Qwen 3.8 Max Live Now

Alibaba Cloud's Qwen team released Qwen3.8-Max, a 2.4-trillion-parameter mixture-of-experts flagship model now live on QwenCloud, delivering autonomous coding for projects spanning 10+ days and handling hundreds of professional tasks across legal, financial, and design domains. The model supports 1M context, native visual understanding for images, text, and video, and is priced at $2 per 1M input tokens and $6 per 1M output tokens.

Qwen3.8-Max Copied Try AI https://www.qwencloud.com/try-ai/chat?models=qwen3.8-max Add to Compare /compare?models=qwen3.8-max ReasoningVisual UnderstandingText Generation Overview ReasoningVisual UnderstandingText Generation 2.4-trillion-parameter MoE flagship delivering a comprehensive leap in coding and professional work. Autonomously codes and delivers complete projects spanning 10+ days. Handles hundreds of specialized tasks across legal, financial, design, and other professional domains, producing production-grade results end-to-end in a single conversation. Native visual understanding runs through the full cycle of planning, execution, and verification, enabling deep semantic analysis of ultra-long documents and extended video content. In long-horizon tasks, plans autonomously, iterates through closed feedback loops, and continuously evolves. Input ImageTextVideo Output Text Features Prefix Completion Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text. View docs https://docs.qwencloud.com/developer-guides/text-generation/partial-mode Function Calling Use function calling to connect large language models with external tools and systems. View docs https://docs.qwencloud.com/developer-guides/text-generation/function-calling Cache Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost. View docs https://docs.qwencloud.com/developer-guides/text-generation/context-cache implicit-cache Structured Outputs Structured Outputs help ensure the model returns a JSON string in the expected format. View docs https://docs.qwencloud.com/developer-guides/text-generation/structured-output Batches feature.funeTuning Pricing - Input$2Per 1M tokens - Output$6Per 1M tokens - Input Implicit Cache $0.25Per 1M tokens - Explicit Cache Creation$2.5Per 1M tokens - Explicit Cache Read$0.17Per 1M tokens Rate Limits & Context - Max Input991.80K - Max Output131.07K - RPMRequests Per Minute15K - TPMTokens Per Minute2M - Max Input Thinking 983.61K - Max Output Thinking 131.07K - Context1M Built-in Tools code interpreter https://docs.qwencloud.com/developer-guides/text-generation/code-interpreter Responses API web extractor https://docs.qwencloud.com/developer-guides/text-generation/web-scraping Responses API web search https://docs.qwencloud.com/developer-guides/text-generation/web-search Responses API t2i search https://docs.qwencloud.com/developer-guides/text-generation/image-search Responses API i2i search https://docs.qwencloud.com/developer-guides/text-generation/image-search Responses API API Reference Get API Key https://home.qwencloud.com/api-keys Copied 123456789101112131415161718 python import os import dashscope dashscope.base http api url = "https://dashscope-intl.aliyuncs.com/api/v1" messages = { "role": "user", "content": {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog and girl.jpeg"}, {"text": "What is depicted in the image?"} } response = dashscope.MultiModalConversation.call api key=os.getenv 'DASHSCOPE API KEY' , model='qwen3.8-max', messages=messages print response.output.choices 0 .message.content 0 "text"