Stable Diffusion 3.5 Mediumvs.LLaDA-Image

Which one is better?

In our own test, LLaDA-Image 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.

Stable Diffusion 3.5 Medium: Typography and layout, first image from the fixed test run — Stable Diffusion 3.5 Medium22 out of 100

LLaDA-Image: Typography and layout, first image from the fixed test run — LLaDA-Image62 out of 100

Result for “Typography and layout”:LLaDA-Image 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

Stable Diffusion 3.5 Medium: fal, requested 1024x1024, n=1, seed=provider random, estimated cost $0.020000, generated Nov 1, 2026, 9:17 PM. Source

LLaDA-Image: manual, 2.13.0 PyTorch, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image@e4e2703f410f7ddb6ee8d6b09dac6a8ec5093039; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0; seed=6428048197886116697; MPS generator; BF16 text/diffusion components, FP32 denoising latent, receipt-reported CPU FP32 native VAE decode; negative_prompt argument omitted (None default, not the manufacturer's empty-string example); MPS community PR @ 5395ce055da9975b09f71e21b865973d409bb3cd; official pinned text encoder uses eager MoE fallback and SDPA attention; other image-component attention paths were not independently captured by this review; no CUDA numerical equivalence claimed; MPS fallback permitted, actual fallback operations not measured; two submissions: the original request stopped at the 60-GiB free-space safeguard before image or latent (supervisor exit 143); one technical same-request/same-seed retry produced the retained first completed output; no quality reroll, generated Nov 3, 2026, 8:50 AM. Source

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

Stable Diffusion 3.5 Medium image SHA 256: 6b8aa732e94479ad302904096c5031908141e4083ee099f080185aa04c2ea800

LLaDA-Image image SHA 256: a34d96894f5500c5bce2c16cca14ae09748f3aff4d47d15f8aff0d1be5749211

Infographic

Stable Diffusion 3.5 Medium: fal, requested 1024x1024, n=1, seed=provider random, estimated cost $0.020000, generated Nov 1, 2026, 9:17 PM. Source

LLaDA-Image: manual, 2.13.0 PyTorch, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image@e4e2703f410f7ddb6ee8d6b09dac6a8ec5093039; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0; seed=1354677768900879717; MPS generator; BF16 text/diffusion components, FP32 denoising latent, receipt-reported CPU FP32 native VAE decode; negative_prompt argument omitted (None default, not the manufacturer's empty-string example); MPS community PR @ 5395ce055da9975b09f71e21b865973d409bb3cd; official pinned text encoder uses eager MoE fallback and SDPA attention; other image-component attention paths were not independently captured by this review; no CUDA numerical equivalence claimed; MPS fallback permitted, actual fallback operations not measured, generated Nov 3, 2026, 8:56 AM. Source

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

Stable Diffusion 3.5 Medium image SHA 256: 61b12e227431387098e51c6ce6d059be9d8df9c85263b994bee62602e2ddfd16

LLaDA-Image image SHA 256: cad67ad3f1a475d12bc2152840dc98657f81b5c7318c09a9e9fa40fbb433372f

Product photography

Stable Diffusion 3.5 Medium: fal, requested 1024x1024, n=1, seed=provider random, estimated cost $0.020000, generated Nov 1, 2026, 9:16 PM. Source

LLaDA-Image: manual, 2.13.0 PyTorch, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image@e4e2703f410f7ddb6ee8d6b09dac6a8ec5093039; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0; seed=152405017095880227; MPS generator; BF16 text/diffusion components, FP32 denoising latent, receipt-reported CPU FP32 native VAE decode; negative_prompt argument omitted (None default, not the manufacturer's empty-string example); MPS community PR @ 5395ce055da9975b09f71e21b865973d409bb3cd; official pinned text encoder uses eager MoE fallback and SDPA attention; other image-component attention paths were not independently captured by this review; no CUDA numerical equivalence claimed; MPS fallback permitted, actual fallback operations not measured, generated Nov 3, 2026, 1:02 AM. Source

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

Stable Diffusion 3.5 Medium image SHA 256: 2412a20e61c118ce7ab7ff83e285b96eb32d5c0f1171cc769e153db6655b8f67

LLaDA-Image image SHA 256: 888e173c25c26ff1dcbfd07aad01ffc6a71cc7735099d3e943e60dfddfe31553

Typography and layout

Stable Diffusion 3.5 Medium: fal, requested 1024x1024, n=1, seed=provider random, estimated cost $0.020000, generated Nov 1, 2026, 9:16 PM. Source

LLaDA-Image: manual, 2.13.0 PyTorch, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image@e4e2703f410f7ddb6ee8d6b09dac6a8ec5093039; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0; seed=8778315039365154420; MPS generator; BF16 text/diffusion components, FP32 denoising latent, receipt-reported CPU FP32 native VAE decode; negative_prompt argument omitted (None default, not the manufacturer's empty-string example); MPS community PR @ 5395ce055da9975b09f71e21b865973d409bb3cd; official pinned text encoder uses eager MoE fallback and SDPA attention; other image-component attention paths were not independently captured by this review; no CUDA numerical equivalence claimed; MPS fallback permitted, actual fallback operations not measured, generated Nov 3, 2026, 12:40 AM. Source

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

Stable Diffusion 3.5 Medium image SHA 256: bc854c3269ea8bada6f764545f4a09abedc110219018c09f2fa5708a14d9af7e

LLaDA-Image image SHA 256: 256a88541c9821a6242bb434fb520d4d6f0793c934c66acd1fd2e8edd8086b5d

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Stable Diffusion 3.5 Medium

not in the arena

n/a

LLaDA-Image

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Stable Diffusion 3.5 Medium

Rank 90 of 108

LLaDA-Image

Rank 63 of 108

Price per imagedollars through the API · lower is better

n/a

Stable Diffusion 3.5 Medium

no API price

n/a

LLaDA-Image

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 3.5 Medium

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

LLaDA-Image

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 · 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 3.5 Medium	LLaDA-Image
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	25	48
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	22	62
Product photographyMaterials, reflections, and fine details	29	35
Character and detailAnatomy, clothing, and spatial consistency	31	63
InfographicNumbers, labels, and visual organization	20	33
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	Oct 29, 2024	Sep 4, 2026
Weightsopen means: runs locally	open	open
Access	API, local	local
Parameters	2.5 billion	6 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 3.5 Medium model source (verified Nov 1, 2026) · LLaDA-Image 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 3.5 Medium and LLaDA-Image from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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