Stable Diffusion 3.5 Largevs.Kandinsky-5.0-T2I-Lite-Pretrain
Which one is better?
In our own test both are on par. Kandinsky-5.0-T2I-Lite-Pretrain has no arena rating yet. 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”:Kandinsky-5.0-T2I-Lite-Pretrain 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
Stable Diffusion 3.5 Large: fal, requested 1024x1024, n=1, seed=7087247023770270000, estimated cost $0.068157, generated Aug 14, 2026, 2:37 PM. Source
Kandinsky-5.0-T2I-Lite-Pretrain: manual, local PyTorch 2.13.0, Diffusers 0.36.0; kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers@0df26d22e3290e9778b0471457d651a51d83ae3d; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=893590444446603478; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoders; float32 for transformer and CPU VAE; native tokenizer truncation: CLIP 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 8:56 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6
Stable Diffusion 3.5 Large image SHA 256: e748006fe56e442ad347e3f994f18b86ee1739f20dcee063aa7a03ee710c5d54
Kandinsky-5.0-T2I-Lite-Pretrain image SHA 256: af9bdf8492b30fdaeb79b8f51d168a948d43ee1bbb29107f50a2f794db91a2e0
Infographic
Stable Diffusion 3.5 Large: fal, requested 1024x1024, n=1, seed=12166653711354006000, estimated cost $0.068157, generated Aug 14, 2026, 2:37 PM. Source
Kandinsky-5.0-T2I-Lite-Pretrain: manual, local PyTorch 2.13.0, Diffusers 0.36.0; kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers@0df26d22e3290e9778b0471457d651a51d83ae3d; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=6008496123190594626; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoders; float32 for transformer and CPU VAE, generated Nov 2, 2026, 9:08 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f
Stable Diffusion 3.5 Large image SHA 256: 4eb0fd741ef42e6d3fee4b1280cdfb098a0a04009b139051963895892e349f1e
Kandinsky-5.0-T2I-Lite-Pretrain image SHA 256: 6d1a99203c55f6a9ba5853aae207dfc5e264adc73b336253fae874f0243bd60e
Product photography
Stable Diffusion 3.5 Large: fal, requested 1024x1024, n=1, seed=13346308312015933000, estimated cost $0.068157, generated Aug 14, 2026, 2:37 PM. Source
Kandinsky-5.0-T2I-Lite-Pretrain: manual, local PyTorch 2.13.0, Diffusers 0.36.0; kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers@0df26d22e3290e9778b0471457d651a51d83ae3d; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=8854898730028115945; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoders; float32 for transformer and CPU VAE; native tokenizer truncation: CLIP 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 8:46 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3
Stable Diffusion 3.5 Large image SHA 256: ea9a7401dee8859ec2cb96af9f7ca29a177ef2202f52f629d8ce9f5839bd4061
Kandinsky-5.0-T2I-Lite-Pretrain image SHA 256: 83169f5969d6ed636c5ba8ff01236290417a5942deec1089b4725cfeb0dc2b76
Typography and layout
Stable Diffusion 3.5 Large: fal, requested 1024x1024, n=1, seed=16632333557910893000, estimated cost $0.068157, generated Aug 14, 2026, 2:37 PM. Source
Kandinsky-5.0-T2I-Lite-Pretrain: manual, local PyTorch 2.13.0, Diffusers 0.36.0; kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers@0df26d22e3290e9778b0471457d651a51d83ae3d; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=9114026266664490789; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoders; float32 for transformer and CPU VAE, generated Nov 2, 2026, 8:36 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419
Stable Diffusion 3.5 Large image SHA 256: e2994b1883a245b1c8c447957d121ea89d93aafff149029aa4e8507dbb769f9a
Kandinsky-5.0-T2I-Lite-Pretrain image SHA 256: 89cd2a1137d4d2ceb46c44b030f889bb3b14ac96ae190d2eb271dff02cfb5b74
The numbers at a glance
Stable Diffusion 3.5 Large
Rank 21 of 21
Kandinsky-5.0-T2I-Lite-Pretrain
not in the arena
Stable Diffusion 3.5 Large
Rank 87 of 108
Kandinsky-5.0-T2I-Lite-Pretrain
Rank 86 of 108
Stable Diffusion 3.5 Large
no API price
Kandinsky-5.0-T2I-Lite-Pretrain
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.
Stable Diffusion 3.5 Large
- Subscription
- n/a No plan in Germany names exactly this model.
- Via API
- No API price known. Access: API, local.
- License
- Open weights.
Kandinsky-5.0-T2I-Lite-Pretrain
- 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 · 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 | Stable Diffusion 3.5 Large | Kandinsky-5.0-T2I-Lite-Pretrain |
|---|---|---|
| Ratings | ||
| Arena ratingfrom votes by users, higher is better | 938 | n/a |
| Arena rank | Rank 21 of 21 | n/a |
| Arena votesmore votes, a safer rating | 23,396 | n/a |
| GraduallyBenchour own rating, 0 to 100 | 27 | 28 |
| GraduallyBench by task | ||
| Typography and layoutLegible text, hierarchy, and composition | 31 | 16 |
| Product photographyMaterials, reflections, and fine details | 17 | 58 |
| Character and detailAnatomy, clothing, and spatial consistency | 23 | 14 |
| InfographicNumbers, labels, and visual organization | 39 | 23 |
| API price | ||
| Per imagelower is better | n/a | n/a |
| 100 images | n/a | n/a |
| Profile | ||
| Released | Oct 22, 2024 | n/a |
| Weightsopen means: runs locally | open | open |
| Access | API, local | local |
| Parameters | 8.1 billion | 6 billion |
| Largest output | 1 Megapixel | 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: Stable Diffusion 3.5 Large model source (verified Aug 15, 2026) · Kandinsky-5.0-T2I-Lite-Pretrain 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 Stable Diffusion 3.5 Large and Kandinsky-5.0-T2I-Lite-Pretrain from the same arena and category. Older model versions are not used as substitutes.
More comparisons
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