{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking","slug":"qwen-qwen3-vl-235b-a22b-thinking","name":"Qwen: Qwen3 VL 235B A22B Thinking","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-vl-235b-a22b-thinking","page_url":"https://unfragile.ai/qwen-qwen3-vl-235b-a22b-thinking","categories":["image-generation"],"tags":["qwen","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.60e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_0","uri":"capability://planning.reasoning.multimodal.reasoning.with.extended.thinking.for.stem.and.mathematical.problem.solving","name":"multimodal reasoning with extended thinking for stem and mathematical problem-solving","description":"Implements a chain-of-thought reasoning architecture that processes both text and visual inputs (images, video frames) through a unified transformer backbone, with extended thinking tokens that allow the model to perform step-by-step mathematical derivations and logical decomposition before generating final answers. The thinking mechanism operates as an intermediate representation layer that reasons over visual and textual context simultaneously, enabling structured problem-solving in domains requiring symbolic manipulation and proof generation.","intents":["I need to solve complex math problems that require step-by-step reasoning with visual diagrams or graphs","I want to analyze scientific papers with embedded figures and derive conclusions from both text and visual evidence","I need to verify mathematical proofs by having the model show its reasoning chain across visual and textual information","I want to debug visual code (e.g., flowcharts, architecture diagrams) by having the model reason about structure and logic"],"best_for":["researchers and educators building STEM tutoring systems","data scientists building automated scientific paper analysis pipelines","developers creating AI-powered homework assistance or exam preparation tools","teams building visual reasoning systems for engineering and architecture domains"],"limitations":["Extended thinking adds latency (typically 5-15 seconds per query) due to intermediate token generation","Thinking tokens consume additional API credits/tokens, increasing per-request cost by 3-5x vs non-thinking models","Visual reasoning quality degrades on low-resolution images (<256px) or heavily compressed video frames","No streaming support for thinking tokens — full response must be generated before output begins","Context window for video is limited to ~30 seconds of footage or ~10 key frames per request"],"requires":["OpenRouter API key or direct Qwen API access","Images in JPEG/PNG/WebP format (max 10MB per image)","Video inputs as MP4/WebM (max 100MB, automatically sampled to key frames)","HTTP/2 capable client for handling extended response times","Support for processing thinking tokens in response parsing (non-standard token type)"],"input_types":["text (prompts up to 8K tokens)","image (single or multiple images per request)","video (short clips, auto-sampled to frames)","mixed multimodal sequences (text + image + text + image patterns)"],"output_types":["text (reasoning chain + final answer)","structured reasoning (step-by-step derivations with LaTeX math notation)","confidence scores for mathematical conclusions","visual annotations (bounding boxes, highlights) in structured format"],"categories":["planning-reasoning","image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_1","uri":"capability://image.visual.video.frame.understanding.with.temporal.reasoning","name":"video frame understanding with temporal reasoning","description":"Processes video inputs by automatically sampling key frames using a temporal attention mechanism that identifies semantically important moments (scene changes, object interactions, text appearance). The model maintains temporal context across frames, allowing it to reason about causality, motion, and sequence of events. Internally, frames are encoded through a vision transformer (ViT) backbone and fused with temporal positional embeddings that preserve frame ordering information.","intents":["I need to analyze a video tutorial and extract the sequence of steps being demonstrated","I want to understand what's happening in a video without watching the entire thing","I need to verify if a video contains specific visual events or objects in a particular order","I want to generate a detailed summary of a video's content with temporal awareness"],"best_for":["content creators building automated video summarization tools","accessibility teams creating video descriptions for deaf/blind users","security teams analyzing surveillance footage for event detection","educational platforms building interactive video understanding systems"],"limitations":["Automatic frame sampling may miss important details in fast-paced videos (>30 fps action sequences)","Maximum video duration is ~2 minutes; longer videos must be split into segments","Temporal reasoning is limited to ~10 key frames per request; dense temporal understanding requires multiple passes","No support for 3D video formats or stereoscopic