{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-vl-8b-instruct","slug":"qwen-qwen3-vl-8b-instruct","name":"Qwen: Qwen3 VL 8B Instruct","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-vl-8b-instruct","page_url":"https://unfragile.ai/qwen-qwen3-vl-8b-instruct","categories":["image-generation"],"tags":["qwen","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$8.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_0","uri":"capability://image.visual.interleaved.mrope.multimodal.fusion.for.vision.language.understanding","name":"interleaved-mrope multimodal fusion for vision-language understanding","description":"Processes images and text through a unified transformer architecture using Interleaved-MRoPE (Multimodal Rotary Position Embeddings) to align visual and linguistic token sequences. This approach enables the model to reason across modalities by maintaining positional awareness of both image patches and text tokens in a single embedding space, allowing structured understanding of spatial relationships and semantic connections between visual and textual content.","intents":["I need to ask questions about images and get detailed answers that reference specific visual elements","I want to analyze images with complex layouts, charts, or diagrams and extract structured information","I need to understand relationships between multiple images and text descriptions in a single query"],"best_for":["developers building document analysis systems with mixed text-image content","teams creating visual question-answering applications","researchers working on multimodal reasoning tasks"],"limitations":["8B parameter size limits reasoning depth on highly complex visual scenes compared to larger models","Interleaved-MRoPE adds computational overhead during inference (~15-20% vs single-modality models)","Performance degrades on images with extreme aspect ratios or very small text without preprocessing","No explicit support for 3D spatial reasoning or temporal video understanding beyond frame-level analysis"],"requires":["API access via OpenRouter or compatible inference endpoint","Images in JPEG, PNG, or WebP format (max resolution typically 2048x2048 for optimal performance)","Text prompt in natural language or structured format"],"input_types":["image (JPEG, PNG, WebP)","text (natural language prompt, structured queries)"],"output_types":["text (natural language response, descriptions, analysis)","structured data (JSON-formatted extractions when prompted)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_1","uri":"capability://image.visual.long.horizon.visual.context.retention.with.extended.token.sequences","name":"long-horizon visual context retention with extended token sequences","description":"Maintains coherent understanding across extended image sequences and long text-image interleaving through optimized attention mechanisms and efficient token management. The model can process multiple images or long documents with embedded visuals while preserving context about earlier images and maintaining reasoning chains across the full sequence, enabling multi-page document analysis and image series understanding.","intents":["I need to analyze a multi-page document with images, tables, and text scattered throughout","I want to compare visual elements across multiple images in a single conversation","I need to track changes or relationships across a sequence of related images"],"best_for":["document processing pipelines handling PDFs with mixed content","visual comparison and diff analysis tools","multi-image narrative understanding applications"],"limitations":["Token budget constraints limit total sequence length (typically 8K-32K tokens depending on deployment)","Attention computation scales quadratically with sequence length, causing latency increases for very long documents","Context retention degrades when images are separated by large blocks of text without explicit linking prompts","No built-in mechanism for hierarchical summarization of earlier images when context window fills"],"requires":["API endpoint supporting batch image uploads or sequential image processing","Sufficient token budget allocation for full document context","Structured prompting to maintain cross-image reference clarity"],"input_types":["image (multiple, in sequence or batch)","text (long-form prompts with image references)"],"output_types":["text (coherent analysis spanning all images)","structured comparisons (JSON, markdown tables)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_2","uri":"capability://image.visual.fine.grained.visual.element.localization.and.spatial.reasoning","name":"fine-grained visual element localization and spatial reasoning","description":"Identifies and reasons about specific regions, objects, and spatial relationships within images by mapping visual features to precise pixel coordinates or bounding box representations. The model can locate text, objects, and visual elements in response to queries and understand spatial relationships (containment, adjacency, relative positioning) without requiring external object detection models, enabling end-to-end visual understanding.","intents":["I need to find where specific text or objects appear in an image and get their locations","I want to understand the spatial layout of elements in a diagram, screenshot, or UI mockup","I need to answer questions about relative positions and relationships between visual elements"],"best_for":["UI/UX analysis and accessibility testing tools","OCR and document layout analysis systems","visual search and object localization applications"],"limitations":["Localization accuracy degrades on small objects or densely packed layouts (typical error margin ±5-10% of image dimensions)","No explicit bounding box output format — requires parsing natural language descriptions or structured prompting","Performance limited on images with extreme clutter or overlapping elements","Spatial reasoning is 2D only; no depth perception or 3D spatial understanding"],"requires":["Images with clear visual elements and reasonable resolution (minimum 512x512 recommended)","Explicit spatial reasoning prompts to trigger localization behavior","Post-processing logic if structured coordinate output is needed"],"input_types":["image (with visual elements to locate)","text (queries asking about locations, positions, or spatial relationships)"],"output_types":["text (natural language descriptions with spatial references)","structured data (coordinates, bounding boxes when explicitly prompted)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_3","uri":"capability://image.visual.video.frame.analysis.and.temporal.visual.understanding","name":"video frame analysis and temporal visual understanding","description":"Processes video content by analyzing key frames or