Nano Banana 2 Litevs.Boogu-Image-0.1-Turbo

Which one is better?

In our own test, Nano Banana 2 Lite 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.

Nano Banana 2 Lite: Product photography, first image from the fixed test run — Nano Banana 2 Lite87 out of 100

Boogu-Image-0.1-Turbo: Product photography, first image from the fixed test run — Boogu-Image-0.1-Turbo6 out of 100

Result for “Product photography”:Nano Banana 2 Lite ahead

Prompt given to both modelsCreate a square premium product photograph of a brushed titanium wristwatch standing upright on dark green marble. The watch has a cream dial, thin black hands set to 10:10, twelve distinct hour markers, a realistic crown, and a dark brown leather strap. Soft window light from the left, controlled reflections, shallow depth of field, no text, no logo, no extra objects.

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

Nano Banana 2 Lite: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:21 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

Nano Banana 2 Lite image SHA 256: 6c7d17b2a84129cbeba7d52c8de703773e9296562d9d603688256d2717e65a0c

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

Infographic

Nano Banana 2 Lite: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:21 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

Nano Banana 2 Lite image SHA 256: 11d8b5ddf15becefa6e4e32fca21729ff7d57f561a10e3a2b4b6f5c7a0aed6ef

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

Product photography

Nano Banana 2 Lite: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:21 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

Nano Banana 2 Lite image SHA 256: 30d9b09b92917eca84675448260a083ad368d9ea25376dabccac0f526f3a3ab7

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

Typography and layout

Nano Banana 2 Lite: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 2:00 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

Nano Banana 2 Lite image SHA 256: 9fab80fe1ad268b9ceceb4ef61ef1dd0e965dd3f28d377af7cf9cf201fdf47fa

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

The numbers at a glance

Arena ratingfrom votes by users · higher is better

1,249

Nano Banana 2 Lite

Rank 11 of 21

n/a

Boogu-Image-0.1-Turbo

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Nano Banana 2 Lite

Rank 21 of 108

Boogu-Image-0.1-Turbo

Rank 104 of 108

Price per imagedollars through the API · lower is better

$0.034

Nano Banana 2 Lite

fairly cheap

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.

Nano Banana 2 Lite

Subscription: Free to use at Google (Google AI Free, with limited credits), more from €4.99 a month (Google AI Plus, with limited credits). Through other providers from ≈ €6.36 a month (ElevenLabs Starter, with limited credits). 51 plans in Germany.
Via API: $0.034 per image, 100 images $3.36.

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	Nano Banana 2 Lite	Boogu-Image-0.1-Turbo
Ratings
Arena ratingfrom votes by users, higher is better	1,249	n/a
Arena rank	Rank 11 of 21	n/a
Arena votesmore votes, a safer rating	17,787	n/a
GraduallyBenchour own rating, 0 to 100	71	9
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	82	18
Product photographyMaterials, reflections, and fine details	87	6
Character and detailAnatomy, clothing, and spatial consistency	47	5
InfographicNumbers, labels, and visual organization	66	8
API price
Per imagelower is better	$0.034	n/a
100 images	$3.36	n/a
Profile
Released	June 2026	n/a
Weightsopen means: runs locally	proprietary	open
Access	API, Web app	local
Parameters	not published	10 billion
Largest output	1K	not consistently published
Plans in Germanynaming exactly this model	51	n/a

API prices for one image at 1,024 × 1,024 pixels according to Artificial Analysis.

Sources: Nano Banana 2 Lite 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 Nano Banana 2 Lite 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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