OmniGen2vs.AuraFlow v0.3

Which one is better?

In our own test both are on par. 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.

OmniGen2: Typography and layout, first image from the fixed test run — OmniGen230 out of 100

AuraFlow v0.3: Typography and layout, first image from the fixed test run — AuraFlow v0.311 out of 100

Result for “Typography and layout”:OmniGen2 ahead

Prompt given to both modelsCreate a square editorial poster for an imaginary night train called MONDFALTER. Show the exact German headline MONDFALTER and the exact subline BERLIN NACH LISSABON. Use a restrained midnight-blue and warm-cream palette, one stylized moth, strong Swiss-grid typography, generous negative space, and no additional words or logos.

What the rating looks at: Legible text, hierarchy, and composition.

“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

OmniGen2: fal, image_size=1024x1024, n=1, seed=639237469, Estimate reserves 2 MP at Fal's $0.15 per MP rate, estimated cost $0.300000, generated Nov 2, 2026, 3:27 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

OmniGen2 image SHA 256: 8c39f2555d07eae2728c45591a183a4343d5ab1b9eaba4ec75a83b26f50665d0

AuraFlow v0.3 image SHA 256: 9d0f14ca671e82f18947b9a0d7d7587e372cb4f515ce028434fac4efbd937155

Infographic

OmniGen2: fal, image_size=1024x1024, n=1, seed=672021196, Estimate reserves 2 MP at Fal's $0.15 per MP rate, estimated cost $0.300000, generated Nov 2, 2026, 3:27 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

OmniGen2 image SHA 256: 2f1a66d64a4f6a41d1c7228eb46ff9c2f5c27b684cf43d5dc8ba18aa7a2cd783

AuraFlow v0.3 image SHA 256: d2a4ecc1c165bc861ca60133d9ff95dc9d5b1d5320763be92c8168ff8c3b8166

Product photography

OmniGen2: fal, image_size=1024x1024, n=1, seed=355687008, Estimate reserves 2 MP at Fal's $0.15 per MP rate, estimated cost $0.300000, generated Nov 2, 2026, 3:27 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

OmniGen2 image SHA 256: 31e9ddbf26881a576ca95a7bbd2642fae4a96c4205362abe3859ade6a3da8dc7

AuraFlow v0.3 image SHA 256: 6889065a427cc29bc1c0a53b0ad0662eb24f4be885148b0ebb8f6d945bbd4749

Typography and layout

OmniGen2: fal, image_size=1024x1024, n=1, seed=713672340, Estimate reserves 2 MP at Fal's $0.15 per MP rate, estimated cost $0.300000, generated Nov 2, 2026, 3:27 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

OmniGen2 image SHA 256: f4413e92d9fca652ec3e6bbb768a0003f064d7286b0652c1385c26b646b0c890

AuraFlow v0.3 image SHA 256: b6c221541210743564f6921b5e0bbc227c4d2246f6410cb0c1779c7c58d9460d

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

OmniGen2

not in the arena

n/a

AuraFlow v0.3

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

OmniGen2

Rank 95 of 108

AuraFlow v0.3

Rank 102 of 108

Price per imagedollars through the API · lower is better

n/a

OmniGen2

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.

OmniGen2

Subscription: n/a No plan in Germany names exactly this model.
Via API: No API price known. Access: local, Web app.
License: Open weights.

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	OmniGen2	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	22	12
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	30	11
Product photographyMaterials, reflections, and fine details	30	15
Character and detailAnatomy, clothing, and spatial consistency	8	16
InfographicNumbers, labels, and visual organization	19	7
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	Jun 16, 2025	n/a
Weightsopen means: runs locally	open	open
Access	local, Web app	local
Parameters	not published	7 billion
Largest output	not consistently published	1,536 × 1,536 pixels
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: OmniGen2 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 OmniGen2 and AuraFlow v0.3 from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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