{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking","slug":"qwen-qwen3-vl-30b-a3b-thinking","name":"Qwen: Qwen3 VL 30B A3B Thinking","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-vl-30b-a3b-thinking","page_url":"https://unfragile.ai/qwen-qwen3-vl-30b-a3b-thinking","categories":["image-generation"],"tags":["qwen","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.30e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_0","uri":"capability://image.visual.multimodal.image.and.video.understanding.with.visual.reasoning","name":"multimodal image and video understanding with visual reasoning","description":"Processes images and video frames through a unified vision-language architecture that jointly encodes visual and textual information, enabling pixel-level understanding of visual content alongside semantic reasoning. The model uses a transformer-based visual encoder that maps image regions to token embeddings compatible with the language model's token space, allowing seamless interleaving of visual and textual reasoning in a single forward pass.","intents":["I need to analyze images and describe what's happening in them with detailed context","I want to answer questions about visual content in images or video frames","I need to extract structured information from images (OCR, object detection, scene understanding)","I want to perform visual reasoning tasks like comparing objects, understanding spatial relationships, or inferring intent from visual context"],"best_for":["Computer vision engineers building multimodal applications","Document processing teams handling mixed text-image workflows","AI product teams needing vision capabilities without separate vision models"],"limitations":["Video processing limited to frame-by-frame analysis without temporal coherence modeling across frames","Image resolution constraints may impact fine-grained detail extraction in high-resolution documents","No real-time streaming video support — requires pre-extracted frames or batch processing"],"requires":["API access via OpenRouter or direct Qwen endpoint","Images in JPEG, PNG, WebP, or GIF format","Video frames pre-extracted and passed as individual image inputs"],"input_types":["image (JPEG, PNG, WebP, GIF)","text (natural language queries or instructions)","video frames (as sequential image inputs)"],"output_types":["text (descriptions, answers, analysis)","structured data (JSON with extracted entities)"],"categories":["image-visual","multimodal-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_1","uri":"capability://planning.reasoning.extended.reasoning.with.chain.of.thought.for.complex.visual.tasks","name":"extended reasoning with chain-of-thought for complex visual tasks","description":"The 'Thinking' variant implements an internal reasoning mechanism that generates intermediate reasoning steps before producing final outputs, particularly for STEM, mathematics, and logic-heavy visual analysis tasks. This approach uses a hidden reasoning token stream that explores multiple solution paths and validates hypotheses before committing to an answer, similar to process-based reward models but integrated into the forward pass.","intents":["I need to solve math problems that involve diagrams, charts, or visual equations","I want detailed step-by-step reasoning for why the model arrived at a particular visual interpretation","I need to analyze complex STEM diagrams (physics, chemistry, biology) with rigorous reasoning","I want to verify visual logic puzzles or spatial reasoning tasks with transparent intermediate steps"],"best_for":["Educational technology platforms requiring explainable visual reasoning","STEM tutoring systems that need to show work for visual problem-solving","Research teams validating model reasoning on complex visual tasks"],"limitations":["Extended reasoning increases latency by 2-5x compared to standard inference","Reasoning tokens are not exposed to users — only final output is returned","Reasoning depth is fixed by model training; cannot be dynamically adjusted per query","May over-reason on simple visual tasks, adding unnecessary computational cost"],"requires":["API access to Qwen3-VL-30B-A3B-Thinking variant (not standard model)","Tolerance for higher latency (typically 5-15 seconds for complex visual reasoning)","Understanding that reasoning is internal and not user-visible"],"input_types":["image (with embedded diagrams, equations, charts)","text (natural language problem statement or query)"],"output_types":["text (final answer with optional explanation)","structured reasoning trace (if exposed via API)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_10","uri":"capability://safety.moderation.visual.content.moderation.and.safety.classification","name":"visual content moderation and safety classification","description":"Analyzes images to identify potentially harmful, inappropriate, or policy-violating content including violence, explicit material, hate symbols, or other sensitive content. The model uses visual understanding to classify content safety and can generate explanations for why content may be flagged. It integrates safety classification into the visual reasoning pipeline without requiring separate moderation models.","intents":["I need to automatically flag inappropriate images in user-generated content","I want to classify images by safety level for content moderation","I need to identify specific types of harmful content (violence, explicit, etc.)","I want to generate explanations for content moderation decisions"],"best_for":["Content moderation platforms handling user-generated images","Social media companies filtering harmful content","Child safety systems identifying inappropriate