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MiniMax Image-01vs.Stable Diffusion XL 1.0

Which one is better?

In our own test, MiniMax Image-01 leads. Neither is in the arena. Neither has an API price.

Test images

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

MiniMax Image-01: Typography and layout, first image from the fixed test run
MiniMax Image-0137 out of 100
Stable Diffusion XL 1.0: Typography and layout, first image from the fixed test run
Stable Diffusion XL 1.05 out of 100

Result for “Typography and layout”:MiniMax Image-01 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

MiniMax Image-01: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.010000, generated Nov 1, 2026, 8:43 PM. Source

Stable Diffusion XL 1.0: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/stable-diffusion-xl-base-1.0@462165984030d82259a11f4367a4eed129e94a7b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0, seed=268378870826470633; 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, 6:07 PM. Source

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

MiniMax Image-01 image SHA 256: b99793e66d1eb69880872d1865f25456b6588dc658aa46906b7636da46f63320

Stable Diffusion XL 1.0 image SHA 256: 26d3c3d3e687614e24dd12191246bc1d96c1576d31f9211840a3e02b64d4d507

Infographic

MiniMax Image-01: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.010000, generated Nov 1, 2026, 8:43 PM. Source

Stable Diffusion XL 1.0: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/stable-diffusion-xl-base-1.0@462165984030d82259a11f4367a4eed129e94a7b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0, seed=5455008934753613262; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:08 PM. Source

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

MiniMax Image-01 image SHA 256: 31b08957fb9eda45525c50935fd332cdbe5a5150a4ed57dead5254e667b2b0de

Stable Diffusion XL 1.0 image SHA 256: ebfe8d1c223478c94ff1973ab2600cd4f696b40b8c3388ca40f26f52c83afdb2

Product photography

MiniMax Image-01: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.010000, generated Nov 1, 2026, 8:43 PM. Source

Stable Diffusion XL 1.0: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/stable-diffusion-xl-base-1.0@462165984030d82259a11f4367a4eed129e94a7b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0, seed=490593220006868735; 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, 6:06 PM. Source

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

MiniMax Image-01 image SHA 256: a8986bd9a5e8262b5705f8a7ba5259968371a3bc8e14394b992736f59e45385b

Stable Diffusion XL 1.0 image SHA 256: f59af9c84f6fbb3e4ca34bf87ebb0f7c088e8caa520fa4cdf815a96d1bffda83

Typography and layout

MiniMax Image-01: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.010000, generated Nov 1, 2026, 8:42 PM. Source

Stable Diffusion XL 1.0: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/stable-diffusion-xl-base-1.0@462165984030d82259a11f4367a4eed129e94a7b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0, seed=253938565953989943; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:05 PM. Source

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

MiniMax Image-01 image SHA 256: 020129f518174bf171032cc5cb7b2234782f4e1d7b1e685d473388094630a107

Stable Diffusion XL 1.0 image SHA 256: c81e5d0d70afcb53fcffb51a0d548041fd0b33df300aed14b8ecd354aaaaec4e

The numbers at a glance

Arena ratingfrom votes by users · higher is better
n/a

MiniMax Image-01

not in the arena

n/a

Stable Diffusion XL 1.0

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better
31

MiniMax Image-01

Rank 82 of 108

13

Stable Diffusion XL 1.0

Rank 100 of 108

Price per imagedollars through the API · lower is better
n/a

MiniMax Image-01

no API price

n/a

Stable Diffusion XL 1.0

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.

MiniMax Image-01

Subscription
n/a No plan in Germany names exactly this model.
Via API
No API price known. Access: API.
Weights
unknown.

Stable Diffusion XL 1.0

Subscription
n/a No plan in Germany names exactly this model.
Via API
No API price known. Access: API, 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 · 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.

KennzahlMiniMax Image-01Stable Diffusion XL 1.0
Ratings
Arena ratingfrom votes by users, higher is bettern/an/a
Arena rankn/an/a
Arena votesmore votes, a safer ratingn/an/a
GraduallyBenchour own rating, 0 to 1003113
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition375
Product photographyMaterials, reflections, and fine details3013
Character and detailAnatomy, clothing, and spatial consistency3328
InfographicNumbers, labels, and visual organization265
API price
Per imagelower is bettern/an/a
100 imagesn/an/a
Profile
ReleasedFeb 28, 2025n/a
Weightsopen means: runs locallyunknownopen
Lifecycleno noticeOlder open checkpoint; provider still lists it in its legacy model overview
AccessAPIAPI, local
Parametersnot publishednot published
Largest outputnot consistently publishednot consistently published
Plans in Germanynaming exactly this modeln/an/a

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

Sources: MiniMax Image-01 model source (verified Nov 1, 2026) · Stable Diffusion XL 1.0 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 MiniMax Image-01 and Stable Diffusion XL 1.0 from the same arena and category. Older model versions are not used as substitutes.

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