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Kandinsky 6.0 Video Lite
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Kandinsky 6.0 Video Lite is an open-weights video model from Sber, released October 2026. Its published size is 3B parameters. Licence: mit.
Catalogue identity notes
Kandinsky 6.0 Video (Sber / Kandinsky Lab), open-sourced 2026-10-06 under MIT (GitHub kandinskylab/kandinsky-6 README news, @8195e72). The arXiv report (2610.05608v1) evaluates 'Kandinsky 6.0 Video Pro and Kandinsky 6.0 Video Lite after RL post-training'; the family table on the HF cards lists separate Pro / Pro-distill (10 steps) / Pro-pretrain and Lite / Lite-distill / Lite-pretrain repos, and the non-distilled, non-pretrain repo is described as 'the main ... checkpoint, the strongest quality setting' of its line, so it is mapped to the report's final (RL post-trained) model. Distilled and pretrain checkpoints are not catalogued (no scored rows). Lite repo kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers pinned 5686616 ('This repository contains Kandinsky 6.0 Lite: the 3B checkpoint of the Lite line').
Model information sources
Release date ↗ · Weights licence ↗ · Parameter count ↗
3B in total as stated in the report (video stream 2B, audio stream 0.6B, cross-attention 0.4B); text encoders, VAEs and vocoder are separate components.
$ how it compares
The video models ranked either side of Kandinsky 6.0 Video Lite, out of 139 with a score, then more from Sber.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 62 |  Kling 1.6 (Pro mode) | 52±4 | comparison only | ||
| 63 |  LongCat-Video | 51±2 | api $0.040/sec +local | ||
| 64 | Kandinsky 6.0 Video Lite | 51±7 | local | ||
| 65 |  Seaweed-7B | 50±6 | comparison only | ||
| 66 |  Luma Dream Machine 1.6 | 50±7 | comparison only | ||
| more from Sber | |||||
| 57 | Kandinsky 5.0 Pro | 52±2 | api $0.040/sec +local | ||
| 77 | Kandinsky 6.0 Video Pro | 48±7 | local | ||
| 82 | Kandinsky 5.0 T2V Lite | 47±2 | local | ||
$ what it's good at
text to video█····█·····█ #1 of 3 * · 76% of its score · 1 test
Beats LTX-2.5 here, though they rank higher overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| VABench T2AV micro Visual QA lab | 69.1% ✎ | ▄█··▄·▄···▄█ beats 3/7 | 76% |
$ where to run it
Other ways to run it
- Run it as published · needs ~33.8 GB, NVIDIA only · from huggingface.co/kandinskylab
uv run --with git+https://github.com/huggingface/diffusers --with torch --with transformers --with accelerate --with av python -c "import torch; from diffusers import Kandinsky6TI2VAPipeline; from diffusers.utils import encode_video; p=Kandinsky6TI2VAPipeline.from_pretrained('kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers', torch_dtype=torch.bfloat16); p.enable_model_cpu_offload(); o=p(prompt='A red dragon wakes on a mound of gold coins in a sunlit cave and slowly unfolds its wings. Audio: deep rumbling breath, clinking coins.', height=480, width=864, num_frames=121, num_inference_steps=50, guidance_scale=5.0); encode_video(o.frames[0], fps=24, output_path='output.mp4', audio=o.audio[0][None], audio_sample_rate=p.audio_sample_rate)"
Weights and documentation: huggingface.co/kandinskylab ↗
$ how the score was worked out · Kandinsky 6.0 Video: Foundati… 76%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation (arXiv:2610.05608v1) · lab-reported | 1 | 76% |
| prior · pull toward the middle | 24% | |
| weighted average | = 51.1 |
$ improve this page
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data updated · sources and licences · json