Overview
LongCat-2.0 is a new-generation trillion-parameter MoE foundational large model **officially open-sourced** by Meituan on 2026/6/30 — **the world's first trillion-level large model to complete the entire pre-training and inference R&D process entirely relying on a domestic 50,000-card computing cluster**, requiring no overseas chips from start to finish. This is regarded by the industry as a milestone event where the domestic computing ecosystem truly runs through a full-stack closed loop.
The model has **1.6 trillion total parameters** (MoE architecture, with an average activation of about 48B, activation range 33B~56B), employing a **zero-computation expert** mechanism for token-level dynamic activation — simple tokens consume no computing power, while complex tokens dispatch more experts to participate. It natively supports **million-character ultra-long context**, with a focus on optimizing **Coding Agent** capabilities — this path aligns with DeepSeek V4's Harness / Code Agent strategy, signaling that domestic large models have entered the 'Agent-first' phase.
**Key Milestones**:
- **2026/4/24**: LongCat-2.0-Preview first released for open testing (released on the same day as DeepSeek V4, known in the industry as the 'twin stars' event for domestic large models)
- **2026/6/30**: Official release, with weights, inference code, and fine-tuning tutorials fully released
- **2026/7/5**: Fully open-sourced under the **MIT commercial-friendly open-source license**, becoming one of the first domestic trillion-level MoE large models to adopt the MIT license (more permissive than Apache 2.0, with fewer commercial restrictions)
- **2026/7/12**: Simultaneously released **domestic chip inference code** (inference adaptation code for Huawei Ascend, T-Head, Cambricon, and other platforms all made public), extending the 'domestic computing full-stack closed loop' from Meituan's internal business to the entire community for reuse
In terms of positioning, LongCat deeply serves Meituan's local life services business (high-concurrency scenarios like in-store dining, food delivery, and rider dispatch), trained on Meituan's massive local life data, excelling in scenarios such as **order parsing, user demand prediction, and local information generation**. It is also available for public trial via the longcat.ai official website and has been launched on the OpenRouter global calling platform.
Key Features
- 1.6 Trillion MoE Architecture: Total parameters of 1.6T, placing it among the world's top-tier large models (on par with DeepSeek V4-Pro)
- World's First Domestic 50,000-Card Training: The first trillion-level model to complete the entire pre-training and inference process entirely relying on a domestic 50,000-card computing cluster, requiring no overseas chips
- Million-Character Ultra-Long Context: Native 1M Token context, capable of processing million-character industry documents at once
- Coding Agent Focused Optimization: Coding Agent is the core upgrade direction of the 6/30 version, aligning with the domestic Harness strategy
- Full Open Source · Free for Commercial Use: Weights, inference code, and fine-tuning tutorials fully released with no usage barriers
- Native Local Life Scenarios: Trained on Meituan's real business data: excels in order parsing, demand prediction, and local information generation
- Deep Optimization for Chinese Scenarios: Deeply fine-tuned for Chinese language data and local scenarios, outperforming overseas models of similar scale on Chinese tasks
Use Cases
- Long document / long conversation analysis (million-character context)
- Coding agent / programming Agent backend selection
- AI integration for local life applications (both within and outside Meituan ecosystem)
- Enterprises with compliance requirements for domestic computing deployment (state-owned enterprises / Xinchuang scenarios)
- Small and medium-sized enterprises and developers seeking free commercial use of trillion-parameter models
- Teams needing top-tier foundational model capabilities in Chinese scenarios
Pros
- 1.6T parameters + 1M context, same specifications as DeepSeek V4-Pro
- World's first domestic 50,000-card trained trillion-level model, benchmark for full-stack domestic production
- Full open source + free commercial use, no licensing costs
- Focus on optimizing Coding Agent, hitting the hottest track of 2026
- Backed by Meituan's real local life scenarios, with thorough engineering validation
- Forms a domestic open-source trio with DeepSeek V4 / Qwen
Pricing
**Completely Free**: From 6/30, model weights, inference code, and fine-tuning tutorials are all open-sourced, allowing free commercial use. The longcat.ai official website still provides free online experience. Enterprise-level private deployment is supported on demand by Meituan's technical team.
Summary
LongCat-2.0 is a landmark event for the full-stack domestic production of domestic large models in 2026 — the combination of **1.6T parameters + 50,000-card domestic cluster + full open source + free commercial use + Coding Agent optimization** instantly places it in the top tier of open-source models. If you need a pure domestic stack for enterprise AI or are looking for the most cost-effective open-source base for Coding Agent, LongCat-2.0 is a must-test; for daily conversations and general tasks, it can still be used complementarily with Doubao / Kimi / DeepSeek.
Version History
- Domestic chip inference code released (2026-07-12): Inference adaptation code for domestic chip platforms such as Huawei Ascend, T-Head, and Cambricon all made public, extending the 'domestic computing full-stack closed loop' from Meituan internally to the entire community for reuse
- Full open source under MIT license (2026-07-05): Fully open-sourced under the MIT commercial-friendly open-source license, becoming one of the first domestic trillion-level MoE large models to adopt the MIT license (more permissive than Apache 2.0)
- LongCat-2.0 official release (2026-06-30): Weights, inference code, and fine-tuning tutorials fully released; world's first trillion-level model to complete full R&D entirely relying on a domestic 50,000-card cluster; zero-computation expert mechanism for token-level dynamic activation
- LongCat-2.0-Preview (2026-04-24): First released for open testing; trillion-level MoE + 1M context; released on the same day as DeepSeek V4, known as the 'twin stars' event for domestic large models