Core Insights - Alibaba's shares surged following the announcement of Quen 3, its next-generation AI model, which is designed to enhance performance while reducing computational costs [1] - The open-source nature of Quen 3 poses a significant competitive threat to both domestic rivals in China and major LLM developers like OpenAI and Anthropic [2] - The K web ETF, which tracks major Chinese internet companies, has increased approximately 2% today and around 40% year-to-date, indicating a strong performance in the China tech sector [3] Company Developments - Both Alibaba and BU are now training their AI models on in-house chips, reducing their dependence on Nvidia [2] - The trend of developing in-house technology reflects China's growing self-sufficiency in AI and the competitive landscape against US companies [4][5] - Rival AI models, such as Kimmy K2, are emerging, further intensifying competition in the AI space [5]

Model Performance & Capabilities - Quen 3, an open-source frontier coding model from Alibaba, was tested for various capabilities [1] - Quen 3 successfully generated code for a 2D Navier Stokes solver and a 3D rotating dodcahedron with bouncing spheres [1] - The model demonstrated spatial reasoning failure in a cube rotation task, but the code generation was successful [1] - Quen 3 passed a "needle in a haystack" test by finding a password within the entire book of Harry Potter and the Sorcerer's Stone [1] - The model exhibited censorship regarding Tiananmen Square [1] - Quen 3 refused to take a stance on political questions, providing balanced perspectives on Trump and Kamla [1][2] - The model provided a thoughtful and nuanced response to a prompt about quitting a job and leaving family [2][3][4][5] - Quen 3 refused to answer illegal questions, such as how to hotwire a car [6] - The model provided a correct diagnosis and management plan for acute anterior myocardial infarction [6][7] - Quen 3 gave a good answer to the trolley problem, evaluating morality using utilitarianism and deontology [7][8] - The model showed reasoning traces in its output when answering gotcha questions, although with some errors [11][12][13][14] Technology & Implementation - Together AI sponsors the use of Quen 3, offering high-performance serverless endpoints and pay-per-token pricing [1][2] - Quen Code, an open-source version of Claude Code, works well with Quen 3 and can be installed via npm [2] - The model has a massive context window, natively 256k tokens, with up to 1 million achieved [1]

Model Overview - Kimmy K2 is a state-of-the-art mixture of experts language model with 32 billion activated parameters and 1 trillion total parameters [3] - The model was pre-trained on 155% trillion tokens with zero training instability [4] - Kimmy K2 supports up to 2 million tokens in the context window [5] Performance Benchmarks - Kimmy K2 Instruct beats Deepseek, Quen, and GPT41 on SWEBench verified, coming in right behind Cloud 4 Opus [7] - On Live Codebench, Kimmy K2 beats Cloud 4 Opus [7] - Kimmy K2 tops the list on Amy 2025 for math, GPQA Diamond [8] Optimization and Training - The model is trained with the Muon optimizer [4] - Kimmy K2 achieves exceptional performance across frontier knowledge reasoning and coding tasks [4] - The training process was open source [8] Availability and Cost - Inference is available through Kimmy directly at $0.15 per million input tokens with a cache, $0.60 without a cache, and $2.50 per million output tokens [10] - Kimmy K2 is available on Open Router [13] Industry Reception - Industry experts compare Kimmy K2 to Deep Seek V3 [11] - Kimmy K2 is recognized as a potentially new leader in open LLMs [14]