Qwen: Qwen3.5-122B-A10B

Processes images, text, and video inputs simultaneously using a hybrid architecture combining linear attention mechanisms with sparse mixture-of-experts routing. The linear attention reduces computational complexity from quadratic to linear in sequence length, enabling efficient processing of high-resolution images and long video sequences without proportional memory overhead. The sparse MoE layer routes inputs to specialized expert subnetworks, activating only relevant experts per token rather than the full model capacity.

Generates coherent, contextually-aware text responses using the 122B parameter model with support for extended context windows. The sparse MoE architecture allows the model to maintain large context without proportional memory growth, as only active experts process each token. Responses are generated autoregressively with support for structured output formatting and multi-turn conversation context preservation.

Analyzes video inputs by processing frame sequences through the vision-language model, with the linear attention mechanism enabling efficient handling of multiple frames without quadratic memory growth. The model can reason about temporal relationships, object motion, scene changes, and narrative progression across video frames. Processing occurs through frame-by-frame encoding followed by cross-frame attention patterns that identify temporal coherence.

Extracts text and structured information from document images and screenshots using visual understanding combined with language modeling. The vision component identifies text regions and layout structure, while the language model component performs semantic understanding of extracted content, enabling extraction of not just raw text but contextual meaning, relationships between elements, and structured data interpretation. Linear attention efficiency allows processing of high-resolution document images without memory constraints.

Analyzes code snippets, technical documentation, and architecture diagrams through the vision-language interface, understanding both textual code and visual representations of systems. The model can explain code logic, identify potential issues, suggest improvements, and answer questions about technical content. The language component provides deep reasoning about code semantics while the vision component handles visual technical content like diagrams and flowcharts.

Provides access to the Qwen 3.5 122B model through OpenRouter's API infrastructure, supporting both single-request inference and batch processing workflows. The API abstracts the underlying sparse MoE and linear attention implementation, exposing standard LLM interfaces for text generation, vision processing, and multimodal understanding. Requests are routed through OpenRouter's load balancing infrastructure, which handles model serving, scaling, and provider selection.