Meituan LongCat-2.5 Official Release: A Deep Dive into the 1.6T Multimodal Agent Model
Introduction
On November 2, 2026, Chinese tech giant Meituan officially released LongCat-2.5, the full commercial version following a preview phase that began on September 25, 2026. LongCat-2.5 is a natively multimodal model engineered specifically for long-horizon agentic tasks. It features 1.6 trillion total parameters with approximately 48 billion active parameters per inference step, utilizing a Mixture-of-Experts (MoE) architecture. Crucially, the entire training process relied exclusively on domestically produced Chinese AI accelerators — a landmark achievement for China's AI self-sufficiency strategy.
The release of LongCat-2.5 comes at a pivotal moment in the global AI agent landscape. As the industry shifts from experimental pilots to standardized infrastructure, Meituan's model represents a significant leap forward in what Chinese AI companies can achieve with domestic hardware and innovative training methodologies. For the Asia-Pacific region, this development carries profound implications for the competitive dynamics of AI agent deployment.
Technical Specifications
Architecture Overview
LongCat-2.5 employs a MoE architecture with 1.6 trillion total parameters, activating only ~48 billion per inference step. This design balances massive model capacity with computational efficiency, enabling cost-effective deployment at scale. The MoE approach allows the model to route different types of inputs to specialized sub-networks, achieving both breadth and depth of capability without the computational overhead of activating all parameters for every query.
| Specification | Value |
|---|---|
| Total Parameters | 1.6 Trillion |
| Active Parameters per Token | ~48 Billion |
| Context Window | 1 Million Tokens |
| Maximum Output Length | 128,000 Tokens |
| Quantization | W4A4 (4-bit weights & activations) |
| Architecture | Mixture-of-Experts (MoE) |
Native Multimodality
Unlike earlier versions that used "bolted-on" visual components, LongCat-2.5 processes visual signals as multi-level discrete tokens using Residual Vector Quantization (RVQ) over native-resolution SAE features, sharing a unified vocabulary with text tokens. This native integration eliminates the limitations of traditional "stitched-together" vision modules, enabling seamless cross-modal reasoning within a single context.
The practical implication of this design is significant: the model can reason about images, code, text, and structured data within the same context window without the information loss that typically occurs when visual and textual representations are processed by separate encoders and then merged. This unified approach enables more coherent multi-step reasoning across modalities.
1-Million-Token Context Window
Supporting up to 1 million tokens of context with a 128,000-token maximum output, LongCat-2.5 can handle ultra-long documents, massive codebases, and complex multi-step agentic workflows that span dozens of tool calls — capabilities that far exceed most competing models. To put this in perspective, 1 million tokens is roughly equivalent to 750,000 words of text, or the entire content of several large software repositories.
This extended context window is particularly valuable for agentic tasks that require maintaining coherent state across many steps. An agent navigating a complex software project, for instance, can keep the entire codebase in context while making targeted modifications — eliminating the need for complex retrieval mechanisms that can introduce errors and inconsistencies.
W4A4 Quantization
The model employs W4A4 quantization (4-bit weights and activations), delivering significant gains in both prefill and decode throughput while maintaining high accuracy. This enables efficient deployment on a wider range of hardware configurations. The W4A4 approach is particularly aggressive — most production models use 8-bit or 16-bit precision — but Meituan's implementation reportedly maintains accuracy within acceptable bounds for the model's target use cases.
Agentic Capabilities and Training Methodology
Long-Horizon Task Design
LongCat-2.5 is designed to function as a true autonomous agent rather than a general-purpose chat model. It can autonomously operate across diverse real-world environments:
- Terminal environments: Execute complex command-line tasks, manage file systems, run build pipelines, and interact with system services
- Browser control: Navigate websites, fill forms, extract structured information, and interact with web applications
- Design tools: Operate graphic design software to generate and modify visual content, create presentations, and produce multimedia assets
- Complex codebases: Understand and modify large-scale software projects across multiple files, languages, and frameworks
The model's agentic design philosophy prioritizes reliability and error recovery. Rather than optimizing purely for benchmark performance, Meituan's team focused on building a model that can gracefully handle unexpected situations, recover from errors, and maintain coherent task state across long execution sequences.
Reinforcement Learning Training Strategy
Meituan employed an innovative reinforcement learning approach focused on long-horizon sequence optimization. The training incorporates "fail-to-pass" and "pass-to-pass" verification mechanisms, mining real-world agent failures as synthetic training data. This allows the model to learn from mistakes and develop robust self-correction capabilities.
The "fail-to-pass" mechanism is particularly innovative: by identifying cases where the model initially fails a task and then succeeds after correction, the training process explicitly teaches the model how to recognize and recover from its own errors. This is fundamentally different from traditional supervised learning approaches that only show the model successful trajectories.
High-Intensity Training Grounds
Meituan built specialized "high-intensity training grounds" integrating diverse environments and tool-use scenarios, ensuring strong generalization to novel real-world tasks. This infrastructure was critical for developing an agent capable of handling the unpredictability of real-world deployments.
The training environments included simulated versions of real-world software development workflows, customer service scenarios, data analysis pipelines, and content creation tasks. By training across this diverse range of scenarios, the model developed the flexibility to adapt its approach to novel situations rather than relying on memorized patterns.
