Muse Imagevs.Boogu-Image-0.1-Turbo
Which one is better?
In our own test, Muse Image leads. Boogu-Image-0.1-Turbo has no arena rating yet. Boogu-Image-0.1-Turbo has no API price.
Test images
Same task, the first image of each. The task with the biggest difference is preselected.
Result for “Infographic”:Muse Image ahead
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
Muse Image: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.010000, source 1600x1600, stored 1024x1024 (Lanczos3 downscale), generated Nov 1, 2026, 8:48 AM. 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
Muse Image image SHA 256: 356427fe1ec15a444aae4eab2194344c7fe6b17ef205a8db16c1d64dbacb750e
Boogu-Image-0.1-Turbo image SHA 256: 0c90d3a3e12870784d8b576ac3ace689b6cb627a4222f7cfc4a0d3793b9e4a37
Infographic
Muse Image: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.010000, source 1600x1600, stored 1024x1024 (Lanczos3 downscale), generated Nov 1, 2026, 8:49 AM. 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
Muse Image image SHA 256: c8b096001990f57cb5225f6387485b391a4a5e3c3031e7b0524a4c19e1e01bc8
Boogu-Image-0.1-Turbo image SHA 256: 096ae82e5a28ef1095f159019c41983c84a0c670a76e752fb3a1f9600fa7c45a
Product photography
Muse Image: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.010000, source 1600x1600, stored 1024x1024 (Lanczos3 downscale), generated Nov 1, 2026, 8:48 AM. 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
Muse Image image SHA 256: 0cb5d4c3dbe33c840ba87f0e8af255deec9e7e02eb6c7f172310af433e78fdb6
Boogu-Image-0.1-Turbo image SHA 256: 50719aadf378d21848c83dbdb510a8134c80be05cbb2b718182a95e9cbb28164
Typography and layout
Muse Image: fal, aspect ratio 1:1, n=1, seed=provider random, estimated cost $0.010000, source 1600x1600, stored 1024x1024 (Lanczos3 downscale), generated Nov 1, 2026, 8:48 AM. 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
Muse Image image SHA 256: b8232890082d6bdd72dd7be99d98a573566a2c62f94597ecdda2bd9110e2e143
Boogu-Image-0.1-Turbo image SHA 256: cec0e99f796e6d0e4dafc517afc93fd93041aecbd52cef74a421ea9c3a15ab69
The numbers at a glance
Muse Image
Rank 7 of 21
Boogu-Image-0.1-Turbo
not in the arena
Muse Image
Rank 4 of 108
Boogu-Image-0.1-Turbo
Rank 104 of 108
Muse Image
cheap
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.
Muse Image
- Subscription
- Free to use at Meta (Meta AI Free). Through other providers from ≈ €9.54 a month (Krea Basic, with limited credits). 6 plans in Germany.
- Via API
- $0.01 per image, 100 images $1.
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.
Arena rating (LMArena) from Nov 6, 2026 · GraduallyBench from Nov 3, 2026 · API prices from Nov 6, 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 | Muse Image | Boogu-Image-0.1-Turbo |
|---|---|---|
| Ratings | ||
| Arena ratingfrom votes by users, higher is better | 1,268 | n/a |
| Arena rank | Rank 7 of 21 | n/a |
| Arena votesmore votes, a safer rating | 57,568 | n/a |
| GraduallyBenchour own rating, 0 to 100 | 78 | 9 |
| GraduallyBench by task | ||
| Typography and layoutLegible text, hierarchy, and composition | 86 | 18 |
| Product photographyMaterials, reflections, and fine details | 81 | 6 |
| Character and detailAnatomy, clothing, and spatial consistency | 61 | 5 |
| InfographicNumbers, labels, and visual organization | 86 | 8 |
| API price | ||
| Per imagelower is better | $0.01 | n/a |
| 100 images | $1 | n/a |
| Profile | ||
| Released | Jul 7, 2026 | n/a |
| Weightsopen means: runs locally | proprietary | open |
| Access | API, Web app | local |
| Parameters | not published | 10 billion |
| Largest output | not consistently published | not consistently published |
| Plans in Germanynaming exactly this model | 6 | n/a |
API prices for one image at 1,024 × 1,024 pixels according to Artificial Analysis.
Sources: Muse Image model source (verified Sep 26, 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 Muse Image and Boogu-Image-0.1-Turbo from the same arena and category. Older model versions are not used as substitutes.
More comparisons
Compare Muse Image and Boogu-Image-0.1-Turbo with the models people compare them with most often.