{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen-vl-max","slug":"qwen-qwen-vl-max","name":"Qwen: Qwen VL Max","type":"model","url":"https://openrouter.ai/models/qwen~qwen-vl-max","page_url":"https://unfragile.ai/qwen-qwen-vl-max","categories":["image-generation"],"tags":["qwen","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$5.20e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen-vl-max__cap_0","uri":"capability://image.visual.multimodal.visual.language.understanding.with.extended.context","name":"multimodal visual-language understanding with extended context","description":"Processes both images and text simultaneously through a unified transformer architecture, maintaining semantic relationships across visual and linguistic modalities within a 7500-token context window. The model uses vision encoders to extract spatial and semantic features from images, then fuses them with text embeddings in a shared representation space, enabling joint reasoning about visual content and natural language queries without separate encoding passes.","intents":["I need to ask questions about images and get detailed textual analysis of visual content","I want to extract structured information from documents, charts, or diagrams by describing what I see","I need to compare multiple images or analyze visual relationships described in natural language","I want to understand complex visual scenes with text overlays, tables, or mixed media content"],"best_for":["developers building document intelligence applications requiring OCR + semantic understanding","teams creating visual QA systems for e-commerce, real estate, or content moderation","researchers analyzing scientific figures, charts, or visual data with natural language queries","product teams building accessibility features that describe images in detail"],"limitations":["7500-token context limit constrains analysis of very long documents or multiple high-resolution images in single request","No image generation capability — model is vision-understanding only, cannot create or edit images","Performance degrades with extremely dense visual information (e.g., wall-of-text screenshots, highly compressed images)","Requires API access via OpenRouter; no local deployment option available","No fine-tuning or custom model adaptation available through standard API"],"requires":["OpenRouter API key with Qwen VL Max model access","HTTP client capable of multipart form data (for image upload)","Image format support: JPEG, PNG, WebP, GIF (base64 encoded or URL)","Network connectivity to OpenRouter inference endpoints"],"input_types":["image (JPEG, PNG, WebP, GIF)","text (natural language queries, prompts)","mixed (image + text in single request)"],"output_types":["text (natural language descriptions, analysis, answers)","structured text (JSON-formatted responses if prompted)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-vl-max__cap_1","uri":"capability://image.visual.optical.character.recognition.with.semantic.context.preservation","name":"optical character recognition with semantic context preservation","description":"Extracts text from images while maintaining spatial layout, formatting, and semantic relationships between text elements through vision-language fusion. Rather than pure OCR character recognition, the model understands text within visual context (e.g., table structure, document hierarchy, text positioning) and can reason about relationships between extracted text and surrounding visual elements, producing contextually-aware transcriptions rather than raw character sequences.","intents":["I need to extract text from scanned documents, screenshots, or photos while preserving structure","I want to understand what text in an image means within its visual context (e.g., labels on diagrams)","I need to convert images of tables, forms, or structured documents into machine-readable text","I want to identify and extract specific text elements from cluttered or complex visual scenes"],"best_for":["document processing pipelines handling mixed-format inputs (scans, photos, screenshots)","teams building form digitization or data entry automation systems","applications requiring context-aware text extraction from technical diagrams or scientific papers","accessibility tools that need to describe text placement and relationships in images"],"limitations":["Handwriting recognition quality depends on legibility; cursive or poor-quality handwriting may have high error rates","Performance on very small text or low-resolution images is degraded compared to specialized OCR engines","Cannot extract text from heavily distorted, rotated, or perspective-skewed images without preprocessing","No batch processing optimization — each image requires separate API call, increasing latency for bulk document processing","Context window limit (7500 tokens) restricts analysis to ~20-30 pages of dense text per request"],"requires":["OpenRouter API key with Qwen VL Max access","Image preprocessing capability (optional but recommended for rotated/skewed images)","Text encoding support for Unicode (UTF-8) to handle multilingual documents"],"input_types":["image (scanned documents, screenshots, photos of text)"],"output_types":["text (extracted and formatted text)","structured data (JSON with text