Qwen2.5-72B-Instruct supports 16k context and generates long texts exceeding 8K. It enables seamless interaction with external systems through function calls, greatly enhancing flexibility and scalability. The model's knowledge has significantly increased, and its coding and mathematical abilities have been greatly improved, with multilingual support for over 29 languages.

Qwen2.5-32B-Instruct is a large language model with 32 billion parameters, offering balanced performance, optimized for Chinese and multilingual scenarios, and supporting applications such as intelligent Q&A and content generation.

Qwen2.5-14B-Instruct is a large language model with 14 billion parameters, delivering excellent performance, optimized for Chinese and multilingual scenarios, and supporting applications such as intelligent Q&A and content generation.

Qwen2.5-7B-Instruct is a large language model with 7 billion parameters, supporting function calls and seamless interaction with external systems, greatly enhancing flexibility and scalability. It is optimized for Chinese and multilingual scenarios, supporting applications such as intelligent Q&A and content generation.

Qwen2 is the latest series of the Qwen model, supporting 128k context. Compared to the current best open-source models, Qwen2-72B significantly surpasses leading models in natural language understanding, knowledge, coding, mathematics, and multilingual capabilities.

Qwen2 is the latest series of the Qwen model, capable of outperforming optimal open-source models of similar size and even larger models. Qwen2 7B has achieved significant advantages in multiple evaluations, especially in coding and Chinese comprehension.

Qwen2.5-Coder-32B-Instruct is a large language model specifically designed for code generation, code understanding, and efficient development scenarios, featuring an industry-leading 32 billion parameters to meet diverse programming needs.

Qwen2.5-Coder-14B-Instruct is a large-scale pre-trained programming instruction model with strong code understanding and generation capabilities, efficiently handling various programming tasks, particularly suited for intelligent code writing, automated script generation, and programming problem-solving.

Qwen2-VL-72B is a powerful visual language model that supports multimodal processing of images and text, capable of accurately recognizing image content and generating relevant descriptions or answers.

InternVL2.5-26B is a powerful visual language model that supports multimodal processing of images and text, capable of accurately recognizing image content and generating relevant descriptions or answers.

InternVL2-8B is a powerful visual language model that supports multimodal processing of images and text, capable of accurately recognizing image content and generating relevant descriptions or answers.

GLM-4-9B-Chat demonstrates high performance across various aspects, including semantics, mathematics, reasoning, coding, and knowledge. It also features web browsing, code execution, custom tool invocation, and long text reasoning, supporting 26 languages including Japanese, Korean, and German.

Yi-1.5-34B significantly enhances mathematical logic and coding abilities by incrementally training on 500 billion high-quality tokens while maintaining the excellent general language capabilities of the original series.

DeepSeek Coder 33B is a code language model trained on 20 trillion data points, of which 87% are code and 13% are in Chinese and English. The model introduces a 16K window size and fill-in-the-blank tasks, providing project-level code completion and snippet filling capabilities.

CodeGeeX4-ALL-9B is a multilingual code generation model that supports comprehensive functions including code completion and generation, code interpretation, web search, function calls, and repository-level code Q&A, covering various scenarios in software development. It is a top-tier code generation model with fewer than 10B parameters.