{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-vl-32b-instruct","slug":"qwen-qwen3-vl-32b-instruct","name":"Qwen: Qwen3 VL 32B Instruct","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-vl-32b-instruct","page_url":"https://unfragile.ai/qwen-qwen3-vl-32b-instruct","categories":["image-generation"],"tags":["qwen","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.04e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_0","uri":"capability://image.visual.multimodal.vision.language.understanding.with.image.text.reasoning","name":"multimodal vision-language understanding with image-text reasoning","description":"Processes images and text simultaneously using a unified transformer architecture that fuses visual tokens from a vision encoder with text embeddings, enabling the model to answer questions about image content, describe visual scenes, and reason across visual and textual information in a single forward pass. The 32B parameter scale allows for nuanced spatial reasoning and semantic understanding of complex visual compositions.","intents":["I need to ask questions about what's in an image and get detailed answers","I want to extract text and structured information from screenshots or documents","I need to analyze visual content for content moderation or classification purposes","I want to generate detailed descriptions of images for accessibility or cataloging"],"best_for":["developers building document processing pipelines","teams creating multimodal AI applications requiring visual understanding","builders prototyping vision-based chatbots or assistants"],"limitations":["Image resolution and aspect ratio constraints may affect fine-grained detail recognition","No real-time video processing — processes individual frames or short video clips with latency","Context window limitations may reduce performance on very long text-image combinations","Inference latency scales with image resolution and batch size"],"requires":["API key for OpenRouter or direct Qwen API access","HTTP/REST client capability","Images in standard formats (JPEG, PNG, WebP, GIF)","Text input in UTF-8 encoding"],"input_types":["image (JPEG, PNG, WebP, GIF, BMP)","text (natural language questions or prompts)","mixed multimodal sequences (interleaved text and images)"],"output_types":["text (natural language descriptions, answers, analysis)","structured text (JSON-formatted extractions, lists)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_1","uri":"capability://image.visual.video.frame.analysis.and.temporal.reasoning","name":"video frame analysis and temporal reasoning","description":"Accepts video input (or sequences of frames) and performs temporal reasoning by processing multiple frames in context, understanding motion, scene changes, and temporal relationships between visual elements. The model maintains coherence across frames through attention mechanisms that track object persistence and state changes, enabling understanding of video narratives and dynamic visual events.","intents":["I need to understand what happens in a video clip and describe the sequence of events","I want to extract key moments or scenes from video content","I need to analyze video for content classification or safety purposes","I want to answer questions about temporal relationships in video (before/after, causality)"],"best_for":["video content moderation platforms","developers building video understanding APIs","teams analyzing surveillance or instructional video content"],"limitations":["Video processing requires frame extraction and sequential processing, adding latency","Maximum frame count per request may limit analysis of very long videos","Temporal reasoning quality degrades with sparse frame sampling","No real-time streaming support — requires pre-extracted frames or complete video upload"],"requires":["Video in standard formats (MP4, WebM, MOV) or pre-extracted frame sequences","Frame extraction capability (ffmpeg or equivalent) if working with raw video files","API key for OpenRouter or Qwen API","Sufficient context window to accommodate multiple frames"],"input_types":["video file (MP4, WebM, MOV, AVI)","image sequence (ordered frames as separate images)","text prompts describing temporal queries"],"output_types":["text (scene descriptions, event summaries, temporal analysis)","structured data (frame-level annotations, event timestamps)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_2","uri":"capability://image.visual.document.and.table.extraction.with.structured.output","name":"document and table extraction with structured output","description":"Analyzes document images (PDFs, scans, screenshots) to extract text, tables, and structured data with layout awareness. Uses visual understanding to identify table boundaries, column headers, and cell content, then outputs structured formats (JSON, CSV, Markdown) that preserve the original document structure. The model understands document semantics including headers, footers, and multi-column layouts.","intents":["I need to convert scanned documents or PDFs into machine-readable structured data","I want to extract tables from images and convert them to CSV or JSON","I need to parse invoices, receipts, or forms and extract key-value pairs","I want to understand document layout and hierarchy for content organization"],"best_for":["document processing automation teams","developers building OCR-adjacent applications","enterprises digitizing paper-based workflows"],"limitations":["Handwritten text recognition is less reliable than printed text","Complex multi-page documents require separate processing per page","Table extraction accuracy decreases with irregular layouts or merged cells","Requires clear image quality — low-resolution or heavily skewed documents may fail"],"requires":["Document images in JPEG, PNG, or PDF format","Minimum image resolution of 150 DPI for reliable text extraction","API key for OpenRouter or Qwen API","Optional: JSON schema definition for structured output validation"],"input_types":["image (document scan, screenshot, PDF page render)","text (instructions for extraction format or field names)"],"output_types":["text (extracted text with formatting)","structured data (JSON, CSV, Markdown table formats)","key-value pairs (form field extraction)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_3","uri":"capability://image.visual.visual.question.answering.with.reasoning.chains","name":"visual question answering with reasoning