The Qwen team recently announced the open-source release of their latest multimodal reasoning model, QVQ, marking an important step forward in the capabilities of artificial intelligence in visual understanding and complex problem-solving. This model is based on Qwen2-VL-72B and aims to enhance AI reasoning capabilities by combining language and visual information. In the MMMU evaluation, QVQ achieved a high score of 70.3, demonstrating significant performance improvements over Qwen2-VL-72B-Instruct in several math-related benchmark tests.

On December 18th, at the Volcano Engine FORCE Power Conference, Volcano Engine President Tan Dai unveiled the new Doubao Visual Understanding Model. This model processes both text and image-related information simultaneously, providing more accurate answers. The new model excels in content recognition, understanding, and reasoning capabilities, featuring more nuanced visual description abilities, which brings stronger intelligent processing capabilities to enterprise users. Tan Dai revealed at the conference that the Doubao Visual Understanding Model has achieved significant technological breakthroughs and has also seen a dramatic reduction in pricing.

Microsoft has released Phi-3.5-vision, a lightweight, multimodal open source AI model designed for processing textual and visual inputs, supporting a context length of 128K. This model is suitable for resource-constrained environments and features capabilities such as image understanding, OCR, chart parsing, and multi-image summarization, showcasing excellent performance and low latency. Comprised of 4.2 billion parameters, it is trained with high-quality data to ensure performance and privacy. It includes three models: lightweight AI, expert mix, and multimodal model, all demonstrating outstanding performance in image and video processing benchmarks.