positions, confidence scores if requested)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-vl-max__cap_2","uri":"capability://image.visual.visual.question.answering.with.reasoning.over.image.content","name":"visual question answering with reasoning over image content","description":"Answers natural language questions about image content through a reasoning process that combines visual feature extraction with language understanding. The model identifies relevant visual regions, extracts semantic information from those regions, and generates answers by reasoning over the extracted visual facts and the question semantics, supporting both factual questions (what is in the image) and reasoning questions (why, how, what if) about visual content.","intents":["I want to ask detailed questions about what's in an image and get accurate answers","I need to verify claims or facts about visual content by asking specific questions","I want to understand relationships, spatial arrangements, or causal connections in images","I need to extract specific information from images by asking targeted questions rather than describing everything"],"best_for":["developers building chatbot interfaces for image analysis and exploration","content moderation systems that need to understand context and intent in user-submitted images","educational platforms where students can ask questions about diagrams, photos, or visual materials","e-commerce applications enabling customers to ask questions about product images"],"limitations":["Reasoning quality depends on image clarity and visual distinctiveness; ambiguous or low-quality images may produce uncertain answers","Cannot perform precise measurements or pixel-level analysis — answers are semantic approximations","May hallucinate details not present in image if question is leading or assumes content that isn't there","No memory of previous questions — each query is independent, requiring full context re-specification for follow-up questions","Performance on abstract, artistic, or highly stylized images is less reliable than on realistic photographs"],"requires":["OpenRouter API key with Qwen VL Max model access","Image in supported format (JPEG, PNG, WebP, GIF)","Natural language question phrased clearly for best results"],"input_types":["image (photograph, diagram, screenshot, artwork)","text (natural language question)"],"output_types":["text (natural language answer)","structured response (if prompted to format as JSON or specific schema)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-vl-max__cap_3","uri":"capability://image.visual.document.and.diagram.analysis.with.structured.information.extraction","name":"document and diagram analysis with structured information extraction","description":"Analyzes complex visual documents (PDFs rendered as images, technical diagrams, infographics, flowcharts) and extracts structured information by understanding visual hierarchy, spatial relationships, and semantic meaning. The model recognizes document structure (headers, sections, tables, lists), identifies key information elements, and can output extracted data in structured formats (JSON, CSV-compatible text) based on visual layout understanding rather than relying on embedded metadata.","intents":["I need to extract key information from PDF documents or scanned pages in structured format","I want to parse technical diagrams, flowcharts, or architectural drawings to understand their structure","I need to convert infographics or data visualizations into machine-readable structured data","I want to identify and extract specific fields from forms, invoices, or business documents"],"best_for":["enterprise document processing pipelines handling diverse document types","teams building intelligent document management systems with automatic categorization","technical documentation platforms that need to extract information from diagrams and specifications","financial or legal tech companies processing invoices, contracts, or compliance documents"],"limitations":["Extraction accuracy depends on document clarity and visual distinctiveness of information elements","Cannot handle documents with complex nested structures or highly stylized layouts reliably","No built-in validation or error correction — extracted data may contain inconsistencies requiring post-processing","Performance degrades on multi-page documents due to 7500-token context limit; requires splitting large documents","Cannot extract information from documents with security features (watermarks, redaction) that obscure content"],"requires":["OpenRouter API key with Qwen VL Max access","Document converted to image format (JPEG, PNG, WebP) if starting from PDF","Clear specification of desired output structure (JSON schema, CSV format, etc.)"],"input_types":["image (document page, diagram, infographic, form)"],"output_types":["structured text (JSON, CSV, YAML)","natural language summary with key information highlighted"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-vl-max__cap_4","uri":"capability://image.visual.comparative.visual.analysis.across.multiple.images","name":"comparative visual analysis across multiple images","description":"Analyzes and compares multiple images within a single request by maintaining visual context for each image and reasoning