Z-Image Turbovs.Stable Diffusion XL 1.0

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

Stable Diffusion XL 1.0: Character and detail, first image from the fixed test run — Stable Diffusion XL 1.028 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

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

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

Z-Image Turbo image SHA 256: 5297796931ae8a9259947d2ee85881444a6408266aa7c9bc45d365b56bd11fe1

Stable Diffusion XL 1.0 image SHA 256: 26d3c3d3e687614e24dd12191246bc1d96c1576d31f9211840a3e02b64d4d507

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

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

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

Z-Image Turbo image SHA 256: 2a0bbce06ee9f7e009876b2d9abc02657f7be5535cd79ab7f37df0bf61b141a8

Stable Diffusion XL 1.0 image SHA 256: ebfe8d1c223478c94ff1973ab2600cd4f696b40b8c3388ca40f26f52c83afdb2

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

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

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

Z-Image Turbo image SHA 256: 16330b5b71c71290aaa2994dd589e5e2088712d286fddcee8046e56c300f124e

Stable Diffusion XL 1.0 image SHA 256: f59af9c84f6fbb3e4ca34bf87ebb0f7c088e8caa520fa4cdf815a96d1bffda83

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

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

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

Z-Image Turbo image SHA 256: 0a9ee8166b04fc677b96ca3f74d8fcf37657d812f81d3b94940092aa661dba50

Stable Diffusion XL 1.0 image SHA 256: c81e5d0d70afcb53fcffb51a0d548041fd0b33df300aed14b8ecd354aaaaec4e

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Z-Image Turbo

not in the arena

n/a

Stable Diffusion XL 1.0

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Z-Image Turbo

Rank 67 of 108

Stable Diffusion XL 1.0

Rank 100 of 108

Price per imagedollars through the API · lower is better

n/a

Z-Image Turbo

no API price

n/a

Stable Diffusion XL 1.0

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.

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.

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	Stable Diffusion XL 1.0
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	13
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	40	5
Product photographyMaterials, reflections, and fine details	30	13
Character and detailAnatomy, clothing, and spatial consistency	71	28
InfographicNumbers, labels, and visual organization	37	5
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	Nov 26, 2025	n/a
Weightsopen means: runs locally	open	open
Lifecycle	no notice	Older open checkpoint; provider still lists it in its legacy model overview
Access	API, local, Web app	API, local
Parameters	not published	not published
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) · Stable Diffusion XL 1.0 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 Z-Image Turbo and Stable Diffusion XL 1.0 from the same arena and category. Older model versions are not used as substitutes.

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