Stable Diffusion Turbovs.SANA-1.5 1.6B 1024px

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 Turbo: Typography and layout, first image from the fixed test run — Stable Diffusion Turbo0 out of 100

SANA-1.5 1.6B 1024px: Typography and layout, first image from the fixed test run — SANA-1.5 1.6B 1024px3 out of 100

Result for “Typography and layout”:SANA-1.5 1.6B 1024px 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 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

SANA-1.5 1.6B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers@b77948f2b4eed5c728e9b828ccff07f7427b43cc; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=4913908686846438154; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; native prompt preprocessing lowercases input; optional instruction disabled, generated Nov 2, 2026, 5:32 PM. Source

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

Stable Diffusion Turbo image SHA 256: 291b69ba3d375bfa017c782f61521b705d67708ed5e1be9350d55d486ad6f1d1

SANA-1.5 1.6B 1024px image SHA 256: abc8d1fcb514ea7754211b79030abbf92ef83a61e546f19b217ccb06df0aaf45

Infographic

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

SANA-1.5 1.6B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers@b77948f2b4eed5c728e9b828ccff07f7427b43cc; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=1936313402304398258; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; native prompt preprocessing lowercases input; optional instruction disabled, generated Nov 2, 2026, 5:33 PM. Source

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

Stable Diffusion Turbo image SHA 256: 80a2687dbc408395b416994d4853332427d09fccead1a6229564cc25b32de7e6

SANA-1.5 1.6B 1024px image SHA 256: 828815adaab125d67312b64061f9b5a448558e9ac56ec9aa5ce8cf00cb63c481

Product photography

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

SANA-1.5 1.6B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers@b77948f2b4eed5c728e9b828ccff07f7427b43cc; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=1353667313865032696; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; native prompt preprocessing lowercases input; optional instruction disabled, generated Nov 2, 2026, 5:32 PM. Source

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

Stable Diffusion Turbo image SHA 256: 13dcbe6eb77495317a03f3eed013c4358ae7466fca00d9f869e512279a8a2af6

SANA-1.5 1.6B 1024px image SHA 256: a43c512ad071c0d7526e16f69874b653f073c532403114299c8fde43770a72c8

Typography and layout

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

SANA-1.5 1.6B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers@b77948f2b4eed5c728e9b828ccff07f7427b43cc; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=2028788407160516328; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; native prompt preprocessing lowercases input; optional instruction disabled; two submissions: first attempt produced no image after a documented MPS VAE decoding crash; technical repeat kept the same prompt and seed, generated Nov 2, 2026, 5:31 PM. Source

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

Stable Diffusion Turbo image SHA 256: 7def19e4f4adf68244ff69e5873b9f465ecd158fb4abab0273443bfc3804438e

SANA-1.5 1.6B 1024px image SHA 256: 7c58a1c863d5b8d22d06ed159b89aa64d13cf9a44f7b3d4871683f491f0bb75c

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Stable Diffusion Turbo

not in the arena

n/a

SANA-1.5 1.6B 1024px

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Stable Diffusion Turbo

Rank 108 of 108

SANA-1.5 1.6B 1024px

Rank 107 of 108

Price per imagedollars through the API · lower is better

n/a

Stable Diffusion Turbo

no API price

n/a

SANA-1.5 1.6B 1024px

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 Turbo

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

SANA-1.5 1.6B 1024px

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 Turbo	SANA-1.5 1.6B 1024px
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	3	4
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	0	3
Product photographyMaterials, reflections, and fine details	1	0
Character and detailAnatomy, clothing, and spatial consistency	5	4
InfographicNumbers, labels, and visual organization	5	7
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	n/a	Mar 16, 2025
Weightsopen means: runs locally	open	open
Lifecycle	Older open fast checkpoint; no active hosted API ID claimed in the reviewed model list	no notice
Access	local	local
Parameters	not published	1.6 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 Turbo model source, status source (verified Nov 1, 2026) · SANA-1.5 1.6B 1024px 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 Turbo and SANA-1.5 1.6B 1024px from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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