Ideogram 4.0vs.Boogu-Image-0.1-Turbo
Which one is better?
In our own test, Ideogram 4.0 leads. Neither is in the arena. 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”:Ideogram 4.0 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
Ideogram 4.0: together, requested 2048x2048, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:28 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
Ideogram 4.0 image SHA 256: d63d24515e4c3a8b7feafecaf995a511433307ae5faae1a1c26b2f0289578af9
Boogu-Image-0.1-Turbo image SHA 256: 0c90d3a3e12870784d8b576ac3ace689b6cb627a4222f7cfc4a0d3793b9e4a37
Infographic
Ideogram 4.0: together, requested 2048x2048, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:28 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
Ideogram 4.0 image SHA 256: f57e6f5b7ae9038953087720387aa4a901832a1794dbb5e3f337f9d3ef213d62
Boogu-Image-0.1-Turbo image SHA 256: 096ae82e5a28ef1095f159019c41983c84a0c670a76e752fb3a1f9600fa7c45a
Product photography
Ideogram 4.0: together, requested 2048x2048, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:27 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
Ideogram 4.0 image SHA 256: 59b59cf5294653460f06085eedcfeb676b8c50f88f58f038b32659841f0500dd
Boogu-Image-0.1-Turbo image SHA 256: 50719aadf378d21848c83dbdb510a8134c80be05cbb2b718182a95e9cbb28164
Typography and layout
Ideogram 4.0: together, requested 2048x2048, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 2:01 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
Ideogram 4.0 image SHA 256: 088eabf7b8c37bd377011dd2649a2deeee49ffb903e4798a6930bb94144f1eb4
Boogu-Image-0.1-Turbo image SHA 256: cec0e99f796e6d0e4dafc517afc93fd93041aecbd52cef74a421ea9c3a15ab69
The numbers at a glance
Ideogram 4.0
not in the arena
Boogu-Image-0.1-Turbo
not in the arena
Ideogram 4.0
Rank 57 of 108
Boogu-Image-0.1-Turbo
Rank 104 of 108
Ideogram 4.0
fairly 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.
Ideogram 4.0
- Subscription
- Free to use at Ideogram (Ideogram Free, with limited credits), more from ≈ €21.20 a month (Ideogram Plus, with limited credits). Through other providers from ≈ €9.54 a month (Krea Basic, with limited credits). 16 plans in Germany.
- Via API
- $0.06 per image, 100 images $6.
- 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 · 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 | Ideogram 4.0 | 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 | 55 | 9 |
| GraduallyBench by task | ||
| Typography and layoutLegible text, hierarchy, and composition | 67 | 18 |
| Product photographyMaterials, reflections, and fine details | 22 | 6 |
| Character and detailAnatomy, clothing, and spatial consistency | 59 | 5 |
| InfographicNumbers, labels, and visual organization | 73 | 8 |
| API price | ||
| Per imagelower is better | $0.06 | n/a |
| 100 images | $6 | n/a |
| Profile | ||
| Released | June 2026 | n/a |
| Weightsopen means: runs locally | open | open |
| Access | API, Web app, local | local |
| Parameters | 9.3 billion | 10 billion |
| Largest output | 2K | not consistently published |
| Plans in Germanynaming exactly this model | 16 | n/a |
API prices for one image at 1,024 × 1,024 pixels according to Artificial Analysis.
Sources: Ideogram 4.0 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 Ideogram 4.0 and Boogu-Image-0.1-Turbo from the same arena and category. Older model versions are not used as substitutes.
More comparisons
Compare Ideogram 4.0 and Boogu-Image-0.1-Turbo with the models people compare them with most often.