Qwen Image 2.1 Turbovs.Stable Diffusion XL 1.0

Which one is better?

In our own test, Qwen Image 2.1 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.

Qwen Image 2.1 Turbo: Infographic, first image from the fixed test run — Qwen Image 2.1 Turbo89 out of 100

Stable Diffusion XL 1.0: Infographic, first image from the fixed test run — Stable Diffusion XL 1.05 out of 100

Result for “Infographic”:Qwen Image 2.1 Turbo ahead

Prompt given to both modelsCreate a square German infographic titled SO FUNKTIONIERT PHOTOSYNTHESE. Show exactly four numbered steps in this order: 1 LICHT, 2 WASSER, 3 CO₂, 4 ZUCKER + SAUERSTOFF. Use a clean editorial science style with one plant cross-section, simple arrows, high contrast, legible labels, and no other text.

What the rating looks at: Numbers, labels, and visual organization.

“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

Qwen Image 2.1 Turbo: openrouter, aspect ratio 1:1, resolution 1K, n=1, seed=provider random, actual cost $0.018400, generated Nov 1, 2026, 8:31 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

Qwen Image 2.1 Turbo image SHA 256: 60c270da7b2ace74fcac4f55b962d4e01fae99265874d25857e2b461c00f3666

Stable Diffusion XL 1.0 image SHA 256: 26d3c3d3e687614e24dd12191246bc1d96c1576d31f9211840a3e02b64d4d507

Infographic

Qwen Image 2.1 Turbo: openrouter, aspect ratio 1:1, resolution 1K, n=1, seed=provider random, actual cost $0.018400, generated Nov 1, 2026, 8:31 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

Qwen Image 2.1 Turbo image SHA 256: 0650a13329baa39bb1923594082473a8138ecbe1ea95bb63017e7e1d76a7fafe

Stable Diffusion XL 1.0 image SHA 256: ebfe8d1c223478c94ff1973ab2600cd4f696b40b8c3388ca40f26f52c83afdb2

Product photography

Qwen Image 2.1 Turbo: openrouter, aspect ratio 1:1, resolution 1K, n=1, seed=provider random, actual cost $0.018400, generated Nov 1, 2026, 8:30 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

Qwen Image 2.1 Turbo image SHA 256: 012d9320a2759329277e1e730e51da03aa4a0d992017a218228f88f07771dc08

Stable Diffusion XL 1.0 image SHA 256: f59af9c84f6fbb3e4ca34bf87ebb0f7c088e8caa520fa4cdf815a96d1bffda83

Typography and layout

Qwen Image 2.1 Turbo: openrouter, aspect ratio 1:1, resolution 1K, n=1, seed=provider random, actual cost $0.018400, generated Nov 1, 2026, 8:30 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

Qwen Image 2.1 Turbo image SHA 256: a052bc14d0b36849b7bd6b38e368bf2bd11d68217ab675dbea3c58d8a62f34dd

Stable Diffusion XL 1.0 image SHA 256: c81e5d0d70afcb53fcffb51a0d548041fd0b33df300aed14b8ecd354aaaaec4e

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Qwen Image 2.1 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

Qwen Image 2.1 Turbo

Rank 23 of 108

Stable Diffusion XL 1.0

Rank 100 of 108

Price per imagedollars through the API · lower is better

n/a

Qwen Image 2.1 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.

Qwen Image 2.1 Turbo

Subscription: n/a No plan in Germany names exactly this model.
Via API: No API price known. Access: API.
Weights: unknown.

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	Qwen Image 2.1 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	69	13
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	51	5
Product photographyMaterials, reflections, and fine details	90	13
Character and detailAnatomy, clothing, and spatial consistency	48	28
InfographicNumbers, labels, and visual organization	89	5
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	n/a	n/a
Weightsopen means: runs locally	unknown	open
Lifecycle	no notice	Older open checkpoint; provider still lists it in its legacy model overview
Access	API	API, local
Parameters	not published	not published
Largest output	2K	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: Qwen Image 2.1 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 Qwen Image 2.1 Turbo and Stable Diffusion XL 1.0 from the same arena and category. Older model versions are not used as substitutes.

More comparisons

Compare Qwen Image 2.1 Turbo and Stable Diffusion XL 1.0 with the models people compare them with most often.

Compare another matchup

First image modelSecond image model