Compare Qwen Cloud vs. Qwen3.5

Qwen Cloud is a cutting-edge platform designed for artificial intelligence, offering a variety of pre-built models, tools, and applications that facilitate the creation and deployment of smart products seamlessly. It features a consolidated API that caters to numerous functions including text generation, intricate reasoning, programming, image and video comprehension, creation and editing of visuals, video production, speech generation, voice replication, multimodal interactions, embeddings, re-ranking, and agent-based applications. Developers have the opportunity to explore advanced models through the Try AI feature, transition from initial prototypes to full-scale production with comprehensive documentation and ready-to-use templates, and easily integrate with OpenAI-compatible SDKs and clients simply by adjusting model parameters. The platform encompasses Qwen's language and vision-language models, Wan's image and video capabilities, CosyVoice's speech technology, as well as multimodal models adept at processing text, images, audio, and video content. Additionally, the platform's built-in function calling support enables models to interact with external tools and APIs, while its reasoning abilities effectively manage complex tasks such as multi-step mathematics and logical reasoning challenges. With such a robust feature set, Qwen Cloud empowers developers to innovate and enhance the capabilities of their intelligent applications significantly.

Qwen3.5 represents a major advancement in open-weight multimodal AI models, engineered to function as a native vision-language agent system. Its flagship model, Qwen3.5-397B-A17B, leverages a hybrid architecture that fuses Gated DeltaNet linear attention with a high-sparsity mixture-of-experts framework, allowing only 17 billion parameters to activate during inference for improved speed and cost efficiency. Despite its sparse activation, the full 397-billion-parameter model achieves competitive performance across reasoning, coding, multilingual benchmarks, and complex agent evaluations. The hosted Qwen3.5-Plus version supports a one-million-token context window and includes built-in tool use for search, code interpretation, and adaptive reasoning. The model significantly expands multilingual coverage to 201 languages and dialects while improving encoding efficiency with a larger vocabulary. Native multimodal training enables strong performance in image understanding, video processing, document analysis, and spatial reasoning tasks. Its infrastructure includes FP8 precision pipelines and heterogeneous parallelism to boost throughput and reduce memory consumption. Reinforcement learning at scale enhances multi-step planning and general agent behavior across text and multimodal environments. Overall, Qwen3.5 positions itself as a high-efficiency foundation for autonomous digital agents capable of reasoning, searching, coding, and interacting with complex environments.