content"],"limitations":["Moderation decisions may have false positives or false negatives depending on training data","Cultural context may not be understood correctly, leading to incorrect classifications","No ability to understand intent or context beyond visual content","Moderation thresholds are fixed by model training; cannot be customized per use case","May struggle with borderline or ambiguous content"],"requires":["Image input","Understanding that moderation is probabilistic and may require human review"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["text (safety classification and explanation)","structured data (safety scores or category labels)"],"categories":["safety-moderation","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_2","uri":"capability://image.visual.dense.visual.captioning.and.scene.description.generation","name":"dense visual captioning and scene description generation","description":"Generates detailed, contextually-aware natural language descriptions of images and video frames by analyzing spatial relationships, object hierarchies, and semantic context. The model produces captions that go beyond simple object lists to include actions, relationships, and inferred intent, using attention mechanisms that weight different image regions based on semantic importance rather than just salience.","intents":["I need to generate alt-text or captions for images in accessibility workflows","I want to create detailed descriptions of scenes for content management systems","I need to generate training data labels for computer vision models","I want to produce narrative descriptions of video content for indexing or search"],"best_for":["Accessibility teams building alt-text generation pipelines","Content management platforms requiring automated image descriptions","Data labeling teams generating training datasets for vision models"],"limitations":["Captions may hallucinate details not present in images, especially for ambiguous or low-quality images","No control over caption length or style — output is determined by model training","Struggles with very small objects or fine-grained visual details in dense scenes","Cultural or contextual biases in description may not match user intent"],"requires":["Image input in supported formats (JPEG, PNG, WebP)","Reasonable image quality (minimum ~100x100 pixels for meaningful captions)"],"input_types":["image (JPEG, PNG, WebP, GIF)","optional text prompt (e.g., 'describe this image in 2 sentences')"],"output_types":["text (natural language caption or description)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_3","uri":"capability://image.visual.visual.question.answering.with.multi.hop.reasoning","name":"visual question answering with multi-hop reasoning","description":"Answers natural language questions about images by performing multi-step visual reasoning that may require identifying multiple objects, understanding relationships, and applying commonsense knowledge. The model uses attention mechanisms to ground question tokens to relevant image regions and iteratively refines its understanding through intermediate reasoning steps before generating answers.","intents":["I want to ask questions about what's in an image and get accurate answers","I need to verify facts or relationships in images (e.g., 'Is the person wearing a hat?')","I want to perform counting or comparison tasks on visual content","I need to answer complex questions that require understanding multiple objects and their relationships"],"best_for":["Interactive AI assistants with visual understanding","Document analysis systems that need to answer questions about scanned documents","Visual search and retrieval systems with natural language interfaces"],"limitations":["Accuracy degrades on questions requiring precise counting in dense scenes (>20 objects)","Struggles with questions about text in images unless text is large and clear","May confuse similar-looking objects or misidentify relationships in cluttered scenes","No ability to reason about temporal sequences in video — only frame-by-frame analysis"],"requires":["Image input with sufficient resolution to identify relevant objects","Natural language question phrased clearly and unambiguously"],"input_types":["image (JPEG, PNG, WebP)","text (natural language question)"],"output_types":["text (answer to the question)","optional confidence or explanation"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_4","uri":"capability://image.visual.optical.character.recognition.and.text.extraction.from.images","name":"optical character recognition and text extraction from images","description":"Extracts and recognizes text from images, including handwritten text, printed documents, and text embedded in scenes. The model uses visual understanding to identify text regions and language understanding to decode characters, handling multiple languages, fonts, and orientations. It preserves spatial layout information when extracting text from structured documents like forms or tables.","intents":["I need to extract text from scanned documents or PDFs","I want to read text from images of signs, labels, or handwritten notes","I need to digitize forms or structured documents with text and layout preservation","I want to extract text from screenshots or UI elements in images"],"best_for":["Document digitization and archival systems","Form processing and data extraction pipelines","Accessibility tools that need to read text from images"],"limitations":["Accuracy on handwritten text is lower than printed text, especially for cursive writing","Struggles with very small text (<8pt) or low-contrast text","No native table structure recognition — extracts text but may not preserve table layout","Multilingual