Z-Image Turbovs.SANA-1.5 4.8B 1024px

Which one is better?

In our own test, Z-Image Turbo 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.

Z-Image Turbo: Character and detail, first image from the fixed test run — Z-Image Turbo71 out of 100

SANA-1.5 4.8B 1024px: Character and detail, first image from the fixed test run — SANA-1.5 4.8B 1024px0 out of 100

Result for “Character and detail”:Z-Image Turbo ahead

Prompt given to both modelsCreate a square cinematic full-body portrait of a fictional bicycle courier waiting under a transparent umbrella at a rainy tram stop in Hamburg at blue hour. She wears a mustard raincoat, navy trousers, red sneakers, a silver helmet, and carries a teal messenger bag. Keep both hands visible, render the bicycle correctly, use realistic wet-street reflections, and include no readable brand names.

What the rating looks at: Anatomy, clothing, and spatial consistency.

“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

Z-Image Turbo: fal, requested 1024x1024, n=1, seed=457367499, prompt expansion disabled, estimated cost $0.010000, generated Nov 1, 2026, 9:35 PM. Source

SANA-1.5 4.8B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers@9468102c3cebb657f8c4b5f1e5a71e989a15f10d; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=6914427587596023045; 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, 5:42 PM. Source

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

Z-Image Turbo image SHA 256: 5297796931ae8a9259947d2ee85881444a6408266aa7c9bc45d365b56bd11fe1

SANA-1.5 4.8B 1024px image SHA 256: 97badf740db81367de7d6af73cc9313f1dfc4fe8383183ddbd9fc12cc67b35ee

Infographic

Z-Image Turbo: fal, requested 1024x1024, n=1, seed=364147116, prompt expansion disabled, estimated cost $0.010000, generated Nov 1, 2026, 9:35 PM. Source

SANA-1.5 4.8B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers@9468102c3cebb657f8c4b5f1e5a71e989a15f10d; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=3447101042619426797; 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, 5:43 PM. Source

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

Z-Image Turbo image SHA 256: 2a0bbce06ee9f7e009876b2d9abc02657f7be5535cd79ab7f37df0bf61b141a8

SANA-1.5 4.8B 1024px image SHA 256: 6c23b2df82d085e155c012c16915d04dd3a1aacdb78c9604eb74668309e07b38

Product photography

Z-Image Turbo: fal, requested 1024x1024, n=1, seed=453185405, prompt expansion disabled, estimated cost $0.010000, generated Nov 1, 2026, 9:35 PM. Source

SANA-1.5 4.8B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers@9468102c3cebb657f8c4b5f1e5a71e989a15f10d; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=1122699157863902383; 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, 5:41 PM. Source

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

Z-Image Turbo image SHA 256: 16330b5b71c71290aaa2994dd589e5e2088712d286fddcee8046e56c300f124e

SANA-1.5 4.8B 1024px image SHA 256: 0ceaa96a83bc85d12c7f56b925226a346ee9b42f831fa5a9592edc4fc1e7ce81

Typography and layout

Z-Image Turbo: fal, requested 1024x1024, n=1, seed=249593273, prompt expansion disabled, estimated cost $0.010000, generated Nov 1, 2026, 9:35 PM. Source

SANA-1.5 4.8B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers@9468102c3cebb657f8c4b5f1e5a71e989a15f10d; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=623973358981804157; 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, 5:40 PM. Source

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

Z-Image Turbo image SHA 256: 0a9ee8166b04fc677b96ca3f74d8fcf37657d812f81d3b94940092aa661dba50

SANA-1.5 4.8B 1024px image SHA 256: 8371e0e35ad4472b71165cedd1940eb0339b19c24f801c3b956874c090a327fc

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Z-Image Turbo

not in the arena

n/a

SANA-1.5 4.8B 1024px

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Z-Image Turbo

Rank 67 of 108

SANA-1.5 4.8B 1024px

Rank 106 of 108

Price per imagedollars through the API · lower is better

n/a

Z-Image Turbo

no API price

n/a

SANA-1.5 4.8B 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.

Z-Image Turbo

Subscription: No Tongyi-MAI / Alibaba subscription includes this model. Through other providers from ≈ €12.72 a month (Recraft Basic, with limited credits). 3 plans in Germany.
Via API: No API price known. Access: API, local, Web app.
License: Open weights.

SANA-1.5 4.8B 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	Z-Image Turbo	SANA-1.5 4.8B 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	44	4
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	40	8
Product photographyMaterials, reflections, and fine details	30	1
Character and detailAnatomy, clothing, and spatial consistency	71	0
InfographicNumbers, labels, and visual organization	37	6
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	Nov 26, 2025	Mar 16, 2025
Weightsopen means: runs locally	open	open
Access	API, local, Web app	local
Parameters	not published	4.8 billion
Largest output	2K	not consistently published
Plans in Germanynaming exactly this model	3	n/a

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

Sources: Z-Image Turbo model source (verified Nov 1, 2026) · SANA-1.5 4.8B 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 Z-Image Turbo and SANA-1.5 4.8B 1024px from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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