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Linear attention reduces computational complexity from O(n²) to O(n) while sparse MoE selectively activates expert parameters based on input type and content, enabling efficient processing of high-resolution visual content alongside text without full model activation.","intents":["analyze images and describe their content with contextual text understanding","extract structured information from documents, charts, and diagrams","process video frames and understand temporal relationships across sequences","answer questions about visual content by reasoning across image and text modalities"],"best_for":["teams building document processing pipelines requiring visual + textual understanding","developers creating multimodal RAG systems with image indexing","applications requiring efficient batch processing of visual content at scale"],"limitations":["Linear attention trades some expressiveness for speed — may miss long-range dependencies in very complex visual scenes compared to full quadratic attention","Sparse MoE routing adds ~50-100ms overhead for expert selection and load balancing per request","Video processing limited to frame-by-frame analysis; no native temporal modeling across video sequences","Maximum image resolution and video frame count not specified in documentation"],"requires":["API key for OpenRouter or direct Qwen API access","HTTP/REST client capable of multipart form data for image uploads","Support for base64 encoding or URL-based image references"],"input_types":["text (prompts, questions, instructions)","image (JPEG, PNG, WebP, GIF formats)","video (frame sequences or video file references)"],"output_types":["text (descriptions, answers, extracted information)","structured JSON (when prompted for extraction tasks)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-plus-02-15__cap_1","uri":"capability://image.visual.native.video.frame.analysis.and.temporal.reasoning","name":"native video frame analysis and temporal reasoning","description":"Processes video inputs by decomposing them into frame sequences and applying vision-language understanding across temporal boundaries. The sparse MoE architecture selectively activates video-specialized experts when video tokens are detected, enabling efficient analysis of motion, scene changes, and temporal relationships without processing every frame through the full model capacity.","intents":["summarize video content and extract key events across multiple frames","detect scene changes, cuts, and transitions in video sequences","answer temporal questions about video content (e.g., 'what happens after X event')","extract structured metadata from video (speaker identification, scene descriptions, action sequences)"],"best_for":["video content moderation and safety analysis platforms","automated video summarization and highlight extraction services","accessibility tools generating captions and descriptions for video content"],"limitations":["Frame-by-frame processing without native temporal convolution — may miss subtle motion patterns requiring optical flow analysis","No built-in support for variable frame rates; requires preprocessing to standardize temporal sampling","Maximum video length and frame count per request not documented","Temporal reasoning limited to sequential frame analysis; no bidirectional temporal attention across the full video"],"requires":["Video preprocessing pipeline to extract frames or provide video file URLs","Frame rate specification for consistent temporal sampling","API key for OpenRouter or Qwen direct access"],"input_types":["video files (format support not fully specified)","frame sequences (as individual images)","text queries about video content"],"output_types":["text descriptions of video content and events","structured JSON with temporal metadata (timestamps, scene descriptions)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-plus-02-15__cap_2","uri":"capability://automation.workflow.efficient.batch.inference.with.dynamic.expert.routing","name":"efficient batch inference with dynamic expert routing","description":"Implements sparse mixture-of-experts routing that dynamically selects which expert parameters activate based on input content type and complexity, reducing per-token computation from full model capacity to a fraction of parameters. The routing mechanism uses learned gating functions to assign tokens to specialized experts (vision, language, multimodal), enabling high-throughput inference without loading all parameters for every request.","intents":["process high-volume batches of mixed text and image requests with minimal latency variance","reduce inference costs by activating only necessary model capacity per input type","scale API endpoints to handle concurrent requests without proportional compute scaling","optimize token-per-second throughput for production inference pipelines"],"best_for":["production API services handling variable input types at scale","cost-sensitive applications requiring per-request optimization","teams building inference infrastructure with strict latency SLAs"],"limitations":["Expert routing adds ~50-100ms per-request overhead for gating computation and load balancing","Uneven expert utilization can cause load imbalance across hardware — requires monitoring and potential rebalancing","Expert capacity is fixed at model training time; cannot dynamically add experts for new input types post-deployment","Batch size optimization requires tuning; small batches may not fully utilize expert parallelism"],"requires":["API client supporting batch request submission","Monitoring infrastructure to track expert utilization and load balance","Understanding of MoE routing overhead for latency budgeting"],"input_types":["text","image","video","mixed multimodal batches"],"output_types":["text","structured JSON"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-plus-02-15__cap_3","uri":"capability://image.visual.high.resolution.image.understanding.with.linear.attention.scaling","name":"high-resolution image understanding with linear attention scaling","description":"Processes high-resolution images using linear attention mechanisms that scale O(n) instead of O(n²), enabling efficient encoding of dense visual tokens without memory explosion. The architecture decomposes image patches into token sequences and applies linear attention transformations, allowing processing of images with significantly