LongCat-Imagevs.Boogu-Image-0.1-Turbo

Which one is better?

In our own test, LongCat-Image 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.

LongCat-Image: Infographic, first image from the fixed test run — LongCat-Image37 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”:LongCat-Image 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

LongCat-Image: fal, image_size=square_hd; exact native pixels are not request-configurable, n=1, seed=1234871882, Estimate reserves 2 MP at Fal's $0.13 per MP rate, estimated cost $0.260000, generated Nov 2, 2026, 3:06 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

LongCat-Image image SHA 256: 7c2d3cbf8efa756277fc1e20af0c0c7c4febec50b30244eb9b85c02c236e97ca

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

Infographic

LongCat-Image: fal, image_size=square_hd; exact native pixels are not request-configurable, n=1, seed=886012823, Estimate reserves 2 MP at Fal's $0.13 per MP rate, estimated cost $0.260000, generated Nov 2, 2026, 3:06 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

LongCat-Image image SHA 256: ce6df19ed744009c81aec8257cef3e5a4de7ea0d4985c709335ec1b50c3381fb

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

Product photography

LongCat-Image: fal, image_size=square_hd; exact native pixels are not request-configurable, n=1, seed=1319752608, Estimate reserves 2 MP at Fal's $0.13 per MP rate, estimated cost $0.260000, generated Nov 2, 2026, 3:06 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

LongCat-Image image SHA 256: 1f95d4152dfa60c5056df13bf813563a75863f4a34419344e1a59a800a46f689

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

Typography and layout

LongCat-Image: fal, image_size=square_hd; exact native pixels are not request-configurable, n=1, seed=943200407, Estimate reserves 2 MP at Fal's $0.13 per MP rate, estimated cost $0.260000, generated Nov 2, 2026, 3:06 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

LongCat-Image image SHA 256: 1d6b72ae389c339ff3011ee3c59bd77b12b5157f1fadcf0932c96ebe5313836b

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

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

LongCat-Image

not in the arena

n/a

Boogu-Image-0.1-Turbo

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

LongCat-Image

Rank 80 of 108

Boogu-Image-0.1-Turbo

Rank 104 of 108

Price per imagedollars through the API · lower is better

n/a

LongCat-Image

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.

LongCat-Image

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

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	LongCat-Image	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	33	9
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	41	18
Product photographyMaterials, reflections, and fine details	29	6
Character and detailAnatomy, clothing, and spatial consistency	24	5
InfographicNumbers, labels, and visual organization	37	8
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	Dec 5, 2025	n/a
Weightsopen means: runs locally	open	open
Access	local	local
Parameters	6 billion	10 billion
Largest output	not consistently published	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: LongCat-Image model source (verified Nov 1, 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 LongCat-Image 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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