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Reve 2.1vs.LLaDA-Image-Turbo

Which one is better?

In our own test, Reve 2.1 leads. LLaDA-Image-Turbo 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-Turbo: Infographic, first image from the fixed test run
LLaDA-Image-Turbo36 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.

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-Turbo: manual, 1:1, native 1024x1024, one request in the successful technical repeat, first output; two total submissions after a local download failure; seed 0 (fixed provider default, randomize_seed=false); BF16 transformer, FP8 text encoder; 4 steps, CFG 1.0; HF Space commit 7b02431121944d65b8065873b48eec736a0c1022; source PNG SHA-256 d1866b4994a664728de07f1c1897776bbf04409b4a00ad7fa98666036a78816d, generated Nov 2, 2026, 3:03 PM. Source

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

Reve 2.1 image SHA 256: 84bcd1680fd0ae861ec66c21ab8f4cd79738bd093eb87ffe7283d9842c37cb1d

LLaDA-Image-Turbo image SHA 256: de0c780506575a32050ce9ca3d3c4dd1a0c4cc7ccf7f7f2f6aaf17ad01295901

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-Turbo: manual, PyTorch 2.13.0, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image-Turbo@f4afc52d925bbac4e22a1c947111fc1f127e37e5; native 1024x1024, 1:1; one request per local attempt, first output; steps=4, guidance=1.0; seed=0; separate MPS initial-latent generator and global MPS scheduler RNG streams with the same integer seed; original Turbo scheduler, stochastic_sampling=true, uniform sigmas, shift=3; BF16 text encoder and diffusion components, FP32 denoising latent and CPU FP32 VAE decode; historical poster uses FP8 text encoder and remains unchanged; no CUDA equivalence claimed; MPS fallback permitted, actual fallback operations not measured; historical Hugging Face Space product-photo attempt used the same archived prompt and seed 0 but a different request body and backend; its AppError left the output unresolved; this is not an exact request replay or a numerical-equivalence comparison, generated Nov 3, 2026, 9:48 AM. Source

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

Reve 2.1 image SHA 256: 1f91c3cb481151fb5165194b4edea2a46e786ca80e4ec3bdd888e3ba0016c510

LLaDA-Image-Turbo image SHA 256: e61fb35e0c12941d28d32b0e0125b934bc0857fc49216a7945406e3bf58b4316

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-Turbo: manual, PyTorch 2.13.0, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image-Turbo@f4afc52d925bbac4e22a1c947111fc1f127e37e5; native 1024x1024, 1:1; one request per local attempt, first output; steps=4, guidance=1.0; seed=7961710876241130415; separate MPS initial-latent generator and global MPS scheduler RNG streams with the same integer seed; original Turbo scheduler, stochastic_sampling=true, uniform sigmas, shift=3; BF16 text encoder and diffusion components, FP32 denoising latent and CPU FP32 VAE decode; historical poster uses FP8 text encoder and remains unchanged; no CUDA equivalence claimed; MPS fallback permitted, actual fallback operations not measured, generated Nov 3, 2026, 9:54 AM. Source

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

Reve 2.1 image SHA 256: 29e9f7f38ed5c63dcfaab671a49fb9f4e9f82cd24b99134b87e1c22442822126

LLaDA-Image-Turbo image SHA 256: aac02152ef0efc45749b3d0a626e9f4114418394aeb6a92d492651c564f40fee

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-Turbo: manual, PyTorch 2.13.0, Diffusers 0.39.0, Transformers 4.57.6; inclusionAI/LLaDA-Image-Turbo@f4afc52d925bbac4e22a1c947111fc1f127e37e5; native 1024x1024, 1:1; one request per local attempt, first output; steps=4, guidance=1.0; seed=5156250474893981751; separate MPS initial-latent generator and global MPS scheduler RNG streams with the same integer seed; original Turbo scheduler, stochastic_sampling=true, uniform sigmas, shift=3; BF16 text encoder and diffusion components, FP32 denoising latent and CPU FP32 VAE decode; historical poster uses FP8 text encoder and remains unchanged; no CUDA equivalence claimed; MPS fallback permitted, actual fallback operations not measured, generated Nov 3, 2026, 9:56 AM. Source

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

Reve 2.1 image SHA 256: 30d157e85d75eb2ca1f71152d27f26d378ad8ea49b899d5f350ae0cda4e2bc59

LLaDA-Image-Turbo image SHA 256: 4e8939998310be86b1ad3dc0744a5e3ceee95a00a9113ee6732f9c06cb6b3618

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-Turbo

not in the arena

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

Reve 2.1

Rank 13 of 108

31

LLaDA-Image-Turbo

Rank 83 of 108

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

Reve 2.1

no API price

n/a

LLaDA-Image-Turbo

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-Turbo

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-Turbo
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 1007331
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition548
Product photographyMaterials, reflections, and fine details7427
Character and detailAnatomy, clothing, and spatial consistency8053
InfographicNumbers, labels, and visual organization8536
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-Turbo 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-Turbo from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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