Since 2025, the speed at which Multimodal Large Language Models (MLLMs) move from the laboratory to industrial applications has significantly accelerated, with the e-commerce industry becoming one of the first to benefit. Unlike traditional pure text models, multimodal large models can simultaneously understand various forms of information such as text, images, videos, audio, etc., which makes them highly applicable in e-commerce scenarios.
In the process of product search and recommendation, the multimodal large model has achieved a leapfrog upgrade from "keyword matching" to "semantic understanding+visual recognition". Traditional e-commerce search relies on keyword matching of product titles, and users often need precise input to find the target product. The search system based on multimodal large models can understand the product images uploaded by users, physical photos taken, and even vague colloquial descriptions, and accurately match the corresponding products. The "photo search for the same product" feature launched by Taobao in 2025 is supported by a multimodal large model - it not only recognizes the appearance of products in pictures, but also understands the scene, style, and matching relationship in pictures. The relevance of recommendation results has increased by about 35% compared to traditional solutions.
The production of product content is another aspect deeply reshaped by multimodal technology. In the past, product detail pages, main images, short videos, and other materials on e-commerce platforms required professional teams to produce, with high costs and long cycles. Now, AI content generation tools based on multimodal big models can automatically generate multilingual and multi style marketing copy and visual materials based on the basic information of products. The "AI Product Assistant" feature launched by Pinduoduo in early 2026 allows merchants to upload a main product image and a few basic descriptions, and the system can automatically generate a complete product detail page, including selling point extraction, scene image generation, and multi version copywriting. The material production time for a single product has been reduced from days to minutes.
Virtual try on and digital live streaming are more cutting-edge application directions for multimodal capabilities. By understanding human posture, clothing materials, and the relationship between light and shadow, multimodal large models can accurately simulate the fitting effect on photos uploaded by users. JD's "AI fitting room" will be launched during the 618 shopping festival in 2026. Users can try on over a million clothing items on the platform by uploading a full body photo, with a return time of no more than 3 seconds. At the same time, digital human anchors based on multimodal drivers are providing 24-hour product explanations and interactive Q&A, significantly reducing the labor and time costs of live streaming e-commerce.
Of particular note is that multimodal large models have also shown great potential in the fields of after-sales and customer service. When users upload photos of damaged products or describe installation issues, the multimodal customer service system can "understand" the problems in the pictures and provide accurate solutions in conjunction with the knowledge base, greatly reducing the proportion of people who need to be referred to manual customer service. According to data from a leading e-commerce platform in Q1 2026, after integrating multimodal customer service, the AI one-time solution rate for after-sales problems has increased from 42% to 71%.
Of course, the large-scale deployment of multimodal large models in e-commerce also faces significant challenges. The inference cost is still relatively high, especially in real-time interactive scenarios, where the single response delay and computational overhead of large models need to be continuously optimized. In addition, real-time updates of product information pose higher requirements for the model's long-term memory and incremental learning capabilities. But with the development of model miniaturization technology and edge inference hardware, these constraints are gradually being alleviated.
It can be foreseen that multimodal big models are transforming from mere marketing tools to irreplaceable core components in e-commerce infrastructure.
[Reference source] The content of this article is comprehensively compiled from public product dynamics and industry analysis reports on platforms such as Taobao, JD.com, and Pinduoduo.