*Data last updated: 2026-04-13 09:14 (UTC+8)
As of 2026-04-13 09:14, MEITUAN 03690.HK (MEITUAN) is priced at $0, with a total market cap of --, a P/E ratio of 0,00, and a dividend yield of %0,00. Today, the stock price fluctuated between $0 and $0. The current price is %0,00 above the day's low and %0,00 below the day's high, with a trading volume of --. Over the past 52 weeks, MEITUAN has traded between $0 to $0, and the current price is %0,00 away from the 52-week high.
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The Gate contract stock section will launch the first trading of 6 perpetual contracts including TENCENT (Tencent Holdings 0700.HK) and MEITUAN (Meituan 03690.HK)
It is reported that Gate's traditional asset category layout is comprehensive, covering the widest range on the entire network.
2026-04-13
Initial Listing: Gate Futures to Launch Stocks with 6 USDT-M Perpetual Contracts, Including TENCENT and XIAOMI
Gate's Futures Stocks will officially launch live trading of TENCENT (TENCENT 00700.HK), MEITUAN (MEITUAN 03690.HK), XIAOMI (XIAOMI 01810.HK), KUAISHOU (KUAISHOU 01024.HK), XUNCE (XUNCE 03317.HK) and ZHIPU (ZhiPu 02513.HK) perpetual contract (USDT-M) at 06:00 (UTC) on April 13, 2026, supporting long and short positions with leverage from 1x to 20x. Leverage can be selected at the time of order placement.
2026-04-13
Will Hong Kong skip QR code payments and go directly to stablecoin payments?
This article analyzes the reasons why Hong Kong missed the opportunity for QR code payment and evaluates the challenges and opportunities for stablecoin implementation in the region.
2025-07-09
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MEITUAN 03690.HK (MEITUAN) Latest News
Gate contract stock section will launch on April 13 with the first 5 Hong Kong stock perpetual contracts, including Tencent, Xiaomi, Meituan, and others, supporting 1x–20x leveraged trading
Gate News message, according to Gate’s official announcement, the Gate Contract Stock section will launch TENCENT (Tencent Holdings 00700.HK), XIAOMI (Xiaomi Group 01810.HK), MEITUAN (Meituan 03690.HK), KUAISHOU (Kuaishou 01024.HK), HKEX (Hong Kong Exchanges and Clearing 00388.HK) perpetual contract spot trading at 2026-04-13 14:00 (UTC+8). Trading uses USDT settlement and supports 1-20x leverage for both long and short positions. Among them, the TENCENT contract is based on Tencent Holdings, the XIAOMI contract is based on Xiaomi Group, the MEITUAN contract is based on Meituan, the KUAISHOU contract is based on Kuaishou, and the HKEX contract is based on Hong Kong Exchanges and Clearing. The price of each contract is denominated in USDT.
2026-04-02 02:02Hong Kong stocks: Most OpenClaw concept stocks fall; Zhipu drops more than 15%
Gate News message, April 2, Hong Kong stock OpenClaw concept stocks (AI open-source model related concepts) mostly fell. Zhipu (02513.HK) fell more than 15%, MINIMAX-W (00100.HK) fell more than 9%, Kingsoft Cloud (03896.HK) and Xiaomi Group (01810.HK) fell more than 3%, Alibaba (09988.HK) and Meituan (03690.HK) fell more than 2%.
2026-03-26 15:07Wang Xing: Meituan will not blindly pursue becoming a "Token factory," but instead views AI as a strategic opportunity.
