Vidu Q2 Imagevs.Boogu-Image-0.1-Base
Which one is better?
In our own test, Vidu Q2 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.
Result for “Product photography”:Vidu Q2 Image ahead
What the rating looks at: Materials, reflections, and fine details.
“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
Vidu Q2 Image: fal, aspect ratio 1:1; use the first provider output, one request, first output, seed=provider random, Budget reserves $0.10 per image, the public page rate; API metadata lists $0.05, estimated cost $0.100000, source 1440x1440, stored 1024x1024 (Lanczos3 downscale), generated Nov 2, 2026, 3:17 PM. Source
Boogu-Image-0.1-Base: manual, local PyTorch 2.11.0, Diffusers 0.38.0; Boogu/Boogu-Image-0.1-Base@334ad7e5c6343d33308b306127e047b133e86926; native 1024x1024, 1:1; one request, first output; steps=50, guidance=4.0, seed=895870850114772015; 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 Base sampling, no Turbo DMD inference, generated Nov 2, 2026, 11:56 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6
Vidu Q2 Image image SHA 256: 6fff5b245381e9812626ed77dbfc2339d20e769e7dccd1104c9c1f19283f1cb7
Boogu-Image-0.1-Base image SHA 256: 5ca2782a121b2ffdd620953a4453df06769a5d51778c7980e1cddc8b8e79e0dd
Infographic
Vidu Q2 Image: fal, aspect ratio 1:1; use the first provider output, one request, first output, seed=provider random, Budget reserves $0.10 per image, the public page rate; API metadata lists $0.05, estimated cost $0.100000, source 1440x1440, stored 1024x1024 (Lanczos3 downscale), generated Nov 2, 2026, 3:18 PM. Source
Boogu-Image-0.1-Base: manual, local PyTorch 2.11.0, Diffusers 0.38.0; Boogu/Boogu-Image-0.1-Base@334ad7e5c6343d33308b306127e047b133e86926; native 1024x1024, 1:1; one request, first output; steps=50, guidance=4.0, seed=7796952798756533109; 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 Base sampling, no Turbo DMD inference, generated Nov 3, 2026, 12:04 AM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f
Vidu Q2 Image image SHA 256: f05906a0af47e56f355e332694eb092267260b475ce90c0f5e4242ce91462934
Boogu-Image-0.1-Base image SHA 256: 7b4a49a08dcb03776edaf18345ffe94ee7ae0293be8b3611bf676aa6c76c6405
Product photography
Vidu Q2 Image: fal, aspect ratio 1:1; use the first provider output, one request, first output, seed=provider random, Budget reserves $0.10 per image, the public page rate; API metadata lists $0.05, estimated cost $0.100000, source 1440x1440, stored 1024x1024 (Lanczos3 downscale), generated Nov 2, 2026, 3:17 PM. Source
Boogu-Image-0.1-Base: manual, local PyTorch 2.11.0, Diffusers 0.38.0; Boogu/Boogu-Image-0.1-Base@334ad7e5c6343d33308b306127e047b133e86926; native 1024x1024, 1:1; one request, first output; steps=50, guidance=4.0, seed=6866357448264114469; 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 Base sampling, no Turbo DMD inference, generated Nov 2, 2026, 11:47 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3
Vidu Q2 Image image SHA 256: 1a83428b6d86bfee63528b77c9a6a9a6d96b4fe80e7b76e00727a7a895f92b58
Boogu-Image-0.1-Base image SHA 256: d0e66f73a26844f514cb97bbbd69bb8f4bf39bd875ac8fca0f04989949a64478
Typography and layout
Vidu Q2 Image: fal, aspect ratio 1:1; use the first provider output, one request, first output, seed=provider random, Budget reserves $0.10 per image, the public page rate; API metadata lists $0.05, estimated cost $0.100000, source 1440x1440, stored 1024x1024 (Lanczos3 downscale), generated Nov 2, 2026, 3:11 PM. Source
Boogu-Image-0.1-Base: manual, local PyTorch 2.11.0, Diffusers 0.38.0; Boogu/Boogu-Image-0.1-Base@334ad7e5c6343d33308b306127e047b133e86926; native 1024x1024, 1:1; one request, first output; steps=50, guidance=4.0, seed=5844980489998903917; 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 Base sampling, no Turbo DMD inference, generated Nov 2, 2026, 11:36 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419
Vidu Q2 Image image SHA 256: 1858a3661e9c0a89560e6d717c851c168d43eb9b80f0d169d61749d608365ba2
Boogu-Image-0.1-Base image SHA 256: b9abc77788966e5c8425e7a94c12632b4e4ec11e6d41c7afd0e1acdc37602db3
The numbers at a glance
Vidu Q2 Image
not in the arena
Boogu-Image-0.1-Base
not in the arena
Vidu Q2 Image
Rank 85 of 108
Boogu-Image-0.1-Base
Rank 103 of 108
Vidu Q2 Image
no API price
Boogu-Image-0.1-Base
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.
Vidu Q2 Image
- Subscription
- n/a No plan in Germany names exactly this model.
- Via API
- No API price known. Access: API.
- Weights
- unknown.
Boogu-Image-0.1-Base
- 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 | Vidu Q2 Image | Boogu-Image-0.1-Base |
|---|---|---|
| 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 | 31 | 10 |
| GraduallyBench by task | ||
| Typography and layoutLegible text, hierarchy, and composition | 27 | 9 |
| Product photographyMaterials, reflections, and fine details | 32 | 8 |
| Character and detailAnatomy, clothing, and spatial consistency | 26 | 8 |
| InfographicNumbers, labels, and visual organization | 37 | 16 |
| API price | ||
| Per imagelower is better | n/a | n/a |
| 100 images | n/a | n/a |
| Profile | ||
| Released | n/a | n/a |
| Weightsopen means: runs locally | unknown | open |
| Access | API | local |
| Parameters | not published | 10 billion |
| Largest output | 4K | 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: Vidu Q2 Image model source (verified Nov 1, 2026) · Boogu-Image-0.1-Base 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 Vidu Q2 Image and Boogu-Image-0.1-Base from the same arena and category. Older model versions are not used as substitutes.
More comparisons
Compare Vidu Q2 Image and Boogu-Image-0.1-Base with the models people compare them with most often.