Wan 2.7 Image Provs.AuraFlow v0.3

Which one is better?

In our own test, Wan 2.7 Image Pro 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 Pro: Infographic, first image from the fixed test run — Wan 2.7 Image Pro89 out of 100

AuraFlow v0.3: Infographic, first image from the fixed test run — AuraFlow v0.37 out of 100

Result for “Infographic”:Wan 2.7 Image Pro 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 2.7 Image Pro: fal, requested 1024x1024, n=1, seed=1941203496, estimated cost $0.075000, generated Nov 1, 2026, 9:25 PM. Source

AuraFlow v0.3: manual, local PyTorch 2.13.0, Diffusers 0.36.0; fal/AuraFlow-v0.3@2cd8588f04c886002be4571697d84654a50e3af3; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=8761677709905735111; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:42 PM. Source

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

Wan 2.7 Image Pro image SHA 256: 0bd2cf4922b58d78d0e6ec3d804aaccd427e5317066816fdc039ca5c80f4de28

AuraFlow v0.3 image SHA 256: 9d0f14ca671e82f18947b9a0d7d7587e372cb4f515ce028434fac4efbd937155

Infographic

Wan 2.7 Image Pro: fal, requested 1024x1024, n=1, seed=1839059937, estimated cost $0.075000, generated Nov 1, 2026, 9:26 PM. Source

AuraFlow v0.3: manual, local PyTorch 2.13.0, Diffusers 0.36.0; fal/AuraFlow-v0.3@2cd8588f04c886002be4571697d84654a50e3af3; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=3793893866514060497; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:47 PM. Source

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

Wan 2.7 Image Pro image SHA 256: caa6b4e95e0676d9867358db906f1f029dd320960e9dd1b8300283a5e2723077

AuraFlow v0.3 image SHA 256: d2a4ecc1c165bc861ca60133d9ff95dc9d5b1d5320763be92c8168ff8c3b8166

Product photography

Wan 2.7 Image Pro: fal, requested 1024x1024, n=1, seed=1277346407, estimated cost $0.075000, generated Nov 1, 2026, 9:24 PM. Source

AuraFlow v0.3: manual, local PyTorch 2.13.0, Diffusers 0.36.0; fal/AuraFlow-v0.3@2cd8588f04c886002be4571697d84654a50e3af3; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=4682738952817248216; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:37 PM. Source

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

Wan 2.7 Image Pro image SHA 256: 1ed875886c86963322fdf329bf809943b96886fb4f373197e1654256316ac67c

AuraFlow v0.3 image SHA 256: 6889065a427cc29bc1c0a53b0ad0662eb24f4be885148b0ebb8f6d945bbd4749

Typography and layout

Wan 2.7 Image Pro: fal, requested 1024x1024, n=1, seed=1291974856, estimated cost $0.075000, generated Nov 1, 2026, 9:23 PM. Source

AuraFlow v0.3: manual, local PyTorch 2.13.0, Diffusers 0.36.0; fal/AuraFlow-v0.3@2cd8588f04c886002be4571697d84654a50e3af3; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=7437917803059710684; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:31 PM. Source

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

Wan 2.7 Image Pro image SHA 256: 4e860e01ecb53ed47d9a3ad2be57e9f2567e8dc010e44780615f71e30713f648

AuraFlow v0.3 image SHA 256: b6c221541210743564f6921b5e0bbc227c4d2246f6410cb0c1779c7c58d9460d

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Wan 2.7 Image Pro

not in the arena

n/a

AuraFlow v0.3

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Wan 2.7 Image Pro

Rank 1 of 108

AuraFlow v0.3

Rank 102 of 108

Price per imagedollars through the API · lower is better

n/a

Wan 2.7 Image Pro

no API price

n/a

AuraFlow v0.3

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 Pro

Subscription: At Alibaba from ≈ €6.36 a month (Token Plan Individual Lite, with limited credits). 7 plans in Germany.
Via API: No API price known. Access: API.
Weights: unknown.

AuraFlow v0.3

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 Pro	AuraFlow v0.3
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	84	12
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	81	11
Product photographyMaterials, reflections, and fine details	87	15
Character and detailAnatomy, clothing, and spatial consistency	78	16
InfographicNumbers, labels, and visual organization	89	7
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
Access	API	local
Parameters	not published	7 billion
Largest output	4K	1,536 × 1,536 pixels
Plans in Germanynaming exactly this model	7	n/a

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

Sources: Wan 2.7 Image Pro model source (verified Nov 1, 2026) · AuraFlow v0.3 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 Pro and AuraFlow v0.3 from the same arena and category. Older model versions are not used as substitutes.

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