frame sequences to understand temporal relationships, motion, scene changes, and narrative progression. The model can answer questions about what happens in a video, identify key moments, and reason about causality and sequence across frames, enabling video summarization and temporal reasoning without requiring explicit video encoding.","intents":["I need to understand what happens in a video and summarize key events","I want to identify specific moments or scenes in a video based on descriptions","I need to analyze changes or motion patterns across video frames"],"best_for":["video content analysis and summarization platforms","video search and retrieval systems","automated video captioning and description generation"],"limitations":["Frame-level analysis means temporal resolution is limited to sampled keyframes; fine-grained motion details are lost","No native video codec support — requires external frame extraction (ffmpeg or similar)","Temporal reasoning is implicit and may miss subtle causality or timing relationships","High token cost for long videos due to per-frame processing","No support for audio analysis; visual-only understanding"],"requires":["Video file in common format (MP4, WebM, MOV, etc.)","Frame extraction tool (ffmpeg, OpenCV) to convert video to image sequences","Sufficient API quota for processing multiple frames per video"],"input_types":["image (video frames, typically 5-30 frames per video)","text (questions about video content, events, or temporal relationships)"],"output_types":["text (video summaries, event descriptions, temporal analysis)","structured data (scene timestamps, event lists when prompted)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_4","uri":"capability://image.visual.instruction.following.visual.task.execution.with.structured.output","name":"instruction-following visual task execution with structured output","description":"Executes complex visual tasks specified through natural language instructions by decomposing requests into reasoning steps and producing structured outputs (JSON, markdown, code) that match specified formats. The model interprets task descriptions, applies visual understanding to images, and formats responses according to user-specified schemas or output requirements, enabling programmatic integration with downstream systems.","intents":["I need to extract structured data from images (tables, forms, lists) in a specific JSON format","I want to generate code or configuration based on visual designs or diagrams","I need to classify or categorize images according to custom criteria and output results in a structured format"],"best_for":["data extraction pipelines from unstructured visual sources","automated testing and QA systems that validate visual outputs","code generation from design mockups or wireframes"],"limitations":["Output format compliance is probabilistic — no guaranteed schema validation without post-processing","Complex nested structures or deeply hierarchical data extraction may have accuracy degradation","Instruction following quality depends heavily on prompt clarity and specificity","No built-in error handling or validation; requires external schema enforcement"],"requires":["Clear, structured task instructions in natural language","Example outputs or schema specifications for desired format","Post-processing logic to validate and correct structured outputs"],"input_types":["image (visual content to analyze)","text (task instructions, format specifications, examples)"],"output_types":["text (natural language responses)","structured data (JSON, YAML, CSV, markdown tables, code)"],"categories":["image-visual","data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_5","uri":"capability://image.visual.multilingual.visual.content.understanding.and.cross.lingual.reasoning","name":"multilingual visual content understanding and cross-lingual reasoning","description":"Processes images containing text in multiple languages and reasons across linguistic boundaries, enabling understanding of multilingual documents, international content, and cross-lingual visual analysis. The model can read text in various scripts (Latin, CJK, Arabic, Devanagari, etc.), translate visual content, and reason about meaning across language barriers within a single inference pass.","intents":["I need to understand documents or images with text in languages I don't read","I want to extract and translate text from images containing multiple languages","I need to analyze international content and reason across language boundaries"],"best_for":["international document processing and translation systems","multilingual content moderation and analysis platforms","global e-commerce and localization workflows"],"limitations":["OCR accuracy varies by language and script; CJK and right-to-left scripts may have higher error rates","Translation quality is dependent on model training data; less common language pairs may be lower quality","Mixing many languages in a single image may reduce overall accuracy due to token budget constraints","No explicit language identification output; requires inference from context"],"requires":["Images with readable text in supported languages","API access with sufficient token budget for multilingual processing","Language hints in prompts if disambiguation is needed"],"input_types":["image (containing text in one or multiple languages)","text (prompts in any supported language)"],"output_types":["text (responses in requested language, translations, multilingual analysis)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_6","uri":"capability://image.visual.chart.diagram.and.infographic.interpretation.with.data.extraction","name":"chart, diagram, and infographic interpretation with data extraction","description":"Analyzes visual representations of data (charts, graphs, diagrams, infographics) to extract underlying data, understand relationships, and answer analytical questions. The model interprets axes, legends, color coding, and visual encoding schemes to reconstruct structured data and provide insights about trends, comparisons, and patterns without requiring manual data entry or separate chart parsing tools.","intents":["I need to extract data from charts and graphs and convert them to tables or datasets","I want to understand trends, patterns, and relationships shown in visualizations","I need to answer specific questions about data represented in diagrams or infographics"],"best_for":["financial and business intelligence analysis systems","scientific paper analysis and data extraction","automated report generation from visual sources"],"limitations":["Accuracy on complex multi-axis charts or 3D visualizations is lower than on simple bar/line charts","Extracted data may have rounding errors or precision loss compared to source data","Unusual