Sou Hu Cai Jing·2026-02-16 16:07Core Insights - The main theme in the large model industry over the past two years has been "scaling up," but this has led to increased deployment costs, making it harder for companies to afford these models. The performance curve and adoption curve are diverging [1] - Alibaba's release of the Qwen 3.5-Plus model, with 397 billion total parameters and only 17 billion activated, demonstrates a shift in focus from merely increasing parameters to enhancing model efficiency and cost-effectiveness [1][3] Model Performance and Efficiency - Qwen 3.5-Plus surpasses the previous generation Qwen 3-Max and competes favorably with models like GPT-5.2 and Gemini 3 pro in various benchmarks, achieving scores such as 87.8 in MMLU-Pro and 88.4 in GPQA [1][3] - The model's API pricing is significantly lower, at 0.8 yuan per million tokens, which is 1/18 of Gemini 3 pro's price, indicating a new cost structure in the industry [1][8] Architectural Innovation - The industry is experiencing a shift from parameter accumulation to architectural innovation, similar to the transition in the chip industry from single-core to multi-core architectures [3] - Qwen 3.5 achieves efficiency by using only 17 billion parameters for inference, resulting in an 8.6 times increase in throughput for 32K context scenarios and up to 19 times for 256K context scenarios, while reducing deployment memory usage by 60% [3][4] Multi-Modal Capabilities - Qwen 3.5 represents a generational leap to a native multi-modal model, integrating text and visual data from the start, which enhances its capabilities compared to models that assemble components separately [4][7] - The model supports direct input of 2-hour videos and can convert hand-drawn sketches into executable front-end code, showcasing its advanced multi-modal functionalities [7] Strategic Implications - Alibaba's commitment to native multi-modal capabilities positions Qwen as a foundational model for enterprise applications, which inherently require multi-modal functionalities [8] - The collaboration between model architecture, chip optimization, and cloud infrastructure results in a sustainable cost structure, challenging closed-source competitors who rely on performance exclusivity [8][9] Market Position and Growth - Qwen is ranked first in the Chinese enterprise-level large model market, with Alibaba Cloud's market share reaching 35.8% in the AI cloud market, surpassing the combined share of the second to fourth competitors [11][12] - The open-source model ecosystem is rapidly expanding, with over 400 models released and more than 200,000 derivative models created, indicating strong developer engagement and market traction [12] Future Considerations - The competition in the large model industry is transitioning from a parameter race to an architecture race, where efficiency and cost become the core competitive dimensions [12][13] - Questions remain about the sustainability of closed-source models in light of open-source alternatives that match performance and cost, as well as the viability of current assembly methods in multi-modal training [13]