---
title: The Other AI Releases You Missed This Week
description: Three Chinese tech companies shipped major AI models in one week while Western attention focused on Opus 4.6 and Codex.
author: Darie Nani (Editor-in-Chief)
date: 2026-02-15T14:43:33.000Z
updated: 2026-02-26T17:55:08.156Z
canonical: https://www.sovereignmagazine.com/article/the-other-ai-releases-you-missed-this-week
image: https://cdn.nanimediahouse.com/china-ai-releases-2026.webp
categories: Artificial Intelligence
content_type: News
region: Global
publication: Sovereign Magazine
---

[Anthropic’s Opus 4.6 and OpenAI’s Codex](https://www.sovereignmagazine.com/article/anthropic-and-openai-release-competing-ai-coding-models-within-minutes-of-each-other) dominated AI coverage this week. In the same five-day window, Alibaba, ByteDance and Kuaishou each released major models covering robotics, video generation and multimodal output. All three are production systems, not research previews, and two are already publicly available.

## Alibaba open-sourced a robotics model that beats Google and Nvidia

On 10 February, Alibaba’s DAMO Academy released [RynnBrain](https://github.com/alibaba-damo-academy/RynnBrain), an open-source model that gives robots spatial awareness, episodic memory and multi-step task continuity. Most vision-language models process individual frames. RynnBrain tracks when and where events occurred, so a robot can resume an interrupted task or count objects it has already handled.

The flagship 30B-A3B variant uses a mixture-of-experts architecture that activates only 3 billion parameters at inference, keeping compute costs low while (according to Alibaba’s own RynnBrain-Bench evaluation suite) outperforming Google’s Gemini Robotics-ER 1.5 and Nvidia’s Cosmos-Reason2 across 16 benchmarks. Independent verification of those claims has not yet been published. All seven model variants are available on GitHub and Hugging Face under open-source licences. Google and Nvidia charge for comparable capabilities through their cloud platforms.

## ByteDance’s video model generated a voice from a photograph

ByteDance released [Seedance 2.0](https://seed.bytedance.com/en/seedance2_0) around 12 February, a text-to-video model that generates realistic footage from written prompts. The model shipped with a feature called Face-to-Voice that ByteDance had to suspend before it even launched properly.

On 10 February, Chinese tech reviewer Tim Pan demonstrated that Face-to-Voice could reconstruct his specific voice and speaking style from a single photograph. No audio sample, no consent, no text prompts describing how he speaks. It inferred vocal characteristics from his face alone. Pan described the experience as ‘terror-inducing’. ByteDance disabled the feature and added a live verification step, but has not disclosed what training data enabled the capability or whether it will retrain the model to remove it.

Separately, a viral video showing a fabricated fight between Tom Cruise and Brad Pitt drew a formal response from the Motion Picture Association, which called it ‘massive infringement’. ByteDance faces simultaneous pressure from privacy advocates over voice cloning and from Hollywood over likeness rights.

## Kuaishou upgraded Kling and its share price followed

Kuaishou’s [Kling 3.0](https://ir.kuaishou.com/news-releases/news-release-details/kling-ai-launches-30-model-ushering-era-where-everyone-can-be) extended video output to 15 seconds with improved consistency and added native audio generation across multiple languages, dialects and accents. Kuaishou’s share price has risen more than 50 per cent over the past year, with Kling cited by analysts as a primary driver. The multilingual audio opens export potential in Southeast Asian markets where Kuaishou’s short-video platform already operates.

## China’s AI development pace is not slowing down

Three production models from three companies in five days. Alibaba gave its away for free, undercutting Google and Nvidia’s paid offerings. ByteDance shipped so fast it had to pull a feature on day one. Kuaishou turned a model upgrade into a stock catalyst. DeepSeek set this pace in late 2025 when its open-source reasoning model compressed margins across the LLM market, and the rest of the [Chinese tech sector has been racing to keep up](https://www.sovereignmagazine.com/article/china-s-ai-rise-innovation-overcomes-chipmaking-and-investment-gaps) since.

## Further Context

**Q: Who is winning the AI race between the US and China?**
By most capability benchmarks, the United States leads. American labs operate the largest training clusters and produce the highest-scoring frontier models. China leads on AI patent filings, accounting for over 70 per cent of global AI-related applications, and on open-source model downloads: Alibaba’s Qwen family overtook Meta’s Llama as the most downloaded model series on Hugging Face in 2025. The gap is narrowing fastest in inference efficiency, where Chinese labs have shown they can match Western model performance at a fraction of the compute cost.

**Q: What is the difference between open-source and proprietary AI models?**
Open-source AI models publish their weights, training code and often their datasets, allowing anyone to download, modify and deploy them without paying the developer. Proprietary models (such as GPT-4o, Claude and Gemini) are accessed through paid APIs and do not disclose their internal workings. Open-source models give organisations full control over deployment and data privacy but require in-house expertise to run and maintain. Proprietary models are easier to deploy but create dependency on a single vendor’s pricing, uptime and terms of service.

**Q: Who is winning the AI patent race?**
China has filed more AI-related patents than any other country, accounting for over 70 per cent of global applications. The United States leads in patent quality and citation impact, particularly in foundational model architectures. South Korea and Japan file significant volumes in applied AI for manufacturing and robotics. Patent counts alone do not capture commercial value: many Chinese AI patents cover incremental applications rather than breakthrough research, and a large share are filed domestically with no international equivalent.