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Qwen Image 2.0 Provs.Boogu-Image-0.1-Base

Which one is better?

In our own test, Qwen Image 2.0 Pro leads. Neither is in the arena. Boogu-Image-0.1-Base has no API price.

Test images

Same task, the first image of each. The task with the biggest difference is preselected.

Qwen Image 2.0 Pro: Character and detail, first image from the fixed test run
Qwen Image 2.0 Pro89 out of 100
Boogu-Image-0.1-Base: Character and detail, first image from the fixed test run
Boogu-Image-0.1-Base8 out of 100

Result for “Character and detail”:Qwen Image 2.0 Pro ahead

Prompt given to both modelsCreate a square cinematic full-body portrait of a fictional bicycle courier waiting under a transparent umbrella at a rainy tram stop in Hamburg at blue hour. She wears a mustard raincoat, navy trousers, red sneakers, a silver helmet, and carries a teal messenger bag. Keep both hands visible, render the bicycle correctly, use realistic wet-street reflections, and include no readable brand names.

What the rating looks at: Anatomy, clothing, and spatial consistency.

“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

Qwen Image 2.0 Pro: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:21 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

Qwen Image 2.0 Pro image SHA 256: 05f19da3e2d0eedb17d098c1b21abf8c83c663afa59083f0ce7a67a948e8a263

Boogu-Image-0.1-Base image SHA 256: 5ca2782a121b2ffdd620953a4453df06769a5d51778c7980e1cddc8b8e79e0dd

Infographic

Qwen Image 2.0 Pro: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:22 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

Qwen Image 2.0 Pro image SHA 256: 003fbe859de5df62a4f49e3d590c00f5036e208149d81226d1835871fd1e0410

Boogu-Image-0.1-Base image SHA 256: 7b4a49a08dcb03776edaf18345ffe94ee7ae0293be8b3611bf676aa6c76c6405

Product photography

Qwen Image 2.0 Pro: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:21 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

Qwen Image 2.0 Pro image SHA 256: b811a440655cae766c039dfc8f9efb1756be5dce63ef22cd95deb1fe7dab6a3a

Boogu-Image-0.1-Base image SHA 256: d0e66f73a26844f514cb97bbbd69bb8f4bf39bd875ac8fca0f04989949a64478

Typography and layout

Qwen Image 2.0 Pro: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 2:00 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

Qwen Image 2.0 Pro image SHA 256: 4eb4915afa71f6a8c76979dcecadd70beabd3869f11ca092a725fd7b6c87b4f2

Boogu-Image-0.1-Base image SHA 256: b9abc77788966e5c8425e7a94c12632b4e4ec11e6d41c7afd0e1acdc37602db3

The numbers at a glance

Arena ratingfrom votes by users · higher is better
n/a

Qwen Image 2.0 Pro

not in the arena

n/a

Boogu-Image-0.1-Base

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better
72

Qwen Image 2.0 Pro

Rank 17 of 108

10

Boogu-Image-0.1-Base

Rank 103 of 108

Price per imagedollars through the API · lower is better
$0.075

Qwen Image 2.0 Pro

fairly expensive

n/a

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.

Qwen Image 2.0 Pro

Subscription
At Alibaba only in team or business plans, from ≈ €21.20 a month per seat (Token Plan Team Team Standard Seat). 3 plans in Germany.
Via API
$0.075 per image, 100 images $7.50.

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 · 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.

KennzahlQwen Image 2.0 ProBoogu-Image-0.1-Base
Ratings
Arena ratingfrom votes by users, higher is bettern/an/a
Arena rankn/an/a
Arena votesmore votes, a safer ratingn/an/a
GraduallyBenchour own rating, 0 to 1007210
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition819
Product photographyMaterials, reflections, and fine details588
Character and detailAnatomy, clothing, and spatial consistency898
InfographicNumbers, labels, and visual organization6216
API price
Per imagelower is better$0.075n/a
100 images$7.50n/a
Profile
ReleasedApril 2026n/a
Weightsopen means: runs locallyproprietaryopen
AccessAPIlocal
Parametersnot published10 billion
Largest output2,048 × 2,048 pixelsnot 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: Qwen Image 2.0 Pro model source (verified Aug 15, 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 Qwen Image 2.0 Pro and Boogu-Image-0.1-Base from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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