about similarities, differences, and relationships between them. The model processes image features for each input image and performs cross-image reasoning within the shared representation space, enabling tasks like identifying matching objects across images, detecting changes between versions, or analyzing visual consistency across a series of images.","intents":["I need to compare two or more images and identify differences or similarities","I want to verify that multiple images show the same object or scene from different angles","I need to detect changes between before/after images or across a sequence of images","I want to analyze visual consistency or style matching across multiple images"],"best_for":["quality assurance teams comparing product photos or design mockups","content moderation systems detecting duplicate or similar content across submissions","medical imaging applications comparing patient scans across time periods","e-commerce platforms enabling visual search and product matching"],"limitations":["Comparison accuracy degrades when images have significant resolution differences or different aspect ratios","Context window limit (7500 tokens) restricts number of images that can be analyzed simultaneously; typically 3-5 high-resolution images per request","Cannot perform pixel-level comparison or precise geometric alignment — comparisons are semantic","May struggle with subtle differences in images with similar overall composition","No temporal reasoning — cannot infer causality or sequence from image order alone"],"requires":["OpenRouter API key with Qwen VL Max access","Multiple images in supported formats (JPEG, PNG, WebP, GIF)","Clear specification of comparison criteria or questions"],"input_types":["image (multiple images, 2-5 recommended)"],"output_types":["text (comparative analysis, identified differences/similarities)","structured data (JSON with comparison results)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-vl-max__cap_5","uri":"capability://image.visual.context.aware.image.captioning.and.description.generation","name":"context-aware image captioning and description generation","description":"Generates natural language descriptions and captions for images by understanding visual content and producing contextually appropriate text at varying levels of detail. The model can generate brief captions (one sentence), detailed descriptions (paragraph-length), or specialized descriptions (technical, accessibility-focused, SEO-optimized) based on implicit or explicit context about the intended use of the description, using the full 7500-token context to produce rich, nuanced descriptions.","intents":["I need to generate alt text or accessibility descriptions for images","I want to create captions for social media or content platforms","I need detailed technical descriptions of diagrams, equipment, or scientific images","I want to generate SEO-optimized descriptions for e-commerce product images"],"best_for":["content management systems requiring automatic alt text generation for accessibility compliance","social media platforms generating captions for user-uploaded images","e-commerce platforms creating product descriptions from images","accessibility teams ensuring comprehensive image descriptions for visually impaired users"],"limitations":["Generated descriptions may be verbose or include unnecessary details for simple images","Cannot guarantee factual accuracy — may hallucinate details or misidentify objects in ambiguous images","Descriptions reflect model's training data biases; may not match domain-specific terminology or conventions","No control over description length without explicit prompting; default length varies based on image complexity","Performance on abstract, artistic, or highly stylized images may produce generic or inaccurate descriptions"],"requires":["OpenRouter API key with Qwen VL Max access","Image in supported format (JPEG, PNG, WebP, GIF)","Optional: specification of description style, length, or target audience"],"input_types":["image (photograph, diagram, artwork, screenshot)"],"output_types":["text (natural language caption or description at specified length)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key with Qwen VL Max model access","HTTP client capable of multipart form data (for image upload)","Image format support: JPEG, PNG, WebP, GIF (base64 encoded or URL)","Network connectivity to OpenRouter inference endpoints","OpenRouter API key with Qwen VL Max access","Image preprocessing capability (optional but recommended for rotated/skewed images)","Text encoding support for Unicode (UTF-8) to handle multilingual documents","Image in supported format (JPEG, PNG, WebP, GIF)","Natural language question phrased clearly for best results","Document converted to image format (JPEG, PNG, WebP) if starting from PDF"],"failure_modes":["7500-token context limit constrains analysis of very long documents or multiple high-resolution images in single request","No image generation capability — model is vision-understanding only, cannot create or edit images","Performance degrades with extremely dense visual information (e.g., wall-of-text screenshots, highly compressed images)","Requires API access via OpenRouter; 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