MAI-Image-2.5 Flashvs.MOSS-Image-Base

Which one is better?

In our own test, MAI-Image-2.5 Flash leads. Neither is in the arena. MOSS-Image-Base 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 Flash: Character and detail, first image from the fixed test run — MAI-Image-2.5 Flash68 out of 100

MOSS-Image-Base: Character and detail, first image from the fixed test run — MOSS-Image-Base46 out of 100

Result for “Character and detail”:MAI-Image-2.5 Flash slightly ahead

Prompt given to both modelsCreate a square cinematic full-body portrait of a fictional bicycle courier waiting under a transparent umbrella at a rainy tram stop in Hamburg at blue hour. She wears a mustard raincoat, navy trousers, red sneakers, a silver helmet, and carries a teal messenger bag. Keep both hands visible, render the bicycle correctly, use realistic wet-street reflections, and include no readable brand names.

What the rating looks at: Anatomy, clothing, and spatial consistency.

“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 Flash: manual, Official Bing Image Creator web app; MAI-Image-2.5-Flash; one request; 1:1; first result; free guest generation; 2026-08-14; https://www.bing.com/images/create/ai-image-generator, generated Aug 14, 2026, 4:23 PM. Source

MOSS-Image-Base: manual, local PyTorch 2.13.0, Diffusers 0.41.0; OpenMOSS-Team/MOSS-Image-Base@85975793d8b3d432f1b6e27f352abaa4c2a4c0d3; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.0, seed=921965437095743280; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; unconditional skip layers=[22]; CPU operator fallback permitted, actual fallback use was not measured; official experimental MPS compatibility runtime OpenMOSS/MOSS-Image@1961ca3d111b828e65278656cde0c8f94484f4dd, generated Nov 2, 2026, 10:39 PM. Source

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

MAI-Image-2.5 Flash image SHA 256: a2637943faf5ae5210c69f645f96c70ea0a1d267c38a64705810947d2ba78dc8

MOSS-Image-Base image SHA 256: 313d95f3e7766e861d46b0563c81bb2a02782708004c24c7aab0c46e8587ff83

Infographic

MOSS-Image-Base: manual, local PyTorch 2.13.0, Diffusers 0.41.0; OpenMOSS-Team/MOSS-Image-Base@85975793d8b3d432f1b6e27f352abaa4c2a4c0d3; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.0, seed=2567672344276262662; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; unconditional skip layers=[22]; CPU operator fallback permitted, actual fallback use was not measured; official experimental MPS compatibility runtime OpenMOSS/MOSS-Image@1961ca3d111b828e65278656cde0c8f94484f4dd, generated Nov 2, 2026, 10:43 PM. Source

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

MAI-Image-2.5 Flash image SHA 256: 6af00e502934a24a9e535b3f72d9b1ca545a984fc463e785ffe796a700e015b3

MOSS-Image-Base image SHA 256: 3175582007cf377490ab51f73a57ebddb312f523ac5bfb54ae7598e72eb3edca

Product photography

MOSS-Image-Base: manual, local PyTorch 2.13.0, Diffusers 0.41.0; OpenMOSS-Team/MOSS-Image-Base@85975793d8b3d432f1b6e27f352abaa4c2a4c0d3; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.0, seed=8912274556852986311; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; unconditional skip layers=[22]; CPU operator fallback permitted, actual fallback use was not measured; official experimental MPS compatibility runtime OpenMOSS/MOSS-Image@1961ca3d111b828e65278656cde0c8f94484f4dd, generated Nov 2, 2026, 10:35 PM. Source

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

MAI-Image-2.5 Flash image SHA 256: 9cb652604f7c64d8ebaa4303e924155a1f45581a940045be12775419e635bd8f

MOSS-Image-Base image SHA 256: af9ea47eb1554bcaaaadf8860fecfa5bb69095a58bf346e3d9a73be8240a2a7c

Typography and layout

MOSS-Image-Base: manual, local PyTorch 2.13.0, Diffusers 0.41.0; OpenMOSS-Team/MOSS-Image-Base@85975793d8b3d432f1b6e27f352abaa4c2a4c0d3; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.0, seed=9219943416610765538; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; unconditional skip layers=[22]; CPU operator fallback permitted, actual fallback use was not measured; official experimental MPS compatibility runtime OpenMOSS/MOSS-Image@1961ca3d111b828e65278656cde0c8f94484f4dd, generated Nov 2, 2026, 10:31 PM. Source

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

MAI-Image-2.5 Flash image SHA 256: 185a852b71d68ea37a41091b57f3ec02829b2d1e87fd1045939ffa0f8e4c3cf8

MOSS-Image-Base image SHA 256: e87b1d9d7aa09487c6b9be505746dacb2f3be551f29a5d85424a44a2a88089c5

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

MAI-Image-2.5 Flash

not in the arena

n/a

MOSS-Image-Base

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

MAI-Image-2.5 Flash

Rank 24 of 108

MOSS-Image-Base

Rank 61 of 108

Price per imagedollars through the API · lower is better

$0.02

MAI-Image-2.5 Flash

cheap

n/a

MOSS-Image-Base

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 Flash

Subscription: n/a No plan in Germany names exactly this model.
Via API: $0.02 per image, 100 images $2.

MOSS-Image-Base

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.

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 Flash	MOSS-Image-Base
Ratings
Arena ratingfrom votes by users, higher is better	n/a	n/a
Arena rank	n/a	n/a
Arena votesmore votes, a safer rating	n/a	n/a
GraduallyBenchour own rating, 0 to 100	69	51
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	70	51
Product photographyMaterials, reflections, and fine details	58	36
Character and detailAnatomy, clothing, and spatial consistency	68	46
InfographicNumbers, labels, and visual organization	81	71
API price
Per imagelower is better	$0.02	n/a
100 images	$2	n/a
Profile
Released	June 2026	Oct 16, 2026
Weightsopen means: runs locally	proprietary	open
Lifecycle	Preview in Microsoft Foundry	no notice
Access	API, Web app	local
Parameters	20 billion	5 billion
Largest output	1,048,576 pixels	not consistently published
Plans in Germanynaming exactly this model	n/a	n/a

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

Sources: MAI-Image-2.5 Flash model source, status source (verified Sep 22, 2026) · MOSS-Image-Base model 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 Flash and MOSS-Image-Base from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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