its newest frontier model is still limited to partners.
• gpt-image-2: OpenAI's April 2026 image flagship integrates O-series reasoning for 99% text accuracy, natural language inpainting, and 2K resolution via a token-based API.
• grok-imagine-image-2: xAI's second-gen image model, live in the Grok app since August 7, 2026, with region-precise editing and a #2 global Arena ranking.
• higgsfield: AI video and image studio, used in 238 countries, that runs Kling, Veo, Seedance and Sora plus its own camera and character tools on one credit plan.
• ideogram: AI image generator that actually gets text right, useful for anything combining words and visuals.
• krea: An AI suite for creatives that generates, enhances, and edits images, videos, and 3D assets in one browser-based platform.
• leonardoai: AI image, video, and 3D asset generation for creators and teams
• mai-image-2.5: MAI-Image-2.5 is Microsoft AI's June 2026 diffusion model, ranked #3 for text-to-image and #2 for image editing on Arena.
• nano-banana-2.1: Google's image generation and editing model in the Gemini API, with 1K to 4K output and multi-image reference mixing, generally available since October 2026.
• photoroom: Photoroom is an AI photo editor with 1,000+ ecommerce templates, instant background removal, and an API built for Netflix- and Shopify-scale sellers.
• qwen-image-2.1: Qwen-Image-2.1 is Alibaba's 7B open-weight diffusion model, released in September 2026 with native transparent image output and multi-image editing.
• recraft-v4.1: Recraft V4.1 is Recraft's May 2026 image model family: nine variants priced per image from a Flash tier to Pro vectors, raster or native SVG, all testable on the free plan.
• remini: Sharpens blurry and old photos and short videos for non-experts in one upload, on iOS, Android and the web, in 17 languages on iOS.
• topaz-photo-ai: Topaz Photo AI sharpens, denoises, and upscales images up to 16x. Trusted by 1.5M+ photographers at Apple and NASA. Annual plans from $199, no free tier.

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