Benchmark Performance
LongCat-2.5 achieved top-tier scores across multiple agentic benchmarks upon its November 2026 launch:
| Benchmark | LongCat-2.5 Score | Context |
|---|---|---|
| BrowseComp | 91.9 | Web browsing and information extraction |
| Terminal Bench v2.1 | 88.4 | Command-line task execution |
| PaperBench | 82.1 | Scientific paper analysis and synthesis |
Meituan claims performance parity with Claude Opus variants, though independent third-party verification remains limited, as benchmarks were absent during the September preview phase. The research community has noted that the model's performance on BrowseComp — a particularly challenging benchmark that requires navigating complex, multi-step web research tasks — is especially impressive, suggesting strong real-world utility for information-gathering workflows.
Domestic Chip Training: Strategic Significance
The Geopolitical Context
One of LongCat-2.5's most notable achievements is its exclusive reliance on domestically produced Chinese AI accelerators throughout training — with no US-origin silicon involved. This carries profound strategic implications that extend well beyond the technical achievement itself.
The United States has progressively tightened export controls on advanced AI chips to China, with restrictions on Nvidia's H100, A100, and subsequent generations of high-performance GPUs. These restrictions were intended to slow China's AI development by limiting access to the most powerful training hardware. LongCat-2.5's successful training on domestic chips represents a direct challenge to this strategy.
Technological Autonomy: Meituan has demonstrated the viability of training frontier AI models on domestic hardware, providing a critical proof-of-concept for China's AI independence strategy. This achievement will likely accelerate investment in domestic AI chip development and encourage other Chinese AI companies to explore similar approaches.
Cost Control: Using domestic chips reduces training costs and supply chain dependencies, improving resilience against geopolitical disruptions. As domestic chip production scales, the cost advantages are expected to improve further.
Industry Demonstration: LongCat-2.5's success will encourage more Chinese AI companies to explore domestic hardware solutions, accelerating the development of China's domestic AI chip ecosystem. This creates a virtuous cycle: more demand for domestic chips drives more investment in chip development, which improves chip performance, which enables more capable AI models.
Availability and Ecosystem Integration
LongCat-2.5 is currently accessible through multiple channels:
- API access: Developers can integrate the model into their applications via a well-documented REST API
- Web interface: Direct access through longcat.ai for interactive use and experimentation
- Agent framework integration: Compatible with Claude Code, Codex, and OpenCode agent harnesses, enabling developers to use LongCat-2.5 as the backbone of their existing agent workflows
The model's compatibility with established agent frameworks is particularly significant for enterprise adoption. Organizations that have already invested in building agent infrastructure around frameworks like Claude Code can potentially switch to LongCat-2.5 as the underlying model without rebuilding their entire workflow.
Asia-Pacific Perspective and Market Implications
Meituan's Unique Data Advantage
The release of LongCat-2.5 carries significant implications for the Asia-Pacific AI ecosystem. As one of China's largest local life services platforms, Meituan has accumulated rich real-world data across food delivery, hotel booking, travel, and other domains. This data provides a unique advantage for training agent models with strong real-world understanding capabilities.
Unlike AI labs that train primarily on internet text, Meituan's models benefit from exposure to real-world task completion data — the actual sequences of actions that humans take to accomplish goals in digital environments. This grounding in real-world task data may explain the model's strong performance on agentic benchmarks.
Enterprise Applications in Asia-Pacific
For enterprise users across Asia-Pacific, LongCat-2.5 offers a powerful localized AI agent solution, particularly well-suited for:
- E-commerce automation: Managing product listings, responding to customer inquiries, processing orders
- Customer service: Handling complex multi-turn conversations in Chinese and other Asian languages
- Business process automation: Automating repetitive knowledge work across finance, HR, and operations
- Content creation: Generating localized content for Asian markets with cultural nuance
Competitive Landscape Analysis
LongCat-2.5's release intensifies competition in the global AI agent model market:
- vs. Claude Opus: Meituan claims comparable performance, with potential advantages in Chinese-language tasks and Asian business scenarios. The domestic chip training also gives LongCat-2.5 a cost advantage in the Chinese market.
- vs. GPT-6 series: Differentiated focus on long-horizon agentic tasks rather than general conversational capabilities. LongCat-2.5's 1M token context window exceeds what most GPT-6 variants offer.
- vs. other Chinese models: LongCat-2.5's native multimodality and domestic chip training distinguish it from competitors like DeepSeek and Kimi, which have focused more on reasoning capabilities.
Future Outlook
The release of LongCat-2.5 is likely just the beginning of Meituan's AI agent ambitions. The company's extensive real-world deployment experience, combined with its growing AI research capabilities, positions it well to continue advancing the state of the art in agentic AI.
Looking ahead, several developments are worth watching:
- Expanded benchmark coverage: Independent third-party verification of LongCat-2.5's performance claims will be crucial for establishing its position in the competitive landscape
- Enterprise adoption: How quickly large enterprises in China and across Asia-Pacific adopt LongCat-2.5 for production use cases will be a key indicator of its real-world utility
- Domestic chip ecosystem: The success of LongCat-2.5 will likely catalyze further investment in Chinese AI chip development, potentially accelerating the improvement of domestic hardware capabilities
Conclusion
The official release of Meituan LongCat-2.5 marks a new milestone for Chinese AI agent technology. Its 1.6T MoE architecture, 1-million-token context window, native multimodal design, and exclusive reliance on domestic chips for training make it one of the most technically significant AI agent models of 2026. As deployment scales across Asia-Pacific enterprises, we can expect AI agents to play an increasingly important role in complex real-world tasks, driving the digital transformation of the region and demonstrating that frontier AI development is no longer the exclusive domain of US-based technology companies.
