Kandinsky 3.1
Kandinsky 3.1 is an advanced text-to-image AI model developed by Sber AI, Russia's largest technology company, named after the pioneering abstract artist Wassily Kandinsky.
Key Highlights
Multilingual Prompt Support
Appeals to a wide user base with multilingual prompt support, primarily in Russian and English.
Fast Inference Performance
Provides efficiency with faster image generation compared to competitors thanks to optimized architecture.
Sber Ecosystem Integration
Offers easy integration and scalability for enterprise projects via API access on the Sber AI platform.
Image-to-Image Transformation
Capability to transform existing images with text prompts for inpainting and style transfer operations.
About
Kandinsky 3.1 is an advanced text-to-image AI model developed by Sber, Russia's largest technology company. Named after Russian painter Wassily Kandinsky, this model offers superior capabilities particularly in Cyrillic alphabet and Russian text rendering. Released as the successor to Kandinsky 3.0, the 3.1 version provides significant improvements in overall image quality, prompt adherence, and inference speed. As a product of Sber AI laboratory's ongoing research efforts, the model is Russia's flagship project in multilingual AI image generation.
In terms of technical architecture, Kandinsky 3.1 preserves the previous version's latent diffusion approach while incorporating important architectural updates. The U-Net diffusion model has been made larger and more efficient, with improved attention mechanisms. The multilingual text encoder has been strengthened, and prompt understanding capacity in multiple languages including Russian has been increased. Model size and training dataset have been expanded compared to 3.0. Inference optimizations enable faster image generation on the same hardware. It can produce output at 1024x1024 pixels and above and supports multiple aspect ratios.
In terms of quality, Kandinsky 3.1 shows improvement over the previous version across all areas. Photorealism and digital art quality have been enhanced, with improved color accuracy and texture detail. The superiority in Cyrillic alphabet text rendering is maintained and strengthened — no other model on the market can deliver this level of performance in Russian typography tasks. Accuracy in human anatomy and facial expressions has been improved. Prompt adherence in complex compositions has been strengthened, enabling more accurate rendering of multi-element scenes. The overall quality gap compared to global models is narrowing with each iteration.
Kandinsky 3.1 is preferred by Russian-speaking creative professionals, marketing teams working for the Russian market, designers running Cyrillic typography projects, educational institutions, and internal users within the Sber ecosystem. It is valuable in scenarios such as Russian advertising campaigns, product visuals containing Cyrillic text, Russian-language educational materials, regional social media content, and corporate presentation materials. It is also used internally across Sber's banking, retail, and media operations for brand-consistent visual content generation.
Kandinsky 3.1 is open-source and downloadable from Hugging Face. API access is also available through the Sber AI platform. It is compatible with the Diffusers library and can be run locally. Hardware requirements are reasonable, with 8-12GB VRAM sufficient for operation. Commercial use is permitted, and the license terms provide flexibility for developers building applications on top of the model.
In the competitive landscape, Kandinsky 3.1 strengthens its unique position in Russian language support and Cyrillic text rendering. The narrowing quality gap compared to 3.0 is positioning the model more competitively in the global market. While there remains some difference in overall quality compared to open-source leaders like SDXL and FLUX.1, it is without alternative in Russian-language use cases. Sber's continued investment signals that future versions of the model will converge further with global quality standards. Its academic significance as a pioneering research project in multilingual AI image generation also continues, influencing the broader research agenda around non-English generative models.
Use Cases
Russian Content Generation
Creating content for the Russian market by generating high-quality images with Russian prompts.
Rapid Prototyping
Instantly visualizing and iterating design concepts with fast inference performance.
API-Based Application Development
Developing web and mobile applications that integrate image generation via Sber AI API.
Image Editing and Inpainting
Creative editing by modifying specific areas of existing images with text guidance.
Pros & Cons
Pros
- Open-source text-to-image model developed by Sber AI
- Improved text understanding and multilingual prompt support
- Natively supports inpainting and outpainting features
- Strong performance with Russian and Cyrillic-based prompts
Cons
- Not as successful as SDXL and FLUX with Western language prompts
- Community and ecosystem support limited compared to competitors
- Documentation mostly in Russian — English resources insufficient
- Behind leading models in photorealism quality
Technical Details
Parameters
12B
Architecture
Diffusion
Training Data
Proprietary multilingual dataset
License
Apache 2.0
Features
- Bilingual (RU/EN)
- High quality
- Fast inference
- API access
- Inpainting support
- Image-to-image
Benchmark Results
| Metric | Value | Compared To | Source |
|---|---|---|---|
| FID (COCO 30K, zero-shot) | 10.2 | SDXL: 9.5 | Kandinsky 3.1 Technical Report |
| CLIP Score | 0.318 | SDXL: 0.322 | Hugging Face Model Card |
| Parametre Sayısı | 11.9B (UNet: 3.0B) | SDXL: 6.6B | Sber AI Official |
Available Platforms
Frequently Asked Questions
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Quick Info
Diffusion