Luma Uni-1.1 Maxvs.Boogu-Image-0.1-Turbo

Which one is better?

In our own test, Luma Uni-1.1 Max 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.

Luma Uni-1.1 Max: Infographic, first image from the fixed test run — Luma Uni-1.1 Max79 out of 100

Boogu-Image-0.1-Turbo: Infographic, first image from the fixed test run — Boogu-Image-0.1-Turbo8 out of 100

Result for “Infographic”:Luma Uni-1.1 Max 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

Luma Uni-1.1 Max: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.102000, source 2048x2048, stored 1024x1024 (Lanczos3 downscale), generated Aug 14, 2026, 1:47 PM. Source

Boogu-Image-0.1-Turbo: manual, local PyTorch 2.11.0, Diffusers 0.38.0; Boogu/Boogu-Image-0.1-Turbo@47cd26d3c211b030c64ffe60d63b2e17a7c519c2; native 1024x1024, 1:1; one request, first output; steps=4, guidance=1.0, seed=2111621457284287488; mps; native PyTorch fallback is enabled by PYTORCH_ENABLE_MPS_FALLBACK=1; actual CPU operation use is not measured; bfloat16; upstream boogu-project/Boogu-Image@9b327526b1053e1f27f1bd0ae36b20ce2c39cb9e; reviewed MPS device allowlist and empty-reference self-attention only on original CPU SDPA, unchanged arguments and dtype; nonempty attention remains on MPS; no CUDA numeric equivalence claimed; native VAE input saved after scaling and shift, decoded float output finite before native same-size interpolation; native instruction template without rewriting; original DMD student inference, conditioning sigma 0.001, generated Nov 2, 2026, 11:24 PM. Source

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

Luma Uni-1.1 Max image SHA 256: 8034ca5627e1a256cdf1d06fc87b583d91d971d02edc9af7df52cd6721d3ea9c

Boogu-Image-0.1-Turbo image SHA 256: 0c90d3a3e12870784d8b576ac3ace689b6cb627a4222f7cfc4a0d3793b9e4a37

Infographic

Luma Uni-1.1 Max: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.102000, source 2048x2048, stored 1024x1024 (Lanczos3 downscale), generated Aug 14, 2026, 1:49 PM. Source

Boogu-Image-0.1-Turbo: manual, local PyTorch 2.11.0, Diffusers 0.38.0; Boogu/Boogu-Image-0.1-Turbo@47cd26d3c211b030c64ffe60d63b2e17a7c519c2; native 1024x1024, 1:1; one request, first output; steps=4, guidance=1.0, seed=8524023330556932596; mps; native PyTorch fallback is enabled by PYTORCH_ENABLE_MPS_FALLBACK=1; actual CPU operation use is not measured; bfloat16; upstream boogu-project/Boogu-Image@9b327526b1053e1f27f1bd0ae36b20ce2c39cb9e; reviewed MPS device allowlist and empty-reference self-attention only on original CPU SDPA, unchanged arguments and dtype; nonempty attention remains on MPS; no CUDA numeric equivalence claimed; native VAE input saved after scaling and shift, decoded float output finite before native same-size interpolation; native instruction template without rewriting; original DMD student inference, conditioning sigma 0.001, generated Nov 2, 2026, 11:24 PM. Source

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

Luma Uni-1.1 Max image SHA 256: 36dea214eb680804d4fea089eec3d4f10a4bf7b7a71c7a80f92313a37e8c2f73

Boogu-Image-0.1-Turbo image SHA 256: 096ae82e5a28ef1095f159019c41983c84a0c670a76e752fb3a1f9600fa7c45a

Product photography

Luma Uni-1.1 Max: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.102000, source 2048x2048, stored 1024x1024 (Lanczos3 downscale), generated Aug 14, 2026, 1:43 PM. Source

