Wan 2.6 Text to Imagevs.Kandinsky-5.0-T2I-Lite-SFT
Which one is better?
In our own test, Wan 2.6 Text to Image leads. Neither is in the arena. Kandinsky-5.0-T2I-Lite-SFT has no API price.
Test images
Same task, the first image of each. The task with the biggest difference is preselected.
Result for “Infographic”:Wan 2.6 Text to Image 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
Wan 2.6 Text to Image: together, requested 1280x1280, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:30 PM. Source
Kandinsky-5.0-T2I-Lite-SFT: manual, local PyTorch 2.13.0, Diffusers 0.36.0; kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers@c0158b5b20858d1fc02088db7ed4d805a296498b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=89463658088346984; 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, 8:12 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6
Wan 2.6 Text to Image image SHA 256: cf49d7136a442f4f1c1ae29af99e917aa83bcbf501fdacfe3a13a3b3e74d7f9a
Kandinsky-5.0-T2I-Lite-SFT image SHA 256: 8d193e702ad1aa0fccdb45f5d1474d60e21d455fa58d13470edd2796af0f2aed
Infographic
Wan 2.6 Text to Image: together, requested 1280x1280, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:30 PM. Source
Kandinsky-5.0-T2I-Lite-SFT: manual, local PyTorch 2.13.0, Diffusers 0.36.0; kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers@c0158b5b20858d1fc02088db7ed4d805a296498b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=649868597087646690; 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, 8:21 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f
Wan 2.6 Text to Image image SHA 256: 28eecbf460194ec047237c35415d934929baee9de9938bdff435cd21a4416435
Kandinsky-5.0-T2I-Lite-SFT image SHA 256: e102016732db9e8e463504a83ac0770ff8a3c4c77918143d0f9dae8498c79101
Product photography
Wan 2.6 Text to Image: together, requested 1280x1280, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:29 PM. Source
Kandinsky-5.0-T2I-Lite-SFT: manual, local PyTorch 2.13.0, Diffusers 0.36.0; kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers@c0158b5b20858d1fc02088db7ed4d805a296498b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=4636200764941689006; 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, 8:02 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3
Wan 2.6 Text to Image image SHA 256: e8dbcfd0ce965814d72d676eddf74f723d476c1f78c5928c7b6efbc4eca4765e
Kandinsky-5.0-T2I-Lite-SFT image SHA 256: 73b31dc5a48059f7712c6a125ac4855f2401133101172c4bfbaa11b76c5ceee1
Typography and layout
Wan 2.6 Text to Image: together, requested 1280x1280, stored 1024x1024 (Lanczos3 downscale), n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 2:01 PM. Source
Kandinsky-5.0-T2I-Lite-SFT: manual, local PyTorch 2.13.0, Diffusers 0.36.0; kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers@c0158b5b20858d1fc02088db7ed4d805a296498b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=3.5, seed=378697477899808767; mps for text encoder and denoising; cpu for native VAE decode; bfloat16 for text encoders; float32 for transformer and CPU VAE; three local execution attempts: first aborted on an MPS dtype error; second returned an unsaved latent but the runner read the wrong output field; accepted first saved image after correcting that field with unchanged prompt, seed and FP32 sampling settings; lost latent content remains unknown, generated Nov 2, 2026, 7:52 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419
Wan 2.6 Text to Image image SHA 256: 7e9557478ee297369d7680b31ab3b23a13c984981c0e0c922ef07372999122a7
Kandinsky-5.0-T2I-Lite-SFT image SHA 256: a84280dd219692f7b20722a37491d6788d1ecdc594f816fa1203981642501ab2
The numbers at a glance
Wan 2.6 Text to Image
not in the arena
Kandinsky-5.0-T2I-Lite-SFT
not in the arena
Wan 2.6 Text to Image
Rank 44 of 108
Kandinsky-5.0-T2I-Lite-SFT
Rank 79 of 108
Wan 2.6 Text to Image
cheap
Kandinsky-5.0-T2I-Lite-SFT
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.
Wan 2.6 Text to Image
- Subscription
- n/a No plan in Germany names exactly this model.
- Via API
- $0.03 per image, 100 images $3.
Kandinsky-5.0-T2I-Lite-SFT
- 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 | Wan 2.6 Text to Image | Kandinsky-5.0-T2I-Lite-SFT |
|---|---|---|
| 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 | 63 | 34 |
| GraduallyBench by task | ||
| Typography and layoutLegible text, hierarchy, and composition | 41 | 21 |
| Product photographyMaterials, reflections, and fine details | 54 | 15 |
| Character and detailAnatomy, clothing, and spatial consistency | 79 | 65 |
| InfographicNumbers, labels, and visual organization | 77 | 33 |
| API price | ||
| Per imagelower is better | $0.03 | n/a |
| 100 images | $3 | n/a |
| Profile | ||
| Released | January 2026 | n/a |
| Weightsopen means: runs locally | proprietary | open |
| Access | API | local |
| Parameters | not published | 6 billion |
| Largest output | 2K | 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: Wan 2.6 Text to Image model source (verified Aug 15, 2026) · Kandinsky-5.0-T2I-Lite-SFT 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 Wan 2.6 Text to Image and Kandinsky-5.0-T2I-Lite-SFT from the same arena and category. Older model versions are not used as substitutes.
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