or non-standard chart types may not be interpreted correctly","No ability to access underlying data files; only visual interpretation is possible","Color-blind or grayscale charts may have reduced interpretability"],"requires":["Clear, readable charts or diagrams (minimum 512x512 resolution recommended)","Standard chart types or explicit descriptions of non-standard visualizations","Prompts specifying what data or insights are needed"],"input_types":["image (charts, graphs, diagrams, infographics)","text (analytical questions, data extraction requests)"],"output_types":["text (descriptions, insights, trend analysis)","structured data (CSV, JSON tables, numerical values)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_7","uri":"capability://image.visual.scene.understanding.and.contextual.visual.reasoning","name":"scene understanding and contextual visual reasoning","description":"Comprehends complex visual scenes by identifying objects, their relationships, spatial context, and implicit meaning to answer high-level questions about what is happening, why, and what might happen next. The model reasons about context, causality, and intent from visual information, enabling understanding of photographs, screenshots, and real-world scenes beyond simple object detection.","intents":["I need to understand what's happening in a photograph or screenshot and describe the context","I want to answer questions about implied relationships, emotions, or intentions in images","I need to reason about cause-and-effect or predict outcomes based on visual context"],"best_for":["image captioning and description systems","visual content moderation and safety analysis","scene understanding for robotics and autonomous systems"],"limitations":["Reasoning about implicit or abstract concepts is probabilistic and may vary across inference runs","Understanding of rare or unusual scenes may be limited by training data distribution","No explicit confidence scores for predictions; all outputs are presented as assertions","Temporal reasoning is limited to static images; no understanding of motion or causality over time","Cultural or context-specific interpretations may differ from human understanding"],"requires":["Images with sufficient visual information and reasonable clarity","Specific questions or prompts to guide reasoning","No special preprocessing required"],"input_types":["image (photographs, screenshots, scenes)","text (questions about context, relationships, or implications)"],"output_types":["text (scene descriptions, contextual analysis, reasoning explanations)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-8b-instruct__cap_8","uri":"capability://image.visual.optical.character.recognition.with.context.aware.text.understanding","name":"optical character recognition with context-aware text understanding","description":"Extracts text from images with high accuracy while maintaining understanding of context, layout, and semantic meaning. The model recognizes characters across multiple languages and scripts, preserves document structure (paragraphs, lists, tables), and understands text meaning in context rather than performing character-level extraction alone, enabling intelligent document digitization.","intents":["I need to extract all text from an image while preserving document structure and layout","I want to read and understand text in images without manual transcription","I need to extract specific text elements (titles, captions, footnotes) while understanding their roles"],"best_for":["document digitization and archival systems","form and receipt processing pipelines","accessibility tools for converting images to text"],"limitations":["Handwritten text recognition is less accurate than printed text","Very small text (< 8pt) or low-resolution images may have recognition errors","Unusual fonts or heavily stylized text may reduce accuracy","No explicit confidence scores per character or word; overall accuracy is implicit","Layout preservation requires post-processing; raw output is linear text"],"requires":["Images with readable text (minimum 300 DPI recommended for high accuracy)","Reasonable image quality and contrast","Post-processing tools if structured output (markdown, HTML) is needed"],"input_types":["image (documents, forms, signs, any text-containing images)"],"output_types":["text (extracted text, optionally with layout preservation)","structured data (markdown, JSON with layout information)"],"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 access via OpenRouter or compatible inference endpoint","Images in JPEG, PNG, or WebP format (max resolution typically 2048x2048 for optimal performance)","Text prompt in natural language or structured format","API endpoint supporting batch image uploads or sequential image processing","Sufficient token budget allocation for full document context","Structured prompting to maintain cross-image reference clarity","Images with clear visual elements and reasonable resolution (minimum 512x512 recommended)","Explicit spatial reasoning prompts to trigger localization behavior","Post-processing logic if structured coordinate output is needed","Video file in common format (MP4, WebM, MOV, etc.)"],"failure_modes":["8B parameter size limits reasoning depth on highly complex visual scenes compared to larger models","Interleaved-MRoPE adds computational overhead during inference (~15-20% vs single-modality models)","Performance degrades on images with extreme aspect ratios or very small text without preprocessing","No explicit support for 3D spatial reasoning or temporal video understanding beyond frame-level analysis","Token budget constraints limit total sequence length (typically 8K-32K tokens depending on deployment)","Attention computation scales quadratically with sequence length, causing latency increases for very long documents","Context retention degrades when images are separated by large blocks of text without explicit linking prompts","No built-in mechanism for hierarchical summarization of earlier images when context window fills","Localization accuracy degrades on small objects or densely packed layouts (typical error margin ±5-10% of image dimensions)","No explicit bounding box output format — requires parsing natural language descriptions or structured prompting","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-8b-instruct","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-vl-8b-instruct"}},"signature":"91Y1AYTYXqPUcZav5ihfkXYkFOsUqeYI/Amn2ProeXoK1QAk7QJRZF62Ws8eTiv6yx3c4vrH/dlhAEc3qOwpCA==","signedAt":"2026-06-21T07:24:57.962Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-vl-8b-instruct","artifact":"https://unfragile.ai/qwen-qwen3-vl-8b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-vl-8b-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"}}