Stable Diffusion XL 1.0vs.BAGEL-7B-MoT

Which one is better?

In our own test both are on par. 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.

Stable Diffusion XL 1.0: Product photography, first image from the fixed test run — Stable Diffusion XL 1.013 out of 100

BAGEL-7B-MoT: Product photography, first image from the fixed test run — BAGEL-7B-MoT88 out of 100

Result for “Product photography”:BAGEL-7B-MoT ahead

Prompt given to both modelsCreate a square premium product photograph of a brushed titanium wristwatch standing upright on dark green marble. The watch has a cream dial, thin black hands set to 10:10, twelve distinct hour markers, a realistic crown, and a dark brown leather strap. Soft window light from the left, controlled reflections, shallow depth of field, no text, no logo, no extra objects.

What the rating looks at: Materials, reflections, and fine details.

“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

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

BAGEL-7B-MoT: fal, provider-default image size; one output sampled; use_thought=false, one request, first output, seed=1668381318, Fal schema does not expose image size or image count fields, estimated cost $0.100000, generated Nov 2, 2026, 3:22 PM. Source

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

Stable Diffusion XL 1.0 image SHA 256: 26d3c3d3e687614e24dd12191246bc1d96c1576d31f9211840a3e02b64d4d507

BAGEL-7B-MoT image SHA 256: f9a99e0e983d8731377afdf880911fc993d516b55c18059fd5cb3390fd01fd31

Infographic

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

BAGEL-7B-MoT: fal, provider-default image size; one output sampled; use_thought=false, one request, first output, seed=1401806471, Fal schema does not expose image size or image count fields, estimated cost $0.100000, generated Nov 2, 2026, 3:23 PM. Source

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

Stable Diffusion XL 1.0 image SHA 256: ebfe8d1c223478c94ff1973ab2600cd4f696b40b8c3388ca40f26f52c83afdb2

BAGEL-7B-MoT image SHA 256: fd19fd4a68dfc264705c92209fcf45e3c8be4384c02433888b066683ee57c2ba

Product photography

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

BAGEL-7B-MoT: fal, provider-default image size; one output sampled; use_thought=false, one request, first output, seed=1385391411, Fal schema does not expose image size or image count fields, estimated cost $0.100000, generated Nov 2, 2026, 3:22 PM. Source

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

Stable Diffusion XL 1.0 image SHA 256: f59af9c84f6fbb3e4ca34bf87ebb0f7c088e8caa520fa4cdf815a96d1bffda83

BAGEL-7B-MoT image SHA 256: e8ef4e099fe8329b2bba090a2b8fee5cf926dfc5f91ad8e8694b4a4c78ab8455

Typography and layout

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

BAGEL-7B-MoT: fal, provider-default image size; one output sampled; use_thought=false, one request, first output, seed=425487007, Fal schema does not expose image size or image count fields, estimated cost $0.100000, generated Nov 2, 2026, 3:21 PM. Source

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

Stable Diffusion XL 1.0 image SHA 256: c81e5d0d70afcb53fcffb51a0d548041fd0b33df300aed14b8ecd354aaaaec4e

BAGEL-7B-MoT image SHA 256: 401dd23a2e6578a4e676d10543a9fea92d94d0f535dee90d6c7a25e56143257a

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Stable Diffusion XL 1.0

not in the arena

n/a

BAGEL-7B-MoT

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Stable Diffusion XL 1.0

Rank 100 of 108

BAGEL-7B-MoT

Rank 76 of 108

Price per imagedollars through the API · lower is better

n/a

Stable Diffusion XL 1.0

no API price

n/a

BAGEL-7B-MoT

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.

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.

BAGEL-7B-MoT

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

Kennzahl	Stable Diffusion XL 1.0	BAGEL-7B-MoT
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	13	34
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	5	11
Product photographyMaterials, reflections, and fine details	13	88
Character and detailAnatomy, clothing, and spatial consistency	28	32
InfographicNumbers, labels, and visual organization	5	6
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	n/a	May 20, 2025
Weightsopen means: runs locally	open	open
Lifecycle	Older open checkpoint; provider still lists it in its legacy model overview	no notice
Access	API, local	local, Web app
Parameters	not published	14 billion
Largest output	not consistently published	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: Stable Diffusion XL 1.0 model source, status source (verified Nov 1, 2026) · BAGEL-7B-MoT 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 Stable Diffusion XL 1.0 and BAGEL-7B-MoT from the same arena and category. Older model versions are not used as substitutes.

More comparisons

Compare Stable Diffusion XL 1.0 and BAGEL-7B-MoT with the models people compare them with most often.

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