MAI-Image-2.5vs.SDXL Turbo

Which one is better?

In our own test, MAI-Image-2.5 leads. SDXL Turbo has no arena rating yet. SDXL Turbo has no API price.

Test images

Same task, the first image of each. The task with the biggest difference is preselected.

MAI-Image-2.5: Typography and layout, first image from the fixed test run — MAI-Image-2.582 out of 100

SDXL Turbo: Typography and layout, first image from the fixed test run — SDXL Turbo3 out of 100

Result for “Typography and layout”:MAI-Image-2.5 ahead

Prompt given to both modelsCreate a square editorial poster for an imaginary night train called MONDFALTER. Show the exact German headline MONDFALTER and the exact subline BERLIN NACH LISSABON. Use a restrained midnight-blue and warm-cream palette, one stylized moth, strong Swiss-grid typography, generous negative space, and no additional words or logos.

What the rating looks at: Legible text, hierarchy, and composition.

“Ahead”: better in all three rating rounds. “Slightly ahead”: a higher value, but not in every round.

Test protocol of the images

Each test attempt uses the same archived prompt and one request. The first output of that attempt counts. Repeats after technical test failures are disclosed for the affected images. Files are normalized to 1,024 × 1,024 pixels without cropping. Provider resolution, randomness, quality tier and prompt processing can still differ. The images are a practical visual test, not a reconstruction of the arena configuration.

Character and detail

MAI-Image-2.5: openrouter, aspect ratio 1:1, provider default 1K resolution, n=1, seed=provider random, actual cost $0.048508, generated Aug 14, 2026, 1:26 PM. Source

SDXL Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sdxl-turbo@71153311d3dbb46851df1931d3ca6e939de83304; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=660064698563452219; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE; native tokenizer truncation: tokenizer 80 to 77 tokens, tokenizer_2 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 6:23 PM. Source

Protocol gradually-image-comparison-v1. Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6

MAI-Image-2.5 image SHA 256: ca1c58688ae4601975d7e1f7b58bb43a0a999e736f610d9f51cd11e06284bced

SDXL Turbo image SHA 256: 9ee6443f4690649e93bdca408e5e8d317b312244636a3af47a02fe14eaf6c20c

Infographic

MAI-Image-2.5: openrouter, aspect ratio 1:1, provider default 1K resolution, n=1, seed=provider random, actual cost $0.048538, generated Aug 14, 2026, 1:26 PM. Source

SDXL Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sdxl-turbo@71153311d3dbb46851df1931d3ca6e939de83304; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=6060807684445796734; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:23 PM. Source

Protocol gradually-image-comparison-v1. Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f

MAI-Image-2.5 image SHA 256: eaf00a906231e8859f98ef5dc2cce7e8017797819aea453305a4ef0e970d4dbf

SDXL Turbo image SHA 256: 20395d858252aa131f235baa1f514039503a52dfec958c8f5b299db708bf4090

Product photography

MAI-Image-2.5: openrouter, aspect ratio 1:1, provider default 1K resolution, n=1, seed=provider random, actual cost $0.048513, generated Aug 14, 2026, 1:25 PM. Source

SDXL Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sdxl-turbo@71153311d3dbb46851df1931d3ca6e939de83304; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=4654331835179858246; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE; native tokenizer truncation: tokenizer 80 to 77 tokens, tokenizer_2 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 6:22 PM. Source

Protocol gradually-image-comparison-v1. Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3

MAI-Image-2.5 image SHA 256: 67d4930b711fac61482231c4bc270cfcdb41c1b0daf653045781fb2126e237dd

SDXL Turbo image SHA 256: 75e5b85283110815e51a0470c937c3e42407244b50d918a3c0c54e10998a4b4c

Typography and layout

MAI-Image-2.5: openrouter, aspect ratio 1:1, provider default 1K resolution, n=1, seed=provider random, actual cost $0.048473, generated Aug 14, 2026, 2:03 PM. Source

SDXL Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sdxl-turbo@71153311d3dbb46851df1931d3ca6e939de83304; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=1511362670078321145; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:21 PM. Source

Protocol gradually-image-comparison-v1. Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419

MAI-Image-2.5 image SHA 256: 4c8a60555b57b8c3b5ebaa131371d1bba9d31bfafe7aaa772554969f7c28cb08

SDXL Turbo image SHA 256: 963088c72f75c98a95fd71be5f8d6e14fdf17061ae984f1109522a39be24ac76

The numbers at a glance

Arena ratingfrom votes by users · higher is better

1,253

MAI-Image-2.5

Rank 9 of 21

n/a

SDXL Turbo

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

MAI-Image-2.5

Rank 26 of 108

SDXL Turbo

Rank 105 of 108

Price per imagedollars through the API · lower is better

$0.048

MAI-Image-2.5

fairly cheap

n/a

SDXL Turbo

no API price

Ranks among the 21 image models with an arena rating and the 108 with a GraduallyBench run; prices compared with 28 models with an API price.

MAI-Image-2.5

Subscription: No Microsoft AI subscription includes this model. Through other providers from €19.04 a month (Magnific Premium, with limited credits). 6 plans in Germany.
Via API: $0.048 per image, 100 images $4.81.

SDXL Turbo

Subscription: n/a No plan in Germany names exactly this model.
Via API: No API price known. Access: local.
License: Open weights.

Subscriptions only count plans that name exactly this model. Subscription prices include VAT, API prices are in dollars.

Arena rating (LMArena) from Nov 6, 2026 · GraduallyBench from Nov 3, 2026 · API prices from Nov 6, 2026 · Subscription database from Sep 29, 2026 (38 days old)

All data side by side

The better value is bold. For ratings only when the gap is clear.

Kennzahl	MAI-Image-2.5	SDXL Turbo
Ratings
Arena ratingfrom votes by users, higher is better	1,253	n/a
Arena rank	Rank 9 of 21	n/a
Arena votesmore votes, a safer rating	61,222	n/a
GraduallyBenchour own rating, 0 to 100	69	4
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	82	3
Product photographyMaterials, reflections, and fine details	70	3
Character and detailAnatomy, clothing, and spatial consistency	55	3
InfographicNumbers, labels, and visual organization	69	8
API price
Per imagelower is better	$0.048	n/a
100 images	$4.81	n/a
Profile
Released	June 2026	n/a
Weightsopen means: runs locally	proprietary	open
Lifecycle	Preview in Microsoft Foundry	Older open fast checkpoint; no active hosted API ID claimed in the reviewed model list
Access	API, Web app	local
Parameters	20 billion	not published
Largest output	1,048,576 pixels	not consistently published
Plans in Germanynaming exactly this model	6	n/a

API prices for one image at 1,024 × 1,024 pixels according to Artificial Analysis.

Sources: MAI-Image-2.5 model source, status source (verified Aug 15, 2026) · SDXL Turbo model source, status source (verified Nov 1, 2026)

Arena ratings by category

From blind votes on LMArena. “Ahead” means the lead is clear.

No shared arena score in the stored snapshot

The stored benchmark database contains no measurement for MAI-Image-2.5 and SDXL Turbo from the same arena and category. Older model versions are not used as substitutes.

More comparisons

Compare MAI-Image-2.5 and SDXL Turbo with the models people compare them with most often.

Compare another matchup

First image modelSecond image model

The most popular comparisons

1MAI-Image-2.5 vs. Nano Banana 2.1