Boogu-Image-0.1-Turbo: manual, local PyTorch 2.11.0, Diffusers 0.38.0; Boogu/Boogu-Image-0.1-Turbo@47cd26d3c211b030c64ffe60d63b2e17a7c519c2; native 1024x1024, 1:1; one request, first output; steps=4, guidance=1.0, seed=9219148100655939974; mps; native PyTorch fallback is enabled by PYTORCH_ENABLE_MPS_FALLBACK=1; actual CPU operation use is not measured; bfloat16; upstream boogu-project/Boogu-Image@9b327526b1053e1f27f1bd0ae36b20ce2c39cb9e; reviewed MPS device allowlist and empty-reference self-attention only on original CPU SDPA, unchanged arguments and dtype; nonempty attention remains on MPS; no CUDA numeric equivalence claimed; native VAE input saved after scaling and shift, decoded float output finite before native same-size interpolation; native instruction template without rewriting; original DMD student inference, conditioning sigma 0.001, generated Nov 2, 2026, 11:23 PM. Source

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

Luma Uni-1.1 Max image SHA 256: 4890cdf8b858c5740124e3b2ba038b73f6a35decc7e3e3d920890462b6e89c2b

Boogu-Image-0.1-Turbo image SHA 256: 50719aadf378d21848c83dbdb510a8134c80be05cbb2b718182a95e9cbb28164

Typography and layout

Luma Uni-1.1 Max: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.102000, source 2048x2048, stored 1024x1024 (Lanczos3 downscale), generated Aug 14, 2026, 2:40 PM. Source

Boogu-Image-0.1-Turbo: manual, local PyTorch 2.11.0, Diffusers 0.38.0; Boogu/Boogu-Image-0.1-Turbo@47cd26d3c211b030c64ffe60d63b2e17a7c519c2; native 1024x1024, 1:1; one request, first output; steps=4, guidance=1.0, seed=4970356096182813142; mps; native PyTorch fallback is enabled by PYTORCH_ENABLE_MPS_FALLBACK=1; actual CPU operation use is not measured; bfloat16; upstream boogu-project/Boogu-Image@9b327526b1053e1f27f1bd0ae36b20ce2c39cb9e; reviewed MPS device allowlist and empty-reference self-attention only on original CPU SDPA, unchanged arguments and dtype; nonempty attention remains on MPS; no CUDA numeric equivalence claimed; native VAE input saved after scaling and shift, decoded float output finite before native same-size interpolation; native instruction template without rewriting; original DMD student inference, conditioning sigma 0.001; two local attempts: original MPSGraph SIGABRT before output; one identical-prompt, identical-seed technical repeat after reviewed backend-only correction, first output retained; exact failing original operator is not proven, generated Nov 2, 2026, 11:18 PM. Source

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

Luma Uni-1.1 Max image SHA 256: d2a6162d544def0a283ed62f939b33252636d296a21142111851ecdedd2989d3

Boogu-Image-0.1-Turbo image SHA 256: cec0e99f796e6d0e4dafc517afc93fd93041aecbd52cef74a421ea9c3a15ab69

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Luma Uni-1.1 Max

not in the arena

n/a

Boogu-Image-0.1-Turbo

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Luma Uni-1.1 Max

Rank 20 of 108

Boogu-Image-0.1-Turbo

Rank 104 of 108

Price per imagedollars through the API · lower is better

n/a

Luma Uni-1.1 Max

no API price

n/a

Boogu-Image-0.1-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.

Luma Uni-1.1 Max

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

Boogu-Image-0.1-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.

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	Luma Uni-1.1 Max	Boogu-Image-0.1-Turbo
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	71	9
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	75	18
Product photographyMaterials, reflections, and fine details	71	6
Character and detailAnatomy, clothing, and spatial consistency	60	5
InfographicNumbers, labels, and visual organization	79	8
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	May 2026	n/a
Weightsopen means: runs locally	proprietary	open
Access	API, Web app	local
Parameters	not published	10 billion
Largest output	2K	not consistently published
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: Luma Uni-1.1 Max model source (verified Aug 15, 2026) · Boogu-Image-0.1-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 Luma Uni-1.1 Max and Boogu-Image-0.1-Turbo from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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