With the rapid development of artificial intelligence, the integration of visual and language capabilities has led to groundbreaking advancements in visual language models (VLMs). These models aim to simultaneously process and understand both visual and textual data, being widely applied in scenarios such as image captioning, visual question answering, optical character recognition, and multimodal content analysis. VLMs play a significant role in developing autonomous systems, enhancing human-computer interaction, and creating efficient document processing tools, successfully bridging the gap between these two data modalities. However, there are challenges in handling high-resolution visual data and diverse textual input.

DeepSeek AI recently launched DeepSeek-V2.5-1210, an enhanced version of DeepSeek-V2.5, aimed at improving AI performance in math, programming, writing, and reasoning tasks. Earlier versions of the model achieved some success in solving math and reasoning tasks, but the stability of performance across various applications needed enhancement, especially in real-time coding and meticulous writing. These shortcomings underscore the potential for developing a more flexible and reliable AI model.

DeepSeek V2.5 demonstrates exceptional performance in the field of artificial intelligence, particularly in code generation and chat models. Through comparative testing with GPT-4, it has achieved significant improvements across multiple metrics, including win rates, MT-Bench, and AlignBench scores. In terms of code generation capabilities, DeepSeek V2.5 achieved a HumanEval score of 89% and a LiveCodeBench score of 41%, showcasing its ability to generate high-quality, executable code.

Recently, a major breakthrough occurred in the field of artificial intelligence. The Coder v2 model launched by AI company Deepseek has excelled in various tests, surpassing Google's Gemini model and becoming the new king in the open source AI domain. According to the evaluation results from Livebench AI, which is recognized as the most challenging benchmarking platform in the industry, the Deepseek Coder v2 model ranks third among open source models, following the commercial models of OpenAI and Anthropic.

DeepSeek recently announced the release of an open-source model called DeepSeek-Coder-V2, which surpasses GPT-4-Turbo in code and mathematical capabilities, and features significant expansion in multilingual support and context processing length. Based on the DeepSeek-V2 model architecture, DeepSeek-Coder-V2 employs a mixture of experts (MoE) architecture specifically designed to enhance code and mathematical reasoning abilities.

Deep Exploration has released a large model of 67B, named DeepSeekLLM67B. This model is fully open-source and can be accessed at chat.deepseek.com. DeepSeekLLM67B has performed well on public evaluation leaderboards, exhibiting excellent reasoning, mathematical, and programming capabilities. DeepSeek has also open-sourced models in two sizes, 7B and 67B, and provided 9 model checkpoints from mid-training.

DeepSeek has launched the open source large model DeepSeek Coder. DeepSeek Coder is an intelligent code assistant that can generate various types of code. DeepSeek Coder has been open-sourced on Hugging Face and GitHub. DeepSeek Coder has demonstrated outstanding performance in tests on internationally authoritative datasets. DeepSeek is dedicated to exploring the essence of AGI and will release more research findings.