Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation

By Team Kandinsky, Agafonova, Akhmatov +85Oct 4, 2026

Score██████░░░░5.7

Key numbers

1920 x 1080

VerdictCompetent work. Briefs at most.

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Abstract

We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video Lite (3B parameters) and Kandinsky 6.0 Video Pro (29B parameters). Both models generate 5-second video clips with synchronized 44 kHz audio, including lip-sync, in text-to-audio-video (T2AV) and image-to-audio-video (I2AV) modes; a built-in super-resolution model raises the output resolution to Full-HD (1920×1080). Building on the video generation capabilities of Kandinsky 5.0, Kandinsky 6.0 Video employs a dual-stream CrossDiT architecture that connects a pretrained video stream and a newly trained audio stream through bidirectional cross-attention for temporal and semantic alignment. Our continuous pretraining strategy first trains the audio stream from scratch on large-scale audio corpora and then trains both streams jointly on paired audio-video data while preserving unimodal fidelity; pretraining is followed by supervised fine-tuning, reinforcement-learning-based post-training, and distillation. In side-by-side human evaluation, Kandinsky 6.0 Video Pro clearly outperforms its predecessor, Kandinsky 5.0 Video Pro, and remains competitive with leading audio-video generation models, particularly in speech quality. To accelerate open research and deployment in multimedia generation, we release the code, model checkpoints, and diffusers integration under the MIT license.

Team Kandinsky, Julia Agafonova, Bulat Akhmatov, Mikhail Aksyutin, Grigorii Alekseenko, Anastasia Aliaskina, Olga Androsova, Vladimir Arkhipkin, Anna Averchenkova, Alexander Belykh, Serafima Bocharova, Sofiya Bogakovskaya, Anton Bukashkin, Mark Bulygin, Kirill Buzygin, Irina Cheremnykh, Kirill Chernyshev, Mikhail Chernyshov, Vladimir Chernyy, David Chikovani, Georgy Daniltsev, Denis Dimitrov, Anna Dmitrienko, Vladimir Dokholyan, Sergey Emelyanov, Dmitry Ermilov, Georgii Fedorov, Polina Gavrilova, Nikolai Gerasimenko, Aleksandr Gordeev, et al.

The editor's rubric

Heuristic review

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	███░░ 3	18%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░ 3	14%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	██░░░ 2	18%	Some room to improve with obvious engineering.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); gains (outperforms); verification (human evaluation, code released); scale (scalable).

How the score was computed

rank-2026-09-29

Score██████░░░░5.7

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Merit: 5.1 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.4 / 10; Shrunk toward the desk prior by editor confidence (38%).
Attention: 89%; Citations, upvotes, points, mentions.
Freshness: 64%; Half-life decay since publication.

Hugging Face upvotes103 (reference 25, via hf-daily, Oct 6, 2026, 13:49 UTC)
GitHub stars1 (reference 250, via hf-daily, Oct 6, 2026, 13:49 UTC)

The record

Reviewed by heuristic-v2 on Oct 6, 2026, 13:49 UTC. Paper type: method.
Categories: cs.CV, cs.AI, cs.LG, cs.MM
LEAD, No.1 in the Artificial Intelligence edition of October 6, 2026.