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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.","intents":["analyze images and describe their content with contextual understanding","extract structured information from visual documents and screenshots","process video frames and understand temporal relationships across sequences","answer questions about images with reasoning that references specific visual regions"],"best_for":["teams building document processing pipelines requiring visual understanding","developers creating multimodal AI agents that reason over images and text simultaneously","applications requiring efficient inference on resource-constrained infrastructure"],"limitations":["Linear attention trades some expressiveness for speed — may miss long-range dependencies that full attention captures in very complex visual scenes","Sparse MoE routing adds ~50-100ms overhead per inference due to expert selection computation","Video processing limited to frame-by-frame analysis; no native temporal modeling across frames","Maximum image resolution and video length not specified in available documentation"],"requires":["API access via OpenRouter or compatible inference endpoint","Input images in JPEG, PNG, or WebP format","Video inputs as frame sequences or base64-encoded data","Network connectivity for API calls"],"input_types":["text (prompts and questions)","image (JPEG, PNG, WebP)","video (frame sequences or encoded video data)"],"output_types":["text (descriptions, answers, reasoning)","structured data (JSON-formatted extractions)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-122b-a10b__cap_1","uri":"capability://text.generation.language.dense.text.generation.with.long.context.reasoning","name":"dense text generation with long-context reasoning","description":"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.","intents":["generate detailed written responses to complex questions requiring reasoning","maintain multi-turn conversation context without losing earlier discussion threads","produce structured outputs (JSON, code, markdown) with consistent formatting","summarize long documents or conversation histories while preserving key details"],"best_for":["conversational AI applications requiring nuanced, context-aware responses","content generation systems needing high-quality long-form text output","developers building agents that maintain conversation state across multiple turns"],"limitations":["Context window size not explicitly documented; typical for 122B models is 4K-32K tokens","No explicit fine-tuning API exposed; model behavior is fixed post-training","Sparse MoE routing may introduce minor variance in output quality depending on expert activation patterns","No native support for streaming partial responses (must wait for full generation)"],"requires":["API key for OpenRouter or compatible endpoint","Text input in UTF-8 encoding","Sufficient API rate limits for intended usage volume"],"input_types":["text (prompts, questions, conversation history)"],"output_types":["text (natural language responses)","structured text (JSON, code, markdown)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-122b-a10b__cap_2","uri":"capability://image.visual.video.frame.analysis.and.temporal.understanding","name":"video frame analysis and temporal understanding","description":"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.","intents":["extract key events and narrative structure from video content","identify object motion and scene transitions across video sequences","answer questions about what happens in specific video segments","generate summaries of video content highlighting temporal progression"],"best_for":["video content analysis platforms requiring automated understanding of visual narratives","accessibility applications generating descriptions of video content for visually impaired users","security and surveillance systems analyzing video feeds for event detection"],"limitations":["Frame-by-frame processing means no native temporal modeling — temporal understanding emerges from spatial analysis of consecutive frames rather than learned temporal embeddings","Maximum number of frames per video not specified; likely limited by context window constraints","No support for audio track analysis; video understanding is purely visual","Temporal resolution depends on frame sampling rate provided by user"],"requires":["Video input as frame sequence (JPEG/PNG) or base64-encoded video data","API access via OpenRouter","Frame extraction preprocessing if starting from raw video file"],"input_types":["video (frame sequences or encoded video)","text (questions or analysis prompts about video content)"],"output_types":["text (descriptions, event summaries, answers about video content)","structured data (timeline of events, object tracking data)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-122b-a10b__cap_3","uri":"capability://image.visual.document.and.screenshot.ocr.with.semantic.understanding","name":"document and screenshot ocr with semantic understanding","description":"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.","intents":["extract text from scanned documents, PDFs rendered as images, or screenshots","parse structured data from forms, tables, and invoices with semantic understanding","identify document type and extract relevant fields based on layout and content","convert unstructured document images into structured JSON or markdown formats"],"best_for":["document processing pipelines requiring both OCR and semantic understanding","form automation systems that need to extract and interpret form fields","knowledge workers building tools to digitize paper documents or screenshots"],"limitations":["OCR accuracy depends on image quality and resolution; low-resolution or heavily skewed images may produce errors","No native support for multi-page