chains","description":"Answers natural language questions about images by performing multi-step visual reasoning. The model decomposes complex questions into sub-questions, locates relevant visual regions, and chains reasoning steps together to arrive at answers. Supports both factual questions (what objects are present) and reasoning questions (why, how, what if) by leveraging the 32B parameter capacity for deeper inference.","intents":["I want to ask detailed questions about image content and get accurate answers","I need to perform visual reasoning tasks like counting, comparing, or inferring relationships","I want to understand the context and meaning behind visual elements in an image","I need to verify claims or facts about image content"],"best_for":["developers building visual search or image understanding APIs","teams creating educational or accessibility tools","builders prototyping multimodal chatbots"],"limitations":["Reasoning quality depends on image clarity and question specificity","Very complex multi-step reasoning may require explicit prompting or chain-of-thought formatting","Hallucination risk increases with ambiguous or low-quality images","Performance on abstract or artistic images is less reliable than photographic content"],"requires":["Image in standard format (JPEG, PNG, WebP)","Natural language question or prompt","API key for OpenRouter or Qwen API","Optional: Few-shot examples for specialized reasoning tasks"],"input_types":["image (photograph, diagram, screenshot, artwork)","text (natural language question or instruction)"],"output_types":["text (natural language answer with explanation)","structured reasoning (step-by-step explanation of inference)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_4","uri":"capability://image.visual.image.classification.and.semantic.tagging","name":"image classification and semantic tagging","description":"Classifies images into semantic categories and generates descriptive tags by analyzing visual content. The model identifies objects, scenes, activities, and attributes present in images, then maps them to predefined or open-ended category systems. Supports both zero-shot classification (without training examples) and few-shot adaptation through in-context learning.","intents":["I need to automatically categorize images into predefined classes","I want to generate semantic tags or labels for image content","I need to filter or organize image collections by visual characteristics","I want to detect and label specific objects or scenes in bulk image processing"],"best_for":["content management and digital asset management teams","developers building image search or recommendation systems","teams automating image organization workflows"],"limitations":["Classification accuracy depends on category clarity and visual distinctiveness","Zero-shot performance may be lower than fine-tuned specialized models","Ambiguous images may receive multiple conflicting tags","Custom category systems require clear definition in prompts"],"requires":["Image in standard format (JPEG, PNG, WebP)","Category definitions or taxonomy (optional for zero-shot)","API key for OpenRouter or Qwen API","Optional: Few-shot examples for improved accuracy on custom categories"],"input_types":["image (photograph, diagram, artwork)","text (category definitions, taxonomy, or tagging instructions)"],"output_types":["text (comma-separated tags or category labels)","structured data (JSON with confidence scores per category)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_5","uri":"capability://image.visual.multimodal.instruction.following.with.complex.prompts","name":"multimodal instruction following with complex prompts","description":"Executes complex, multi-step instructions that combine visual and textual inputs, following detailed specifications for output format, reasoning style, and content constraints. The model parses structured prompts (including system instructions, few-shot examples, and detailed task descriptions) and applies them consistently across multimodal inputs. Supports instruction-following patterns like chain-of-thought, role-playing, and format specifications.","intents":["I need to apply consistent processing rules to images with detailed specifications","I want to use few-shot examples to teach the model custom tasks","I need to enforce specific output formats (JSON, Markdown, XML) for downstream processing","I want to combine multiple reasoning styles or perspectives in image analysis"],"best_for":["developers building custom vision pipelines with specific requirements","teams implementing domain-specific image analysis workflows","builders creating instruction-tuned multimodal applications"],"limitations":["Instruction complexity is limited by context window size","Very long or ambiguous instructions may lead to inconsistent outputs","Format compliance requires explicit specification and may need validation","Few-shot learning effectiveness depends on example quality and relevance"],"requires":["Image in standard format (JPEG, PNG, WebP)","Detailed text instructions or system prompts","API key for OpenRouter or Qwen API","Optional: Few-shot examples in the same format as target output"],"input_types":["image (any visual content)","text (system instructions, task descriptions, few-shot examples)"],"output_types":["text (formatted according to instructions)","structured data (JSON, XML, Markdown, CSV as specified)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_6","uri":"capability://image.visual.visual.content.safety.and.moderation.analysis","name":"visual content safety and moderation analysis","description":"Analyzes images for potentially harmful, inappropriate, or policy-violating content by identifying visual elements that may require moderation. The model detects violence, explicit content, hate symbols, misinformation indicators, and other safety-relevant visual patterns. Provides confidence scores and detailed explanations for moderation decisions, enabling human-in-the-loop review workflows.","intents":["I need to automatically screen user-uploaded images for policy violations","I want to identify potentially harmful visual content before it's published","I need to generate moderation reports with reasoning for content decisions","I want to detect specific harmful patterns (violence, explicit content, symbols)"],"best_for":["content moderation teams at social platforms","developers building safety-first image upload