Wan 2.7 Imagevs.LLaDA-Image

Which one is better?

In our own test, Wan 2.7 Image 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.

Wan 2.7 Image: Product photography, first image from the fixed test run — Wan 2.7 Image88 out of 100

LLaDA-Image: Product photography, first image from the fixed test run — LLaDA-Image35 out of 100

Result for “Product photography”:Wan 2.7 Image ahead

Prompt given to both modelsCreate a square premium product photograph of a brushed titanium wristwatch standing upright on dark green marble. The watch has a cream dial, thin black hands set to 10:10, twelve distinct hour markers, a realistic crown, and a dark brown leather strap. Soft window light from the left, controlled reflections, shallow depth of field, no text, no logo, no extra objects.

What the rating looks at: Materials, reflections, and fine details.

“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 2.7 Image: fal, requested 1024x1024, n=1, seed=2030081785, estimated cost $0.030000, generated Nov 1, 2026, 9:22 PM. Source

LLaDA-Image: manual, 2.13.0 PyTorch, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image@e4e2703f410f7ddb6ee8d6b09dac6a8ec5093039; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0; seed=6428048197886116697; MPS generator; BF16 text/diffusion components, FP32 denoising latent, receipt-reported CPU FP32 native VAE decode; negative_prompt argument omitted (None default, not the manufacturer's empty-string example); MPS community PR @ 5395ce055da9975b09f71e21b865973d409bb3cd; official pinned text encoder uses eager MoE fallback and SDPA attention; other image-component attention paths were not independently captured by this review; no CUDA numerical equivalence claimed; MPS fallback permitted, actual fallback operations not measured; two submissions: the original request stopped at the 60-GiB free-space safeguard before image or latent (supervisor exit 143); one technical same-request/same-seed retry produced the retained first completed output; no quality reroll, generated Nov 3, 2026, 8:50 AM. Source

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

Wan 2.7 Image image SHA 256: 62258e364ab7627aa6c1d0d4214ad1cc32287ede3b059aec041acf4dcdb20cf4

LLaDA-Image image SHA 256: a34d96894f5500c5bce2c16cca14ae09748f3aff4d47d15f8aff0d1be5749211

Infographic

Wan 2.7 Image: fal, requested 1024x1024, n=1, seed=1843752681, estimated cost $0.030000, generated Nov 1, 2026, 9:22 PM. Source

LLaDA-Image: manual, 2.13.0 PyTorch, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image@e4e2703f410f7ddb6ee8d6b09dac6a8ec5093039; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0; seed=1354677768900879717; MPS generator; BF16 text/diffusion components, FP32 denoising latent, receipt-reported CPU FP32 native VAE decode; negative_prompt argument omitted (None default, not the manufacturer's empty-string example); MPS community PR @ 5395ce055da9975b09f71e21b865973d409bb3cd; official pinned text encoder uses eager MoE fallback and SDPA attention; other image-component attention paths were not independently captured by this review; no CUDA numerical equivalence claimed; MPS fallback permitted, actual fallback operations not measured, generated Nov 3, 2026, 8:56 AM. Source

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

Wan 2.7 Image image SHA 256: f5707f29066b971a3c094495e0ff2a1bdd0479aa5c583a2a4d3b607ce7ca0636

LLaDA-Image image SHA 256: cad67ad3f1a475d12bc2152840dc98657f81b5c7318c09a9e9fa40fbb433372f

Product photography

Wan 2.7 Image: fal, requested 1024x1024, n=1, seed=1918862783, estimated cost $0.030000, generated Nov 1, 2026, 9:21 PM. Source

LLaDA-Image: manual, 2.13.0 PyTorch, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image@e4e2703f410f7ddb6ee8d6b09dac6a8ec5093039; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0; seed=152405017095880227; MPS generator; BF16 text/diffusion components, FP32 denoising latent, receipt-reported CPU FP32 native VAE decode; negative_prompt argument omitted (None default, not the manufacturer's empty-string example); MPS community PR @ 5395ce055da9975b09f71e21b865973d409bb3cd; official pinned text encoder uses eager MoE fallback and SDPA attention; other image-component attention paths were not independently captured by this review; no CUDA numerical equivalence claimed; MPS fallback permitted, actual fallback operations not measured, generated Nov 3, 2026, 1:02 AM. Source

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

Wan 2.7 Image image SHA 256: 6b7d5e90e75158d052d09c570b3af8fbb633ed1e20d9d84b8d23e864aeb446ac

LLaDA-Image image SHA 256: 888e173c25c26ff1dcbfd07aad01ffc6a71cc7735099d3e943e60dfddfe31553

Typography and layout

Wan 2.7 Image: fal, requested 1024x1024, n=1, seed=457490918, estimated cost $0.030000, generated Nov 1, 2026, 9:21 PM. Source

LLaDA-Image: manual, 2.13.0 PyTorch, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image@e4e2703f410f7ddb6ee8d6b09dac6a8ec5093039; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0; seed=8778315039365154420; MPS generator; BF16 text/diffusion components, FP32 denoising latent, receipt-reported CPU FP32 native VAE decode; negative_prompt argument omitted (None default, not the manufacturer's empty-string example); MPS community PR @ 5395ce055da9975b09f71e21b865973d409bb3cd; official pinned text encoder uses eager MoE fallback and SDPA attention; other image-component attention paths were not independently captured by this review; no CUDA numerical equivalence claimed; MPS fallback permitted, actual fallback operations not measured, generated Nov 3, 2026, 12:40 AM. Source

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

Wan 2.7 Image image SHA 256: 60d9602757484b80c9a30506243d50d74d1160ff6998e80ba21aeb99338f5587

LLaDA-Image image SHA 256: 256a88541c9821a6242bb434fb520d4d6f0793c934c66acd1fd2e8edd8086b5d

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Wan 2.7 Image

not in the arena

n/a

LLaDA-Image

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Wan 2.7 Image

Rank 2 of 108

LLaDA-Image

Rank 63 of 108

Price per imagedollars through the API · lower is better

n/a

Wan 2.7 Image

no API price

n/a

LLaDA-Image

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 2.7 Image

Subscription: At Alibaba from ≈ €6.36 a month (Token Plan Individual Lite, with limited credits). Through other providers from €6.83 a month (Poe Basic, with limited points). 12 plans in Germany.
Via API: No API price known. Access: API.
Weights: unknown.

LLaDA-Image

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	Wan 2.7 Image	LLaDA-Image
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	81	48
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	94	62
Product photographyMaterials, reflections, and fine details	88	35
Character and detailAnatomy, clothing, and spatial consistency	63	63
InfographicNumbers, labels, and visual organization	79	33
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	n/a	Sep 4, 2026
Weightsopen means: runs locally	unknown	open
Access	API	local
Parameters	not published	6 billion
Largest output	2K	not consistently published
Plans in Germanynaming exactly this model	12	n/a

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

Sources: Wan 2.7 Image model source (verified Nov 1, 2026) · LLaDA-Image 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 Wan 2.7 Image and LLaDA-Image from the same arena and category. Older model versions are not used as substitutes.

More comparisons

Compare Wan 2.7 Image and LLaDA-Image with the models people compare them with most often.

Compare another matchup

First image modelSecond image model