more pixels than quadratic-attention models while maintaining spatial reasoning capability.","intents":["analyze high-resolution documents, scans, and diagrams without downsampling","extract fine-grained details from images (small text, intricate patterns, technical drawings)","process large batches of images without memory constraints limiting resolution","maintain spatial relationships and layout understanding in complex visual documents"],"best_for":["document digitization and OCR pipelines requiring high fidelity","technical diagram and schematic analysis systems","medical imaging analysis where detail preservation is critical"],"limitations":["Linear attention approximations may lose some fine-grained spatial relationships compared to full quadratic attention in very complex scenes","Maximum image resolution not specified; practical limits depend on token budget and hardware","Linear attention kernels require specialized implementation — may have compatibility issues with some inference hardware","Trade-off between resolution and context window — higher resolution images consume more tokens, reducing available context for text"],"requires":["Images in supported formats (JPEG, PNG, WebP, GIF)","API client supporting image upload or URL references","Understanding of token budget implications for high-resolution inputs"],"input_types":["image (high-resolution, multiple formats)","text queries about image content"],"output_types":["text descriptions and extracted information","structured JSON with spatial metadata"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-plus-02-15__cap_4","uri":"capability://text.generation.language.multilingual.text.generation.and.understanding","name":"multilingual text generation and understanding","description":"Generates and understands text across multiple languages using a shared token vocabulary and language-agnostic attention mechanisms. The model applies the same linear attention and sparse MoE routing to all languages, with language-specific expert routing enabling efficient multilingual inference without separate model instances per language.","intents":["translate text between supported languages while preserving meaning and context","generate multilingual content from single prompts (e.g., product descriptions in 10 languages)","understand and respond to queries in non-English languages with cultural context awareness","build multilingual chatbots and customer support systems with single model instance"],"best_for":["global applications requiring multilingual support without model duplication","translation and localization services","international customer support platforms"],"limitations":["Language coverage not specified — some languages may have lower quality due to training data imbalance","Code-switching (mixing languages in single input) behavior not documented","Language-specific cultural nuances may be lost in translation compared to human translators","No explicit language detection — requires user to specify language or relies on implicit detection"],"requires":["API key for OpenRouter or Qwen direct access","Language specification in prompts or API parameters","Text input in supported languages"],"input_types":["text in multiple languages","mixed-language prompts"],"output_types":["text in requested language","structured JSON with language metadata"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-plus-02-15__cap_5","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.content","name":"structured data extraction from unstructured content","description":"Extracts structured information (JSON, tables, key-value pairs) from unstructured text and images using prompt-based schema specification and constrained decoding. The model applies vision-language understanding to identify relevant content regions, then generates structured output conforming to specified schemas, with optional validation against provided JSON schemas.","intents":["extract invoice data (amounts, dates, vendor info) from document images","parse tables and convert them to structured JSON or CSV formats","identify and extract entities (names, addresses, phone numbers) from text or images","generate structured API responses from natural language descriptions"],"best_for":["document processing and data entry automation","knowledge extraction from unstructured documents","API response generation from natural language specifications"],"limitations":["Extraction accuracy depends on schema clarity and example quality in prompts","No built-in schema validation — requires post-processing to ensure output conforms to specified schema","Hallucination risk when extracting information not present in source content","Complex nested schemas may require multiple passes or prompt engineering"],"requires":["JSON schema or structured format specification in prompt","Source content (text or image) containing information to extract","API key for OpenRouter or Qwen direct access"],"input_types":["text (unstructured)","image (documents, forms, tables)","JSON schema specification"],"output_types":["JSON","CSV","structured key-value pairs"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-plus-02-15__cap_6","uri":"capability://code.generation.editing.context.aware.code.understanding.and.generation","name":"context-aware code understanding and generation","description":"Analyzes and generates code across multiple programming languages using vision-language understanding to parse code syntax from images and text, combined with language-specific expert routing in the MoE layer. Supports code completion, explanation, and refactoring by maintaining semantic understanding of code structure and applying language-specific reasoning patterns.","intents":["explain code snippets and technical documentation with visual diagrams","generate code from natural language descriptions or pseudocode","refactor code while preserving functionality and improving readability","debug code by analyzing error messages and suggesting fixes"],"best_for":["educational platforms teaching programming with visual code examples","code documentation generation from source code and diagrams","technical support systems explaining code to non-technical users"],"limitations":["Code generation accuracy varies by language — specialized languages may have lower quality","No execution environment — cannot verify generated code correctness","Large codebases may exceed context window; requires chunking