systems","teams implementing automated content governance"],"limitations":["Context-dependent moderation decisions may require human review","False positive rates increase with ambiguous or artistic content","Cultural and regional variations in moderation standards require customization","Satire, educational, and journalistic content may be incorrectly flagged"],"requires":["Image in standard format (JPEG, PNG, WebP)","Moderation policy definitions or category specifications","API key for OpenRouter or Qwen API","Optional: Custom moderation guidelines for domain-specific rules"],"input_types":["image (user-generated content, uploads, or bulk content)"],"output_types":["text (moderation decision with reasoning)","structured data (JSON with violation categories and confidence scores)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_7","uri":"capability://image.visual.scene.understanding.and.spatial.reasoning","name":"scene understanding and spatial reasoning","description":"Understands spatial relationships, object positions, and scene composition by analyzing visual layouts. The model identifies foreground/background relationships, depth cues, spatial arrangements, and geometric relationships between objects. Supports queries about relative positions, occlusion, perspective, and scene structure, enabling applications that require spatial reasoning beyond simple object detection.","intents":["I need to understand the spatial layout and composition of a scene","I want to answer questions about object positions and relationships","I need to detect occlusion, overlap, and depth relationships in images","I want to analyze perspective and geometric properties of visual content"],"best_for":["developers building robotics or autonomous systems with visual perception","teams creating spatial analysis or architectural visualization tools","builders implementing scene understanding for VR/AR applications"],"limitations":["Spatial reasoning accuracy depends on image clarity and perspective consistency","Ambiguous depth cues may lead to incorrect spatial interpretations","Very complex scenes with many overlapping objects may reduce accuracy","Perspective distortion or unusual viewpoints can confuse spatial reasoning"],"requires":["Image in standard format (JPEG, PNG, WebP)","Optional: Spatial reasoning queries or instructions","API key for OpenRouter or Qwen API"],"input_types":["image (photograph, diagram, scene rendering)","text (spatial reasoning questions or instructions)"],"output_types":["text (spatial descriptions, relationship explanations)","structured data (coordinates, relative positions, spatial relationships)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-32b-instruct__cap_8","uri":"capability://image.visual.text.recognition.and.ocr.with.language.understanding","name":"text recognition and ocr with language understanding","description":"Recognizes and extracts text from images while understanding context and language semantics. Beyond character-level OCR, the model comprehends text meaning, identifies text language, handles multiple scripts, and understands text in context (e.g., captions, labels, handwriting). Supports text extraction from complex layouts including rotated text, overlapping text, and variable font sizes.","intents":["I need to extract text from images while understanding its meaning and context","I want to recognize text in multiple languages or scripts from a single image","I need to handle challenging OCR scenarios like handwriting or rotated text","I want to extract and understand text labels, captions, and annotations"],"best_for":["developers building document digitization pipelines","teams processing multilingual content","builders creating accessibility tools for image-to-text conversion"],"limitations":["Handwritten text recognition is less reliable than printed text","Very small text or low-resolution images may fail to extract accurately","Heavily stylized or decorative fonts may be misrecognized","Mixed-language text requires clear language boundaries"],"requires":["Image in standard format (JPEG, PNG, WebP)","Minimum image resolution of 100 DPI for reliable text extraction","API key for OpenRouter or Qwen API","Optional: Language hints for improved accuracy on multilingual content"],"input_types":["image (document, screenshot, sign, label, handwritten content)"],"output_types":["text (extracted text with formatting preserved)","structured data (JSON with text regions, bounding boxes, language tags)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API key for OpenRouter or direct Qwen API access","HTTP/REST client capability","Images in standard formats (JPEG, PNG, WebP, GIF)","Text input in UTF-8 encoding","Video in standard formats (MP4, WebM, MOV) or pre-extracted frame sequences","Frame extraction capability (ffmpeg or equivalent) if working with raw video files","API key for OpenRouter or Qwen API","Sufficient context window to accommodate multiple frames","Document images in JPEG, PNG, or PDF format","Minimum image resolution of 150 DPI for reliable text extraction"],"failure_modes":["Image resolution and aspect ratio constraints may affect fine-grained detail recognition","No real-time video processing — processes individual frames or short video clips with latency","Context window limitations may reduce performance on very long text-image combinations","Inference latency scales with image resolution and batch size","Video processing requires frame extraction and sequential processing, adding latency","Maximum frame count per request may limit analysis of very long videos","Temporal reasoning quality degrades with sparse frame sampling","No real-time streaming support — requires pre-extracted frames or complete video upload","Handwritten text recognition is less reliable than printed text","Complex multi-page documents require separate processing per page","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.27,"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-vl-32b-instruct","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-vl-32b-instruct"}},"signature":"tkVVvIzKA6x0ZZyj7Iusi/nOfu0Wi+aAPd+kFntYc0GMTuvqE9cXUKQ9KzyUbW34aPiPzAJ45CUR6Six279fDg==","signedAt":"2026-06-19T19:31:58.030Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-vl-32b-instruct","artifact":"https://unfragile.ai/qwen-qwen3-vl-32b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-vl-32b-instruct","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"}}