Meituan open-sources LongCat-2.0, a 1.6T-parameter model that it says was trained on a 50K-chip cluster of domestic Chinese processors, without giving details
China's food delivery giant Meituan (3690.HK) said on Tuesday it had released and would open-source its next-generation LongCat …
Reuters
Context & Ripple Effects
Meituan’s AI push predates this release: it moved to acquire AI startup Light Year in 2023. The model release also comes while Meituan is managing losses and an intense food-delivery rivalry with Alibaba and JD.com, making AI capability a strategically visible investment rather than a standalone research exercise.
The release fits a broader Chinese pattern of making large models broadly available: Alibaba had committed to an open commercial-use version of Tongyi Qianwen, while Tencent and MiniMax are also pursuing open-model releases at substantial scale.
First-order effects
Developers and companies can now access LongCat-2.0 under Meituan’s open-source release, giving Meituan a public position in China’s large-model ecosystem.
Meituan has publicly tied the training run to a 50,000-chip domestic-processor cluster, but the lack of hardware and training details limits what outsiders can verify about the underlying stack.
Second-order effects
Tencent, MiniMax, Alibaba and other Chinese model builders face added pressure to compete not only on model capability but also on openness, licensing and the credibility of their training-infrastructure claims.
The stated use of domestic processors gives Chinese AI-hardware suppliers a high-profile validation opportunity, although absent chip and performance details it does not establish which suppliers or systems benefited.
Third-order effects
If similarly scaled releases continue, open-weight models could become a more central route for Chinese internet platforms to build developer ecosystems and diffuse AI capabilities beyond proprietary APIs.
The key structural question is whether domestic compute can support repeatable frontier-scale training; disclosures such as this are a signal of that ambition, not yet sufficient evidence of a durable hardware-equivalence shift.
The trend: China’s major internet and AI companies are increasingly pairing very large open-model releases with claims of domestically built compute capacity.
https://longcat.chat/... People are missing out on how big a deal Longcat 2.0 by Meituan (aka “Chinese Doordash") is. Near frontier performance, trained on 50k Chinese domestic accelerators! The first ever to achieve this! [image]
Meituan, basically China's DoorDash, trained a 1.6T parameter LLM on 50K Chinese chips. It reminds me of Jensen Huang's point on the Dwarkesh podcast: export controls on Nvidia GPUs won't stop China. They'll just accelerate the development of AI that runs on Chinese chips. [image…
A 1.6T parameter MoE model with 1M context is a massive technical achievement. Seeing LongCat-2.0 outperform GPT-5.5 on SWE-bench Pro shows that the focus on agentic coding from the ground up is paying off. The sparse attention architecture is the real deal for long-context
Introducing LongCat-2.0 🐱 1.6T parameters · MoE with ~48B active · 1M context The full model behind Owl Alpha on @OpenRouter — now available. Built for agentic coding from the ground up: ◆ LongCat Sparse Attention (LSA) — scales efficiently for 1M-context tokens ◆ [image]
the new sparse attention method introduced with this model is basically a combination of components from existing ones. let's go over each sparse attention method and what they keep from them: - deepseek sparse attention (DSA): they keep the top-k indexer, this is the basis of [i…
@Yuchenj_UW Checked with friends at Meituan, and it's confirmed that the chips they are currently using are all Chinese chips, and the performance is quite impressive.
💻 Coding: Reads your entire codebase at once — understands the architecture, tracks cross-file dependencies, and delivers changes that compile clean. [video]
I'm hearing that “Owl Alpha”, one of OpenRouter's fastest-growing agent models, is actually Meituan LongCat-2.0-Preview The reported design is a huge 1.6T-parameter MoE, active 48B. A dynamic active range of roughly 33B to 56B, and natively supports a 1M-token context window. [im…
Most popular model on @OpenRouter (10tr tokens) turns out to be a 1.6tr MoE by @Meituan_LongCat (superapp/DoorDash of China) Basically Gemini / Opus 4.6 level 35tr tokens trained entirely on 50k Chinese ASICs No GPUs needed https://longcat.chat/... [image]
tried out Long-cat. Clearly, it's not the best model out there, but what it represents is huge. The market has grossly underestimated quite a few things
Meituan, China's largest food delivery platform, open-sourced a 1.6 trillion parameter AI model today. LongCat-2.0 was trained from scratch on a 50,000-card domestic GPU cluster. Native 1M context. 30T+ tokens of pretraining data. A delivery company. Trillion parameters. [image]