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Wan 2.6 Text to Imagevs.Stable Diffusion XL 1.0

Which one is better?

In our own test, Wan 2.6 Text to Image leads. Neither is in the arena. Stable Diffusion XL 1.0 has no API price.

Test images

Same task, the first image of each. The task with the biggest difference is preselected.

Wan 2.6 Text to Image: Infographic, first image from the fixed test run
Wan 2.6 Text to Image77 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”:Wan 2.6 Text to Image 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

Wan 2.6 Text to Image: together, requested 1280x1280, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:30 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=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

Wan 2.6 Text to Image image SHA 256: cf49d7136a442f4f1c1ae29af99e917aa83bcbf501fdacfe3a13a3b3e74d7f9a

Stable Diffusion XL 1.0 image SHA 256: 26d3c3d3e687614e24dd12191246bc1d96c1576d31f9211840a3e02b64d4d507

Infographic

Wan 2.6 Text to Image: together, requested 1280x1280, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:30 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=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

Wan 2.6 Text to Image image SHA 256: 28eecbf460194ec047237c35415d934929baee9de9938bdff435cd21a4416435

Stable Diffusion XL 1.0 image SHA 256: ebfe8d1c223478c94ff1973ab2600cd4f696b40b8c3388ca40f26f52c83afdb2

Product photography

Wan 2.6 Text to Image: together, requested 1280x1280, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:29 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

Wan 2.6 Text to Image image SHA 256: e8dbcfd0ce965814d72d676eddf74f723d476c1f78c5928c7b6efbc4eca4765e

Stable Diffusion XL 1.0 image SHA 256: f59af9c84f6fbb3e4ca34bf87ebb0f7c088e8caa520fa4cdf815a96d1bffda83

Typography and layout

Wan 2.6 Text to Image: together, requested 1280x1280, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 2:01 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

Wan 2.6 Text to Image image SHA 256: 7e9557478ee297369d7680b31ab3b23a13c984981c0e0c922ef07372999122a7

Stable Diffusion XL 1.0 image SHA 256: c81e5d0d70afcb53fcffb51a0d548041fd0b33df300aed14b8ecd354aaaaec4e

The numbers at a glance

Arena ratingfrom votes by users · higher is better
n/a

Wan 2.6 Text to Image

not in the arena

n/a

Stable Diffusion XL 1.0

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better
63

Wan 2.6 Text to Image

Rank 44 of 108

13

Stable Diffusion XL 1.0

Rank 100 of 108

Price per imagedollars through the API · lower is better
$0.03

Wan 2.6 Text to Image

cheap

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.

Wan 2.6 Text to Image

Subscription
n/a No plan in Germany names exactly this model.
Via API
$0.03 per image, 100 images $3.

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 · API prices from Nov 6, 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.

KennzahlWan 2.6 Text to ImageStable Diffusion XL 1.0
Ratings
Arena ratingfrom votes by users, higher is bettern/an/a
Arena rankn/an/a
Arena votesmore votes, a safer ratingn/an/a
GraduallyBenchour own rating, 0 to 1006313
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition415
Product photographyMaterials, reflections, and fine details5413
Character and detailAnatomy, clothing, and spatial consistency7928
InfographicNumbers, labels, and visual organization775
API price
Per imagelower is better$0.03n/a
100 images$3n/a
Profile
ReleasedJanuary 2026n/a
Weightsopen means: runs locallyproprietaryopen
Lifecycleno noticeOlder open checkpoint; provider still lists it in its legacy model overview
AccessAPIAPI, local
Parametersnot publishednot published
Largest output2Knot consistently published
Plans in Germanynaming exactly this modeln/an/a

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

Sources: Wan 2.6 Text to Image model source (verified Aug 15, 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 Wan 2.6 Text to Image and Stable Diffusion XL 1.0 from the same arena and category. Older model versions are not used as substitutes.

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