Wan 2.7 Image Provs.SANA-1.5 1.6B 1024px

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: Product photography, first image from the fixed test run — Wan 2.7 Image Pro87 out of 100

SANA-1.5 1.6B 1024px: Product photography, first image from the fixed test run — SANA-1.5 1.6B 1024px0 out of 100

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

SANA-1.5 1.6B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers@b77948f2b4eed5c728e9b828ccff07f7427b43cc; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=4913908686846438154; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; native prompt preprocessing lowercases input; optional instruction disabled, generated Nov 2, 2026, 5:32 PM. Source

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

Wan 2.7 Image Pro image SHA 256: 0bd2cf4922b58d78d0e6ec3d804aaccd427e5317066816fdc039ca5c80f4de28

SANA-1.5 1.6B 1024px image SHA 256: abc8d1fcb514ea7754211b79030abbf92ef83a61e546f19b217ccb06df0aaf45

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

SANA-1.5 1.6B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers@b77948f2b4eed5c728e9b828ccff07f7427b43cc; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=1936313402304398258; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; native prompt preprocessing lowercases input; optional instruction disabled, generated Nov 2, 2026, 5:33 PM. Source

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

Wan 2.7 Image Pro image SHA 256: caa6b4e95e0676d9867358db906f1f029dd320960e9dd1b8300283a5e2723077

SANA-1.5 1.6B 1024px image SHA 256: 828815adaab125d67312b64061f9b5a448558e9ac56ec9aa5ce8cf00cb63c481

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

SANA-1.5 1.6B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers@b77948f2b4eed5c728e9b828ccff07f7427b43cc; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=1353667313865032696; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; native prompt preprocessing lowercases input; optional instruction disabled, generated Nov 2, 2026, 5:32 PM. Source

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

Wan 2.7 Image Pro image SHA 256: 1ed875886c86963322fdf329bf809943b96886fb4f373197e1654256316ac67c

SANA-1.5 1.6B 1024px image SHA 256: a43c512ad071c0d7526e16f69874b653f073c532403114299c8fde43770a72c8

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

SANA-1.5 1.6B 1024px: manual, local PyTorch 2.13.0, Diffusers 0.36.0; Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers@b77948f2b4eed5c728e9b828ccff07f7427b43cc; native 1024x1024, 1:1; one request, first output; steps=20, guidance=4.5, seed=2028788407160516328; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoder and transformer; float32 for VAE; native prompt preprocessing lowercases input; optional instruction disabled; two submissions: first attempt produced no image after a documented MPS VAE decoding crash; technical repeat kept the same prompt and seed, generated Nov 2, 2026, 5:31 PM. Source

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

Wan 2.7 Image Pro image SHA 256: 4e860e01ecb53ed47d9a3ad2be57e9f2567e8dc010e44780615f71e30713f648

SANA-1.5 1.6B 1024px image SHA 256: 7c58a1c863d5b8d22d06ed159b89aa64d13cf9a44f7b3d4871683f491f0bb75c

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

SANA-1.5 1.6B 1024px

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Wan 2.7 Image Pro

Rank 1 of 108

SANA-1.5 1.6B 1024px

Rank 107 of 108

Price per imagedollars through the API · lower is better

n/a

Wan 2.7 Image Pro

no API price

n/a

SANA-1.5 1.6B 1024px

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.

SANA-1.5 1.6B 1024px

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	SANA-1.5 1.6B 1024px
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	4
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	81	3
Product photographyMaterials, reflections, and fine details	87	0
Character and detailAnatomy, clothing, and spatial consistency	78	4
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	Mar 16, 2025
Weightsopen means: runs locally	unknown	open
Access	API	local
Parameters	not published	1.6 billion
Largest output	4K	not consistently published
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) · SANA-1.5 1.6B 1024px 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 SANA-1.5 1.6B 1024px from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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