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Recently, a new evaluation benchmark - RBench-V, specifically designed to test the visual reasoning capabilities of multi-modal large models, was released by research teams from Tsinghua University, Tencent HUNYUAN, Stanford University, and Carnegie Mellon University. The introduction of this benchmark aims to fill the gap in the current evaluation system regarding the model's visual output capabilities, allowing for a more comprehensive understanding of existing model performance. The RBench-V benchmark consists of 803 questions covering multiple fields, including geometry and graph theory, mechanics and electromagnetism, multi-target recognition, and path planning.

Kunlun Wanwei has officially released Skywork R1V (referred to as "R1V"), the world's first industrial-grade multimodal reasoning model. This 3.8 billion parameter model's performance is close to the well-known closed-source model DeepSeek-R1, and even surpasses it in several benchmark tests, outperforming a series of current state-of-the-art (SOTA) technologies. Kunlun Wanwei's decision to open-source R1V aims to promote technology sharing and progress, injecting new vitality into the global AI open-source community. R1V is distinguished by its superior multimodal reasoning capabilities.

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.