Models

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: * Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. * A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. Long-context Support up to 128K tokens.

Following the release of Qwen2.5, the team responded to the community's demand for handling longer contexts. Over the past few months, significant optimizations have been made to enhance the model's capabilities and inference performance for extremely long contexts. Now, the team is proud to introduce the new **Qwen2.5-Turbo** model, featuring the following advancements: - **Extended Context Support**: The context length has been increased from 128k to 1M tokens, equivalent to approximately 1 million English words or 1.5 million Chinese characters. This capacity corresponds to 10 full-length novels, 150 hours of speech transcripts, or 30,000 lines of code. Qwen2.5-Turbo achieves 100% accuracy in the 1M-token Passkey Retrieval task and scores 93.1 on the RULER long-text evaluation benchmark, outperforming GPT-4 (91.6) and GLM4-9B-1M (89.9). Moreover, the model retains strong performance in short sequence tasks, comparable to GPT-4o-mini. - **Faster Inference Speed**: Leveraging sparse attention mechanisms, the time to generate the first token for a 1M-token context has been reduced from 4.9 minutes to just 68 seconds, representing a 4.3x speed improvement. - **Cost Efficiency**: The pricing remains unchanged at $0.05 per 1M tokens. At this rate, Qwen2.5-Turbo processes 3.6 times more tokens than GPT-4o-mini for the same cost.

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: * Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. * Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. * Long-context Support up to 128K tokens and can generate up to 8K tokens. * Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

Qwen2-VL is the latest iteration of multimodal large language models developed by the Qwen team at Alibaba Cloud. This advanced AI system represents a significant leap forward in the field of vision-language models, building upon its predecessor, Qwen-VL. Qwen2-VL boasts state-of-the-art capabilities in understanding images of various resolutions and aspect ratios, as well as the ability to comprehend videos exceeding 20 minutes in length. One of the most notable features of Qwen2-VL is its versatility as an agent capable of operating mobile devices, robots, and other systems based on visual input and text instructions. This makes it a powerful tool for a wide range of applications, from personal assistance to industrial automation. The model also offers robust multilingual support, enabling it to understand and process text in various languages within images, catering to a global user base.

Qwen2-VL is the latest iteration of multimodal large language models developed by the Qwen team at Alibaba Cloud. This advanced AI system represents a significant leap forward in the field of vision-language models, building upon its predecessor, Qwen-VL. Qwen2-VL boasts state-of-the-art capabilities in understanding images of various resolutions and aspect ratios, as well as the ability to comprehend videos exceeding 20 minutes in length. One of the most notable features of Qwen2-VL is its versatility as an agent capable of operating mobile devices, robots, and other systems based on visual input and text instructions. This makes it a powerful tool for a wide range of applications, from personal assistance to industrial automation. The model also offers robust multilingual support, enabling it to understand and process text in various languages within images, catering to a global user base.

Qwen2-Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT4o).

Qwen2-Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT4o).

Qwen2-Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT4o).

Qwen2-Audio is the new series of Qwen large audio-language models. Qwen2-Audio is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. We introduce two distinct audio interaction modes: * voice chat: users can freely engage in voice interactions with Qwen2-Audio without text input; * audio analysis: users could provide audio and text instructions for analysis during the interaction;