Model overview Qwen3.5-397B-A17B is a large language model developed by Qwen that combines dense and sparse architectures to deliver exceptional performance with efficient inference. The model contains 397 billion total parameters but activates only 17 billion during inference, making it more cost-effective than traditional dense models. It represents a significant advancement in the Qwen series, building on earlier releases like Qwen2.5-3B and Qwen3-1.7B by introducing multimodal capabilities and architectural innovations that improve both reasoning and efficiency. Qwen3.5-397B-A17B Qwen Qwen2.5-3B Qwen3-1.7B Model inputs and outputs Qwen3.5-397B-A17B processes text and images as inputs and generates coherent text responses. The model supports an exceptionally long context window of 262,144 tokens natively, which extends up to 1,010,000 tokens when needed. This allows it to handle extensive documents, lengthy conversations, and complex multi-step tasks without losing important information. Qwen3.5-397B-A17B Inputs Text prompts in 201 different languages and dialects for comprehensive global coverageImages for vision-language tasks including visual reasoning, document understanding, and spatial analysisStructured data such as tables and formatted information for parsing and analysis Text prompts in 201 different languages and dialects for comprehensive global coverage Text prompts Images for vision-language tasks including visual reasoning, document understanding, and spatial analysis Images Structured data such as tables and formatted information for parsing and analysis Structured data Outputs Generated text in the same language as the input, maintaining context and coherence across long conversationsCode for programming tasks across multiple languages and frameworksStructured responses including JSON and other formatted outputs for tool integrationMultimodal reasoning combining textual and visual understanding for complex tasks Generated text in the same language as the input, maintaining context and coherence across long conversations Generated text Code for programming tasks across multiple languages and frameworks Code Structured responses including JSON and other formatted outputs for tool integration Structured responses Multimodal reasoning combining textual and visual understanding for complex tasks Multimodal reasoning Capabilities This model excels across multiple domains through its hybrid architecture. In mathematics and reasoning, it achieves state-of-the-art performance on benchmarks including HMMT and AIME competitions. For coding tasks, it handles software engineering challenges and terminal commands with high accuracy. Vision-language capabilities span from document understanding and text recognition to mathematical problem-solving with visual components. The model demonstrates strong performance in agent tasks, enabling tool use and complex multi-step planning. Multilingual support extends to 201 languages, allowing it to understand cultural nuances and regional variations. Long-context processing enables handling of books, extensive codebases, and prolonged multi-turn conversations where earlier tokens remain accessible and relevant. What can I use it for? Development teams can deploy this model for code generation and software engineering tasks through managed inference services like Alibaba Cloud Model Studio. Content creators can leverage its long-context capabilities to process and analyze extensive documents or generate lengthy coherent texts. Businesses seeking multilingual AI agents can build customer support systems, research assistants, or knowledge workers that operate across global markets. Educational institutions can integrate it into tutoring systems for mathematics and STEM subjects where reasoning quality matters. Enterprises can use it for document analysis, data extraction from unstructured sources, and complex knowledge work requiring both language understanding and visual processing. The sparse architecture makes deployment cost-effective for large-scale applications. Things to try Experiment with the native 262,144 token context by feeding entire books, research papers, or code repositories to test whether the model maintains consistency and accuracy across extreme document lengths. Push the multilingual capabilities by mixing languages within a single prompt to see how well it handles code-switching and cross-lingual reasoning. Combine image and text inputs in complex scenarios like providing screenshots of error messages alongside code snippets to test multimodal problem-solving. Use the tool calling and agent capabilities to build autonomous workflows that interact with external systems, APIs, and databases. Compare performance on your specific domain tasks against smaller dense models to understand the practical benefits of the efficient sparse architecture for your particular use case. This is a simplified guide to an AI model called Qwen3.5-397B-A17B maintained by Qwen. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called Qwen3.5-397B-A17B maintained by Qwen. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called Qwen3.5-397B-A17B maintained by Qwen. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.

This Qwen Model Eats Entire Books (262K Tokens) and Still Remembers the Plot

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