BlockBeats News, March 26 — Regarding AI business, Meituan CEO Wang Xing stated that in the AI revolution, the only reasonable strategy is to attack rather than defend. However, Meituan will not blindly pursue becoming a "Token (word unit) factory," but will regard AI as a strategic opportunity. Since the beginning of 2023, Meituan has made large-scale investments in capital expenditure and AI talent. "Apart from companies with cloud computing businesses, Meituan's investment in AI is likely the largest among domestic companies, and we have been committed to this layout for over three years." Meituan continues to invest in its self-developed foundational large model LongCat, and is collaborating with top third-party large models in the industry, aiming to understand the real physical world as accurately as possible. Wang Xing believes that the key to the AI "super gateway" lies in accurately understanding user needs and efficiently executing tasks, which is far more complex than a "chatbot." "We hope to use a new generation of AI technology to make the Meituan app the first-choice platform for users to solve local life needs. We will strengthen AI search capabilities, enhance execution abilities, and strive to upgrade Meituan into a leading AI-driven application, becoming the future AI gateway for local life needs," Wang Xing said. (The Paper)
2026-03-26 01:51Meituan Open-Source LongCat-Next: 3B Parameters for Unified Visual Understanding, Generation, and Speech
According to 1M AI News monitoring, Meituan Longmao team has open-sourced LongCat-Next, a native multimodal model based on MoE architecture with 3 billion activated parameters. It unifies five capabilities—text, visual understanding, image generation, speech understanding, and speech synthesis—within a single autoregressive framework. The model and accompanying tokenizer are open-sourced under the MIT license, with weights available on HuggingFace. LongCat-Next's core design is the DiNA (Discrete Native Autoregressive) paradigm: by designing paired tokenizers and decoders for each modality, visual and audio signals are converted into discrete tokens, sharing the same embedding space with text, and all tasks are completed through unified next-token prediction. The key component on the visual side, dNaViT (Discrete Native Resolution Vision Transformer), extracts image features into "visual words," supporting dynamic tokenization and decoding. It maintains strong image generation quality even at 28 times compression ratio, especially excelling in text rendering. In comparisons with models of similar activated parameter size (A3B), LongCat-Next's main benchmark performances are: 1. Visual understanding: MMMU-Pro 60.3 (Qwen3-Omni 57.0, GPT5-minimal 62.7), MathVista 83.1 (Qwen3-Omni 75.9, GPT5-minimal 50.9), MathVision 64.7 (outperforming all comparison models), DocVQA 94.2 2. Image generation: GenEval 84.44, LongText-EN 93.15 (FLUX.1-dev 60.70, Emu-3.5 97.60) 3. Programming: SWE-Bench 43.0 (Kimi-Linear-48B 32.8, Qwen3-Next-80B 37.6) 4. Agent tool invocation: Tau2-Retail 73.68 (Qwen3-Next 57.3), Tau2-Telecom 62.06 (Qwen3-Next 13.2) In cross-model comparisons of understanding and generation within a unified model, LongCat-Next's MMMU score of 70.6 surpasses second-place NEO-unify (68.9), significantly exceeding previous unified model solutions like BAGEL (55.3) and Ovis-U1 (51.1). The performance of SWE-Bench 43.0 and Tau2 series tool invocation benchmarks also demonstrate that this multimodal unified architecture does not sacrifice pure text and agent capabilities.
2026-03-21 02:27Meituan open-sources a 560B parameter theorem-proving model, achieving a 97.1% success rate over 72 inferences, setting a new open-source SOTA.
Gate News reports that on March 21, the Meituan LongCat team open-sourced LongCat-Flash-Prover, a MoE model with 560 billion parameters focused on mathematical reasoning tasks in the formal theorem proving language Lean4. The model weights are released under the MIT license and are available on GitHub, Hugging Face, and ModelScope. This model breaks down formal reasoning into three independent capabilities: automatic formalization (converting natural language math problems into Lean4 formal statements), sketch generation (producing proof frameworks in lemma style), and complete proof generation. All three capabilities are integrated with reasoning (TIR) through the Agent toolkit, which interacts with the Lean4 compiler for real-time verification. For training, the team proposed the Hybrid-Experts Iteration Framework to generate cold-start data. During reinforcement learning, the HisPO algorithm was introduced to stabilize long-term training of the MoE model, along with theorem consistency and validity checks to prevent reward hacking. Benchmark results show that LongCat-Flash-Prover sets new state-of-the-art in automatic formalization and theorem proving among open-source models. On MiniF2F-Test, it achieved a 97.1% pass rate with only 72 inference attempts. On ProverBench and PutnamBench, it reached 70.8% and 41.5%, respectively, with no more than 220 inference steps per problem.
