Models

Text generation-Qwen

The commercial models of the Qwen series, boasts the latest capabilities and enhancements over its open source counterpart.

Qwen-Max

Qwen-Max provides the best inference performance among Qwen models, especially for complex and multi-step tasks. Usage instructions | API reference | Try online

Qwen-Plus

Qwen-Plus provides a balanced combination of performance, speed, and cost, ideal for moderately complex tasks. Usage instructions | API reference | Try online

Qwen-Turbo

Qwen-Turbo provides fast speed and low cost, suitable for simple tasks. Usage instructions | API reference | Try online

Qwen-VL

Qwen-VL is a text generation model that can understand and process images. The model performs OCR operations and provides further functionalities, such as summarizing and reasoning. For example, it can extract product attributes from photos, and solving problems from images. Usage instructions | API reference | Try online

Qwen-VL is billed based on the total number of input and output tokens.

Image token calculation rule: Every 28 × 28 pixels count as 1 token. Each image converts to at least 4 tokens. For more information, see Calculate image tokens.

Qwen-MT

Qwen-MT is a large language model for machine translation built based on Qwen. It specializes in Chinese-English translation and multilingual translation between Chinese/English and 24 other languages, including Japanese, Korean, French, Spanish, German, Portuguese (Brazilian), Thai, Indonesian, Vietnamese, and Arabic. Qwen-MT also provides capabilities such as terminology intervention, domain prompting, and translation memory to enhance translation quality in complex scenarios. Usage instructions

Text generation - Qwen - open source

• In the model name, 'xxb' indicates the parameter scale. For example, 'qwen2-72b-instruct' has 72 billion parameters.

• Model Studio facilitates the use of open source Qwen models without the need for local deployment. Qwen2 is most recommended among the open source models.

Qwen2.5

Qwen2.5 is the latest series of the Qwen LLM. For Qwen2.5, we have launched a series of base and instruct models with parameter sizes ranging from 7 billion to 72 billion. Qwen2.5 has made the following improvements over Qwen2:

• Qwen2.5 is pre-trained on our latest large-scale dataset containing 18 trillion tokens.

• Thanks to our expert models in specific fields, Qwen2.5 has significantly increased knowledge and greatly improved coding and maths capabilities.

• Qwen2.5 has shown significant improvements in following instructions, generating long texts (over 8K tokens), understanding structured data (such as tables), and generating structured outputs (especially JSON). It supports more diversified system prompts, enhancing its role-playing and conditional setting as a chatbot.

• Qwen2.5 supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

Usage instructions | API reference | Try online

Qwen2

The open-source Qwen2 models. Usage instructions | API reference | Try online

Qwen1.5

The open-source Qwen1.5 models. Usage instructions | API reference | Try online

Qwen-VL - open source

The open-source version of Qwen-VL. Usage instructions | API reference

Qwen2.5-VL has made the following improvements over Qwen2-VL:

• Richer perception of the world: Qwen2.5-VL is good at recognizing common objects such as flowers, birds, fish, and insects, as well as analyzing text, charts, icons, graphics, and layouts within images.

• Long video understanding: Qwen2.5-VL can understand videos of up to 10 minutes. It can also pinpoint video segments to capture events.

• Visual locating: Qwen2.5-VL can accurately locate objects in images by generating bounding boxes (coordinates for the top-left and bottom-right corners) or points (coordinates for the center of the bounding box). It can provide stable JSON outputs for these coordinates.

• Structured output: Qwen2.5-VL supports structured output for data such as invoices, forms, and tables, suitable in finance, business, among other scenarios.

Text embedding

Converts text into numerical representations, suitable for search, clustering, recommendation, and classification tasks. Billed based on the number of input tokens. API reference