Stable Diffusion 3.5 Mediumvs.Stable Diffusion Turbo

Which one is better?

In our own test, Stable Diffusion 3.5 Medium 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: Product photography, first image from the fixed test run — Stable Diffusion 3.5 Medium29 out of 100

Stable Diffusion Turbo: Product photography, first image from the fixed test run — Stable Diffusion Turbo1 out of 100

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

Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=5483236571890770311; 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; archived input unchanged, generated Nov 2, 2026, 6:17 PM. Source

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

Stable Diffusion 3.5 Medium image SHA 256: 6b8aa732e94479ad302904096c5031908141e4083ee099f080185aa04c2ea800

Stable Diffusion Turbo image SHA 256: 291b69ba3d375bfa017c782f61521b705d67708ed5e1be9350d55d486ad6f1d1

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

Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=8161577731918994504; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:18 PM. Source

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

Stable Diffusion 3.5 Medium image SHA 256: 61b12e227431387098e51c6ce6d059be9d8df9c85263b994bee62602e2ddfd16

Stable Diffusion Turbo image SHA 256: 80a2687dbc408395b416994d4853332427d09fccead1a6229564cc25b32de7e6

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

Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=2124044582169182899; 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; archived input unchanged, generated Nov 2, 2026, 6:16 PM. Source

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

Stable Diffusion 3.5 Medium image SHA 256: 2412a20e61c118ce7ab7ff83e285b96eb32d5c0f1171cc769e153db6655b8f67

Stable Diffusion Turbo image SHA 256: 13dcbe6eb77495317a03f3eed013c4358ae7466fca00d9f869e512279a8a2af6

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

Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=2303175353176269810; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:16 PM. Source

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

Stable Diffusion 3.5 Medium image SHA 256: bc854c3269ea8bada6f764545f4a09abedc110219018c09f2fa5708a14d9af7e

Stable Diffusion Turbo image SHA 256: 7def19e4f4adf68244ff69e5873b9f465ecd158fb4abab0273443bfc3804438e

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

Stable Diffusion Turbo

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Stable Diffusion 3.5 Medium

Rank 90 of 108

Stable Diffusion Turbo

Rank 108 of 108

Price per imagedollars through the API · lower is better

n/a

Stable Diffusion 3.5 Medium

no API price

n/a

Stable Diffusion 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.

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.

Stable Diffusion 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.

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	Stable Diffusion Turbo
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	3
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	22	0
Product photographyMaterials, reflections, and fine details	29	1
Character and detailAnatomy, clothing, and spatial consistency	31	5
InfographicNumbers, labels, and visual organization	20	5
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	Oct 29, 2024	n/a
Weightsopen means: runs locally	open	open
Lifecycle	no notice	Older open fast checkpoint; no active hosted API ID claimed in the reviewed model list
Access	API, local	local
Parameters	2.5 billion	not published
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) · Stable Diffusion 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 Stable Diffusion 3.5 Medium and Stable Diffusion Turbo from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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