document processing — each page must be processed separately","Handwritten text recognition not explicitly supported; optimized for printed/digital text","Table extraction produces text representation rather than structured table data (requires post-processing for true table parsing)"],"requires":["Document or screenshot image in JPEG, PNG, or WebP format","Minimum recommended resolution of 300 DPI for document images","API access via OpenRouter"],"input_types":["image (document scans, screenshots, form images)","text (instructions for what to extract or how to structure output)"],"output_types":["text (extracted text content)","structured data (JSON with extracted fields, markdown-formatted content)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-122b-a10b__cap_4","uri":"capability://code.generation.editing.code.understanding.and.technical.documentation.analysis","name":"code understanding and technical documentation analysis","description":"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.","intents":["explain what a code snippet does and how it works","identify bugs, security issues, or performance problems in code","understand architecture diagrams and system design documentation","answer questions about technical documentation with code examples"],"best_for":["developers seeking code review and explanation assistance","teams onboarding new engineers who need codebase understanding","technical documentation systems that need to explain visual diagrams"],"limitations":["Code understanding is general-purpose; not specialized for specific languages or frameworks beyond training data coverage","Cannot execute code or verify correctness through runtime testing","Visual code (screenshots of IDEs) may lose syntax highlighting information that aids understanding","Large codebases must be analyzed in chunks; no native support for whole-repository analysis"],"requires":["Code input as text or image (screenshot of code editor)","API access via OpenRouter"],"input_types":["text (code snippets, technical questions)","image (code screenshots, architecture diagrams, flowcharts)"],"output_types":["text (explanations, suggestions, analysis)","structured data (identified issues, refactoring recommendations)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-122b-a10b__cap_5","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing","name":"api-based inference with streaming and batch processing","description":"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.","intents":["integrate Qwen 3.5 into applications without managing model infrastructure","process multiple inference requests efficiently through batch APIs","access the model through standard LLM SDKs and frameworks","scale inference workloads without managing GPU infrastructure"],"best_for":["startups and small teams without ML infrastructure expertise","applications requiring flexible model selection across multiple providers","developers building LLM applications who want to avoid infrastructure management"],"limitations":["API latency adds network round-trip overhead compared to local inference","Batch processing throughput depends on OpenRouter's infrastructure capacity and current load","No guarantee of response time SLAs; inference speed varies based on provider load","API costs scale with token usage; high-volume applications may be more cost-effective with self-hosted inference"],"requires":["OpenRouter API key (paid account)","Network connectivity to OpenRouter endpoints","HTTP client library or LLM SDK integration"],"input_types":["text (prompts, questions)","image (JPEG, PNG, WebP for vision tasks)","video (frame sequences for video analysis)"],"output_types":["text (model responses)","structured data (JSON-formatted outputs)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or compatible inference endpoint","Input images in JPEG, PNG, or WebP format","Video inputs as frame sequences or base64-encoded data","Network connectivity for API calls","API key for OpenRouter or compatible endpoint","Text input in UTF-8 encoding","Sufficient API rate limits for intended usage volume","Video input as frame sequence (JPEG/PNG) or base64-encoded video data","API access via OpenRouter","Frame extraction preprocessing if starting from raw video file"],"failure_modes":["Linear attention trades some expressiveness for speed — may miss long-range dependencies that full attention captures in very complex visual scenes","Sparse MoE routing adds ~50-100ms overhead per inference due to expert selection computation","Video processing limited to frame-by-frame analysis; no native temporal modeling across frames","Maximum image resolution and video length not specified in available documentation","Context window size not explicitly documented; typical for 122B models is 4K-32K tokens","No explicit fine-tuning API exposed; model behavior is fixed post-training","Sparse MoE routing may introduce minor variance in output quality depending on expert activation patterns","No native support for streaming partial responses (must wait for full generation)","Frame-by-frame processing means no native temporal modeling — temporal understanding emerges from spatial analysis of consecutive frames rather than learned temporal embeddings","Maximum number of frames per video not specified; likely limited by context window constraints","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwen-qwen3.5-122b-a10b","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3.5-122b-a10b"}},"signature":"5+sV/5ORBjoGQpDfFE72qei8SVL23dNdv4IgmHcmntKI1WAwGj7ccAziRRaSIKOEPvnjTdfnWUUdgWPV75bOBQ==","signedAt":"2026-06-20T02:13:14.794Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3.5-122b-a10b","artifact":"https://unfragile.ai/qwen-qwen3.5-122b-a10b","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3.5-122b-a10b","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}