Name: AI Image Generation Price Index 2026
Creator: DrAI
Published: 2026-08-16
License: https://ai.dr-ai.top/terms

Complete image generation price table

List prices in USD per generated image (standard output size). Midjourney V7 is an estimate from its subscription tiers. Prices verified 2026-08-16.

Model	Provider	Resolution	$ / image	Highlights
GPT Image 2	OpenAI	2048×2048	$0.08	Premium tier; highest fidelity
Midjourney V7 est.	Midjourney	2048×2048	$0.06	Artistic output, community ecosystem
Ideogram V3	Ideogram	1024×1024	$0.05	Best-in-class text rendering
GPT Image 1.5	OpenAI	1536×1024	$0.04	Mid tier; strong editing
DALL-E 3 1024	OpenAI	1024×1024	$0.04	Classic quality; prompt adherence
FLUX Pro	Black Forest Labs	1024×1024	$0.04	Photorealism; text rendering
Stable Diffusion XL	Stability AI	1024×1024	$0.03	open Style control; fine-tunable
Nano Banana	Google	1024×1024	$0.03	Photo editing & inpainting
GPT Image 1 entry	OpenAI	1024×1024	$0.02	Fast, reliable baseline
FLUX Schnell	Black Forest Labs	1024×1024	$0.01	open Fast open-weights generation
SDXL Turbo cheapest	Stability AI	512×512	$0.01	open Real-time iteration; draft boards
Stable Image Ultra	Stability AI	1024×1024	$0.08	Top open-weights quality

Resolution & quality price ladder

Image pricing is a ladder, not a dial: each step up in resolution roughly doubles the effective cost. Know which rung your use case needs.

Tier	Resolution	Price range	Typical models	Best for
Draft	512×512	$0.01	SDXL Turbo, FLUX Schnell	Storyboards, concept iteration, real-time
Standard	1024×1024	$0.02–$0.04	GPT Image 1, DALL-E 3, FLUX Pro, SDXL, Ideogram, Nano Banana	Social content, marketing, product shots
Premium	1536–2048	$0.04–$0.08	GPT Image 1.5 / 2, Midjourney V7	Hero images, print, e-commerce detail

How to read this

4× the pixels ≈ 2–3× the price: going from 1024×1024 to 2048×2048 quadruples pixels but typically costs 2–4× — resolution is the single biggest price driver.
1024 is the sweet spot: it clears social-platform compression, print at 300 DPI up to A5, and e-commerce zoom — most teams never need native 2K generation.
Upscale later, not at generation: AI upscalers add ~$0.005–$0.01 per image. Generating at 1024 then upscaling beats native 2K generation on cost for one-off hero assets.
Draft-tier is underrated: a $0.01 iteration loop (SDXL Turbo) lets a designer burn 50 variants for $0.50 before spending $0.04 on the final render.

Batch generation cost examples

Real volumes, real totals — from a startup's first campaign to an e-commerce catalog.

Volume	Model	Per image	Total cost
1,000 images	SDXL Turbo	$0.01	$10
1,000 images	GPT Image 1	$0.02	$20
10,000 images	SDXL	$0.03	$300
10,000 images	FLUX Pro	$0.04	$400
10,000 images	DALL-E 3	$0.04	$400
100,000 images	SDXL Turbo	$0.01	$1,000
100,000 images	GPT Image 1.5	$0.04	$4,000
100,000 images	GPT Image 2	$0.08	$8,000

The tier decision is a business decision

8× spread: at 100K images/month, draft-tier costs $1,000 while premium costs $8,000. The gap pays for a designer's salary — not the pixels.
Product catalogs: 10K SKUs with 3 variants each at SDXL ($0.03) = $900 one-time, vs FLUX Pro at $1,200. Reuse across seasons changes the math entirely.
Open weights change the ceiling: self-hosted SDXL/FLUX Schnell on a single GPU sustain ~1 image/sec — 100K images cost only electricity (~$30–60).
DrAI note: image generation is included in DrAI's flat subscription — no per-image metering. See pricing.

Cache & reuse: asset library vs generate-every-time

The cheapest image is the one you don't regenerate. Teams that treat generations as a library instead of a firehose cut image spend by 25–40%.

Reuse strategies that actually work

Style-locked master sets: generate 20–50 hero images once per campaign, then composite text, crops and variants from them — never re-run the same prompt family.
Composite workflows: generate the subject on a plain background once, then cheaply composite onto scenes. Backgrounds and layouts stop being per-image costs.
Template prompts with stable seeds: keep the style seed fixed and swap only the product/SKU token — each new variant reuses the style work already paid for.
CDN + cache-first delivery: identical requests should never be re-billed. Cache generated assets at the edge; a 30–50% cache hit rate on editorial workloads is normal.
Inpainting over regeneration: fixing a detail with Nano Banana / GPT Image editing costs the same $0.02–$0.04 but keeps brand consistency — regenerating from scratch drifts the whole image.
Budget rule of thumb: if an image was generated more than once in the last 90 days, it belongs in the asset library. Library-first teams spend 25–40% less per delivered asset.

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