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Reve 2.1vs.LLaDA-Image

Which one is better?

In our own test both are on par. LLaDA-Image has no arena rating yet. Neither has an API price.

Test images

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

Reve 2.1: Infographic, first image from the fixed test run
Reve 2.185 out of 100
LLaDA-Image: Infographic, first image from the fixed test run
LLaDA-Image33 out of 100

Result for “Infographic”:Reve 2.1 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

Reve 2.1: manual, Official Reve web app; Reve 2.1; chat creation; one request; 1:1 requested in prompt; first of two results; free plan; 2026-08-14; https://app.reve.com/model, source 4096x4096, stored 1024x1024 (Lanczos3 downscale), generated Aug 14, 2026, 4:23 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

Reve 2.1 image SHA 256: 29e9f7f38ed5c63dcfaab671a49fb9f4e9f82cd24b99134b87e1c22442822126

LLaDA-Image image SHA 256: a34d96894f5500c5bce2c16cca14ae09748f3aff4d47d15f8aff0d1be5749211

Infographic

Reve 2.1: manual, Official Reve web app; Reve 2.1; chat creation; one request; 1:1 requested in prompt; first of two results; free plan; 2026-08-14; https://app.reve.com/model, source 4096x4096, stored 1024x1024 (Lanczos3 downscale), generated Aug 14, 2026, 4:23 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

Reve 2.1 image SHA 256: 30d157e85d75eb2ca1f71152d27f26d378ad8ea49b899d5f350ae0cda4e2bc59

LLaDA-Image image SHA 256: cad67ad3f1a475d12bc2152840dc98657f81b5c7318c09a9e9fa40fbb433372f

Product photography

Reve 2.1: manual, Official Reve web app; Reve 2.1; chat creation; one request; 1:1 requested in prompt; first of two results; free plan; 2026-08-14; https://app.reve.com/model, source 4096x4096, stored 1024x1024 (Lanczos3 downscale), generated Aug 14, 2026, 4:23 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

Reve 2.1 image SHA 256: 1f91c3cb481151fb5165194b4edea2a46e786ca80e4ec3bdd888e3ba0016c510

LLaDA-Image image SHA 256: 888e173c25c26ff1dcbfd07aad01ffc6a71cc7735099d3e943e60dfddfe31553

Typography and layout

Reve 2.1: manual, Official Reve web app; Reve 2.1; chat creation; one request; 1:1 requested in prompt; first of two results; free plan; 2026-08-14; https://app.reve.com/model, source 4096x4096, stored 1024x1024 (Lanczos3 downscale), generated Aug 14, 2026, 4:23 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

Reve 2.1 image SHA 256: 84bcd1680fd0ae861ec66c21ab8f4cd79738bd093eb87ffe7283d9842c37cb1d

LLaDA-Image image SHA 256: 256a88541c9821a6242bb434fb520d4d6f0793c934c66acd1fd2e8edd8086b5d

The numbers at a glance

Arena ratingfrom votes by users · higher is better
1,300

Reve 2.1

Rank 6 of 21

n/a

LLaDA-Image

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better
73

Reve 2.1

Rank 13 of 108

48

LLaDA-Image

Rank 63 of 108

Price per imagedollars through the API · lower is better
n/a

Reve 2.1

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.

Reve 2.1

Subscription
No Reve subscription includes this model. Through other providers from ≈ €12.72 a month (Recraft Basic, with limited credits). 3 plans in Germany.
Via API
No API price known. Access: .

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.

Arena rating (LMArena) from Nov 6, 2026 · 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.

KennzahlReve 2.1LLaDA-Image
Ratings
Arena ratingfrom votes by users, higher is better1,300n/a
Arena rankRank 6 of 21n/a
Arena votesmore votes, a safer rating8,237n/a
GraduallyBenchour own rating, 0 to 1007348
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition5462
Product photographyMaterials, reflections, and fine details7435
Character and detailAnatomy, clothing, and spatial consistency8063
InfographicNumbers, labels, and visual organization8533
API price
Per imagelower is bettern/an/a
100 imagesn/an/a
Profile
ReleasedJuly 2026Sep 4, 2026
Weightsopen means: runs locallyproprietaryopen
LifecycleRetired; no new image or video generation after September 27, 2026. Existing assets can be downloaded until October 31, 2026no notice
Accesslocal
Parametersnot published6 billion
Largest output4Knot consistently published
Plans in Germanynaming exactly this model3n/a

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

Sources: Reve 2.1 model source, status source (verified Aug 15, 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 Reve 2.1 and LLaDA-Image from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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