腾讯，重磅开源

Core Insights - Tencent has launched the Hunyuan-A13B, the industry's first 13B-level MoE (Mixture of Experts) open-source inference model, which features a total of 80 billion parameters but activates only 13 billion, achieving high performance with lower resource requirements [1][2] Model Performance - Hunyuan-A13B is one of Tencent's most utilized large language models, with over 400 business applications and an average daily request volume exceeding 130 million [2] - In various authoritative industry benchmarks, Hunyuan-A13B has demonstrated competitive performance compared to models like OpenAI's o1-1217, DeepSeek's R1-0120, and Qwen3-A22B [2][3] Benchmark Results - In the Mathematics category, Hunyuan-A13B scored 87.3 in AIME2024, outperforming OpenAI's o1-1217 and DeepSeek's R1-0120 [3] - Hunyuan-A13B excelled in reasoning tasks, achieving a score of 89.1 in BBH, indicating strong reasoning capabilities [3] - The model also showed notable performance in agent tool invocation and long-text capabilities, utilizing a multi-agent data synthesis framework [3] Model Features - The Hunyuan-A13B allows users to select between fast and slow reasoning modes, optimizing resource allocation for efficiency and task accuracy [4] - This model is part of Tencent's ongoing efforts to enhance its AI capabilities, following the release of the TurboS model, which focuses on rapid reasoning [4] Strategic Developments - Tencent is restructuring its large model R&D system, focusing on three core areas: computing power, algorithms, and data management [5] - The company has established new departments dedicated to large language models and multimodal models, aiming to explore cutting-edge technologies and improve model capabilities [5] Financial Investments - Tencent's R&D expenditure reached 70.69 billion yuan in 2024, with capital expenditures showing a significant year-on-year increase of 221%, reflecting the company's commitment to AI investment [6] - The increase in capital spending is attributed to the acquisition of more GPUs to meet growing inference demands, with plans for further investment in 2025 [6]