or summarization","Language-specific idioms and best practices may not be fully captured"],"requires":["Code input in text or image format","Language specification for code generation tasks","API key for OpenRouter or Qwen direct access"],"input_types":["code (text or image)","natural language descriptions","pseudocode","error messages"],"output_types":["code (multiple languages)","explanations (text)","refactored code"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-plus-02-15__cap_7","uri":"capability://planning.reasoning.reasoning.and.multi.step.problem.solving","name":"reasoning and multi-step problem solving","description":"Performs multi-step reasoning and problem decomposition using chain-of-thought patterns and planning-aware expert routing. The sparse MoE architecture activates reasoning-specialized experts when processing complex queries, enabling step-by-step problem solving with explicit intermediate reasoning steps that improve accuracy on tasks requiring logical inference.","intents":["solve math problems by showing step-by-step work and reasoning","decompose complex questions into sub-problems and solve systematically","analyze arguments and identify logical fallacies or inconsistencies","plan multi-step workflows or project timelines from natural language descriptions"],"best_for":["educational applications requiring explainable problem solving","technical support systems providing detailed troubleshooting steps","planning and project management tools"],"limitations":["Reasoning quality depends on problem complexity — very complex problems may exceed reasoning capability","No external tool access for verification — cannot validate mathematical calculations or check facts","Reasoning steps may be verbose, increasing token consumption and latency","Hallucination risk in reasoning chains — intermediate steps may be plausible but incorrect"],"requires":["Complex query or problem statement","API key for OpenRouter or Qwen direct access","Tolerance for longer response times due to multi-step reasoning"],"input_types":["text (questions, problems, scenarios)","image (diagrams, charts, visual problems)"],"output_types":["text with step-by-step reasoning","structured JSON with reasoning steps and final answer"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3.5-plus-02-15__cap_8","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.support","name":"api-based inference with streaming and batch support","description":"Provides HTTP/REST API access to the model with support for both streaming (token-by-token) and batch inference modes. Streaming responses enable real-time output display and early termination, while batch mode optimizes throughput for non-latency-sensitive workloads. The API abstracts underlying sparse MoE routing and linear attention mechanisms, exposing a standard interface compatible with OpenAI API conventions.","intents":["integrate Qwen into existing applications via standard REST API","stream model outputs to user interfaces for real-time interaction","process large batches of requests asynchronously for cost optimization","monitor and control inference through API parameters (temperature, max_tokens, etc.)"],"best_for":["web applications and chatbots requiring real-time streaming responses","backend services processing high-volume batch inference jobs","teams integrating multiple LLM providers with unified API abstraction"],"limitations":["API latency includes network round-trip time — not suitable for sub-100ms response requirements","Streaming responses consume more API calls than batch mode — higher cost for high-volume workloads","Rate limiting and quota management required for production deployments","API availability depends on OpenRouter or Qwen infrastructure uptime"],"requires":["API key for OpenRouter or Qwen direct access","HTTP client library (curl, requests, axios, etc.)","Network connectivity to API endpoint","Understanding of API rate limits and quota management"],"input_types":["text (prompts, messages)","image (base64 encoded or URLs)","video (frame sequences or URLs)"],"output_types":["text (streaming or batch)","structured JSON responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API key for OpenRouter or direct Qwen API access","HTTP/REST client capable of multipart form data for image uploads","Support for base64 encoding or URL-based image references","Video preprocessing pipeline to extract frames or provide video file URLs","Frame rate specification for consistent temporal sampling","API key for OpenRouter or Qwen direct access","API client supporting batch request submission","Monitoring infrastructure to track expert utilization and load balance","Understanding of MoE routing overhead for latency budgeting","Images in supported formats (JPEG, PNG, WebP, GIF)"],"failure_modes":["Linear attention trades some expressiveness for speed — may miss long-range dependencies in very complex visual scenes compared to full quadratic attention","Sparse MoE routing adds ~50-100ms overhead for expert selection and load balancing per request","Video processing limited to frame-by-frame analysis; no native temporal modeling across video sequences","Maximum image resolution and video frame count not specified in documentation","Frame-by-frame processing without native temporal convolution — may miss subtle motion patterns requiring optical flow analysis","No built-in support for variable frame rates; requires preprocessing to standardize temporal sampling","Maximum video length and frame count per request not documented","Temporal reasoning limited to sequential frame analysis; no bidirectional temporal attention across the full video","Expert routing adds ~50-100ms per-request overhead for gating computation and load balancing","Uneven expert utilization can cause load imbalance across hardware — requires monitoring and potential rebalancing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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-plus-02-15","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3.5-plus-02-15"}},"signature":"KigGpCCI9ME/vSRmYTN+ih8FDa7JdXq634hbwfP2MacmwJdD3KF6Tie0xnicjRJPSc8x0xB0w3dedU7ZfbMTDQ==","signedAt":"2026-06-21T09:05:21.297Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3.5-plus-02-15","artifact":"https://unfragile.ai/qwen-qwen3.5-plus-02-15","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3.5-plus-02-15","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"}}