Chinese DoorDash Is Making Better LLMs Than Meta

Quick Overview

Meituan researchers are significantly advancing AI by releasing numerous open-source models and technical papers, demonstrating strong performance across various benchmarks, particularly in agentic capabilities, which surpasses the output of closed models like those from Meta.

Key Points: Meituan researchers released LongCat-Flash, a 560-billion parameter Mixture-of-Experts (MoE) model, trained on over 20 trillion tokens in just 30 days for $0.70 per million tokens. The LongCat team has published over 700 technical blogs and papers, including LongCat-Flash, LongCat-Audio-Codec, LongCat-Video, and LongCat-Flash-Omni, demonstrating consistent research output since 2019. LongCat-Flash achieves high throughput and low inference latency, exceeding 100 tokens per second (TPS) on H800 hardware during inference. The LongCat-Flash-Omni model is state-of-the-art for open-source multi-modal tasks, outperforming models like Qwen3-Omni, GPT-4o, and Gemini 2.5 Flash on several OmniBench, Audio, Video, and Image benchmarks. Meituan's Meituan (DoorDash equivalent in China) is projected to have $55.3 billion in estimated 2025 revenue, nearly four times the revenue of DoorDash. The LongCat models leverage techniques like Context-Aware Dynamical Computation Mechanism and MoE++ architecture to maintain high performance while reducing compute cost. The video highlights the comprehensive nature of Meituan's AI research, covering everything from fundamental LLM concepts to advanced multi-modal architectures.

Context: This video examines the significant and rapidly growing contributions of Meituan's AI research team, known as LongCat, to the field of large language models (LLMs) and multi-modal AI. The video contrasts Meituan's open-source output with closed models from major tech companies like Meta, suggesting that Meituan's research is setting new standards in efficiency and capability, especially in agentic tasks, challenging the dominance of US-based tech giants.

Detailed Analysis