text mixing in a single image may cause confusion","Rotated or skewed text requires image preprocessing for best results"],"requires":["Image with readable text (minimum ~12pt font for reliable extraction)","Reasonable image quality and contrast"],"input_types":["image (JPEG, PNG, WebP, GIF)"],"output_types":["text (extracted text content)","structured data (JSON with bounding boxes and confidence scores if available)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_5","uri":"capability://image.visual.object.detection.and.localization.with.semantic.labels","name":"object detection and localization with semantic labels","description":"Identifies and localizes objects within images by generating semantic labels and spatial coordinates (bounding boxes or region descriptions) for detected entities. The model uses visual attention to focus on relevant objects and language generation to produce structured descriptions of their locations and properties, without requiring explicit bounding box regression layers.","intents":["I need to identify all objects in an image and their locations","I want to find specific objects in images and get their positions","I need to generate training data with object labels and locations","I want to understand the spatial layout of a scene"],"best_for":["Computer vision teams building object detection datasets","Visual search systems that need to locate specific objects","Scene understanding applications requiring object inventories"],"limitations":["Bounding box accuracy is lower than specialized object detection models (e.g., YOLO, Faster R-CNN)","Struggles with small objects or objects partially occluded by other objects","No ability to output precise bounding box coordinates — only region descriptions","May miss objects in cluttered scenes or confuse similar-looking objects","Localization is text-based (e.g., 'top-left corner') rather than pixel-precise"],"requires":["Image with clearly visible objects","Acceptance that localization is approximate rather than pixel-perfect"],"input_types":["image (JPEG, PNG, WebP)","optional text prompt specifying which objects to detect"],"output_types":["text (object labels and spatial descriptions)","structured data (JSON with object names and locations)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_6","uri":"capability://image.visual.document.understanding.and.structured.information.extraction","name":"document understanding and structured information extraction","description":"Analyzes documents (scanned PDFs, forms, invoices, receipts) to extract structured information like fields, tables, and key-value pairs. The model understands document layout, identifies sections, and extracts relevant data while preserving context about relationships between fields. It uses visual understanding of document structure combined with language understanding to map visual elements to semantic categories.","intents":["I need to extract invoice data (amount, date, vendor) from document images","I want to parse form submissions and extract field values","I need to identify and extract table data from scanned documents","I want to classify document types and extract relevant fields automatically"],"best_for":["Accounts payable automation teams processing invoices","Form processing systems handling applications or surveys","Document management platforms requiring automated data extraction"],"limitations":["Accuracy on handwritten forms is lower than printed documents","Complex table structures with merged cells may be misinterpreted","No native support for multi-page document analysis — requires page-by-page processing","Field extraction is not guaranteed to be in consistent format (e.g., dates may vary)","Requires relatively clean, well-structured documents for reliable extraction"],"requires":["Document image with reasonable quality and contrast","Structured or semi-structured document format (forms, invoices, tables)"],"input_types":["image (JPEG, PNG, WebP of document pages)"],"output_types":["structured data (JSON with extracted fields and values)","text (natural language summary of document content)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_7","uri":"capability://image.visual.image.to.text.generation.with.style.and.format.control","name":"image-to-text generation with style and format control","description":"Generates natural language text from images with optional style, format, or length constraints specified in the prompt. The model produces coherent, contextually-appropriate text that describes image content while respecting user-specified parameters like tone, length, or target audience. This uses the language model's ability to follow instructions combined with visual understanding.","intents":["I want to generate product descriptions from product photos","I need to create social media captions for images","I want to generate blog post content from images","I need to create marketing copy that matches brand voice from visual content"],"best_for":["Content creation teams generating captions and descriptions at scale","E-commerce platforms automating product description generation","Marketing teams creating social media content from visual assets"],"limitations":["Generated text may not match brand voice or style perfectly without detailed prompting","Factual accuracy is not guaranteed — model may hallucinate details or misinterpret content","Length control is approximate; output may exceed or fall short of specified word counts","Tone and style are influenced by training data biases, not always controllable via prompts"],"requires":["Image input","Optional text prompt specifying style, length, or format requirements"],"input_types":["image (JPEG, PNG, WebP)","text (optional style or format instructions)"],"output_types":["text (generated description or caption)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_8","uri":"capability://image.visual.comparative.visual.analysis.and.image.to.image.reasoning","name":"comparative visual analysis and image-to-image reasoning","description":"Analyzes multiple images together to identify similarities, differences, and relationships between visual content. The model processes multiple image inputs in a single request and generates comparative analysis, enabling tasks like before-after analysis, product comparison, or scene change detection. It uses cross-image attention mechanisms to ground comparisons in specific visual elements.","intents":["I need to compare two product images and identify differences","I want to analyze before-and-after images and describe changes","I need to detect changes in scenes across multiple images","I want to compare visual styles or compositions across images"],"best_for":["Quality assurance teams comparing product images","Change detection systems monitoring visual content","Comparative analysis tools for product or design review"],"limitations":["Accuracy depends on image similarity — very different images may not produce meaningful comparisons","No pixel-level change detection — only semantic-level differences are identified","Limited to 2-3 images per request (API-dependent); cannot analyze large image sequences","May miss subtle differences or focus on irrelevant variations"],"requires":["Multiple images (typically 2-3) for comparison","Images should be related or comparable for meaningful analysis"],"input_types":["image (multiple JPEG, PNG, or WebP images)"],"output_types":["text (comparative analysis and identified differences)","structured data (JSON with difference categories)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-30b-a3b-thinking__cap_9","uri":"capability://image.visual.video.frame.analysis.and.temporal.scene.understanding","name":"video frame analysis and temporal scene understanding","description":"Analyzes video content by processing individual frames and generating descriptions or answers about video scenes. While the model processes frames independently, it can be prompted to reason about temporal sequences when frames are provided in order, enabling basic temporal understanding. The model uses frame-by-frame visual understanding combined with language understanding to describe video content and answer questions about what happens in videos.","intents":["I need to generate descriptions of video content for indexing or search","I want to answer questions about what happens in specific video frames","I need to extract key moments or scenes from videos","I want to analyze surveillance or monitoring video for events"],"best_for":["Video indexing and search platforms","Content moderation systems analyzing video frames","Surveillance analysis systems identifying events"],"limitations":["No native temporal modeling — requires manual frame extraction and sequential prompting","Cannot understand motion or temporal relationships without explicit frame ordering","Struggles with fast-moving content or motion blur","No ability to track objects across frames or understand temporal causality","Requires pre-extracted frames; no native video file support"],"requires":["Video frames extracted as individual images (JPEG, PNG, WebP)","Frames provided in chronological order for temporal reasoning"],"input_types":["image (extracted video frames in sequence)"],"output_types":["text (frame descriptions or answers about video content)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or direct Qwen endpoint","Images in JPEG, PNG, WebP, or GIF format","Video frames pre-extracted and passed as individual image inputs","API access to Qwen3-VL-30B-A3B-Thinking variant (not standard model)","Tolerance for higher latency (typically 5-15 seconds for complex visual reasoning)","Understanding that reasoning is internal and not user-visible","Image input","Understanding that moderation is probabilistic and may require human review","Image input in supported formats (JPEG, PNG, WebP)","Reasonable image quality (minimum ~100x100 pixels for meaningful captions)"],"failure_modes":["Video processing limited to frame-by-frame analysis without temporal coherence modeling across frames","Image resolution constraints may impact fine-grained detail extraction in high-resolution documents","No real-time streaming video support — requires pre-extracted frames or batch processing","Extended reasoning increases latency by 2-5x compared to standard inference","Reasoning tokens are not exposed to users — only final output is returned","Reasoning depth is fixed by model training; cannot be dynamically adjusted per query","May over-reason on simple visual tasks, adding unnecessary computational cost","Moderation decisions may have false positives or false negatives depending on training data","Cultural context may not be understood correctly, leading to incorrect classifications","No ability to understand intent or context beyond visual content","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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-30b-a3b-thinking","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-vl-30b-a3b-thinking"}},"signature":"Va9unP4jClqIBug3x9jjjtY3XZyGxujBe8w4RPZw1VCufKcroFKt9e6KwXg3Ynume+8a/JsW6zIkidWesQoODg==","signedAt":"2026-06-20T19:22:57.252Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-vl-30b-a3b-thinking","artifact":"https://unfragile.ai/qwen-qwen3-vl-30b-a3b-thinking","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-vl-30b-a3b-thinking","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"}}