content","Audio track is ignored; only visual content is processed"],"requires":["Video file in MP4, WebM, or MOV format","Minimum resolution 480p; 1080p or higher recommended for text-in-video extraction","File size under 100MB (larger files auto-compressed, may lose detail)","OpenRouter API key with video processing enabled"],"input_types":["video (MP4/WebM/MOV)","text (natural language query about video content)","mixed (video + specific question about temporal events)"],"output_types":["text (narrative description of video events)","structured timeline (JSON with timestamps and descriptions)","frame-level annotations (which frames contain specific objects/events)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_2","uri":"capability://image.visual.dense.visual.question.answering.with.multi.image.reasoning","name":"dense visual question-answering with multi-image reasoning","description":"Accepts multiple images in a single request and performs cross-image reasoning by building a unified visual context representation. The model can compare objects across images, track visual elements across a sequence, and answer questions that require synthesizing information from multiple visual sources. Internally, images are encoded through a shared vision backbone and their representations are fused through cross-attention mechanisms that allow the model to identify correspondences and relationships between images.","intents":["I need to compare two versions of a design and identify the differences","I want to analyze a sequence of photos and describe how a scene changed over time","I need to verify that objects in multiple images are the same (e.g., product verification across listings)","I want to extract information from a multi-page document by providing images of each page"],"best_for":["e-commerce platforms building visual product verification systems","document processing teams handling multi-page form extraction","design teams building automated design review and comparison tools","quality assurance teams verifying visual consistency across product variants"],"limitations":["Maximum 10 images per request; larger batches require multiple API calls","Cross-image reasoning quality degrades when images have very different resolutions or aspect ratios","No built-in image ordering; if sequence matters, must be specified in the prompt","Memory consumption scales linearly with image count; 10 high-res images may cause timeout on slower connections","No support for animated GIFs or image sequences with transparency"],"requires":["Multiple images in JPEG/PNG/WebP format","Each image under 10MB; total batch under 50MB","Images should be at least 256x256 pixels for meaningful analysis","OpenRouter API key with multimodal support"],"input_types":["multiple images (2-10 per request)","text (question or instruction referencing multiple images)","mixed (images + comparative or sequential prompts)"],"output_types":["text (comparative analysis, differences, similarities)","structured data (JSON with per-image annotations)","extracted information (text from documents, object lists)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_3","uri":"capability://image.visual.optical.character.recognition.with.mathematical.notation.and.diagram.understanding","name":"optical character recognition with mathematical notation and diagram understanding","description":"Extracts text from images with specialized handling for mathematical notation (LaTeX, handwritten equations), scientific diagrams, and technical drawings. The model uses a hybrid approach combining traditional OCR-style character recognition with semantic understanding of mathematical symbols and spatial relationships. Handwritten content is recognized through a dedicated handwriting recognition module trained on mathematical notation, and spatial relationships between symbols are preserved to maintain equation structure.","intents":["I need to digitize handwritten math homework or exam papers","I want to extract equations from scientific papers or textbooks as LaTeX","I need to read text from technical diagrams and preserve the spatial layout","I want to convert a whiteboard photo into structured text and equations"],"best_for":["educational technology companies building homework digitization tools","research teams automating scientific paper processing","accessibility teams converting visual math content to accessible formats","document management systems handling technical and scientific documents"],"limitations":["Handwriting recognition accuracy drops below 85% for cursive or heavily stylized writing","Mathematical notation recognition requires clear, well-formed symbols; ambiguous or overlapping equations may be misinterpreted","Spatial layout preservation is approximate; complex multi-column layouts may be flattened","No support for color-coded diagrams or diagrams relying on color for semantic meaning","Handwritten subscripts and superscripts are sometimes misplaced in output"],"requires":["Image with clear, legible text (minimum 72 DPI)","For handwriting: pen/pencil on white or light background","For equations: standard mathematical notation or LaTeX-compatible symbols","OpenRouter API key"],"input_types":["image (photo of handwritten content, printed text, or diagrams)","text (optional: context about expected notation type, e.g., 'chemistry equations' or 'calculus')"],"output_types":["text (extracted text with formatting preserved)","LaTeX (mathematical equations in LaTeX format)","structured data (JSON with text regions, equations, and spatial coordinates)","markdown (formatted text with embedded LaTeX)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_4","uri":"capability://safety.moderation.visual.content.moderation.and.safety.classification","name":"visual content moderation and safety classification","description":"Analyzes images and video frames to detect and classify potentially harmful, inappropriate, or policy-violating content. The model uses a multi-label classification approach that identifies specific categories of concern (violence, explicit content, hate symbols, misinformation indicators) with confidence scores. The classification operates through a dedicated safety classifier head trained on moderation datasets, separate from the main vision-language backbone, allowing it to make moderation decisions without generating descriptive text about harmful content.","intents":["I need to automatically filter user-uploaded images on my platform for policy violations","I want to identify potentially violent or explicit content in video streams in real-time","I need to flag images containing hate symbols or extremist indicators for human review","I want to detect deepfakes or manipulated media that could spread misinformation"],"best_for":["social media platforms and content moderation teams","e-commerce platforms screening user-generated content","streaming services implementing content filtering","research teams studying online harms and misinformation"],"limitations":["Moderation decisions are probabilistic; confidence scores below 0.7 should trigger human review","False positive rate is ~5-8% on borderline content (e.g., artistic nudity, violence in historical context)","Cultural context is limited; symbols or gestures with different meanings across cultures may be misclassified","No support for detecting subtle manipulation or deepfakes with high confidence (>90%)","Real-time video moderation requires frame sampling, potentially missing brief violations"],"requires":["Image or video in standard formats (JPEG/PNG/MP4)","OpenRouter API key with safety classification enabled","Human review workflow for confidence scores between 0.5-0.8"],"input_types":["image (single or batch)","video (short clips, auto-sampled to frames)"],"output_types":["structured data (JSON with violation categories and confidence scores)","boolean (pass/fail moderation decision)","risk level (low/medium/high with explanation)"],"categories":["safety-moderation","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_5","uri":"capability://data.processing.analysis.structured.data.extraction.from.visual.documents.with.schema.validation","name":"structured data extraction from visual documents with schema validation","description":"Extracts structured information from images (forms, invoices, tables, receipts) and validates the output against a provided JSON schema. The model uses a schema-aware extraction approach where the schema is embedded in the prompt context, guiding the model to extract only relevant fields and format them according to specification. The extraction process involves visual understanding of document layout, text recognition, and semantic mapping of visual elements to schema fields, with built-in validation that flags missing or invalid fields.","intents":["I need to extract invoice data (vendor, amount, date, line items) from photos and validate against my accounting schema","I want to process application forms and extract structured data into a database","I need to extract table data from documents and convert to CSV or JSON","I want to validate that extracted data matches expected types and constraints"],"best_for":["document processing and RPA teams automating data entry","financial services automating invoice and receipt processing","healthcare organizations extracting patient information from forms","logistics companies processing shipping documents and manifests"],"limitations":["Schema validation is performed by the model; complex constraints (e.g., cross-field validation) require post-processing","Extraction accuracy depends on document quality; poor scans or handwriting reduce accuracy to 70-80%","No support for documents with non-standard layouts or custom form designs without examples","Field extraction may fail silently if a field is not visually present; no automatic fallback to defaults","Large schemas (>50 fields) may cause the model to miss fields due to context window limitations"],"requires":["Document image in JPEG/PNG/WebP format","JSON schema defining expected fields, types, and constraints","OpenRouter API key","Post-processing logic for handling validation failures"],"input_types":["image (document photo or scan)","JSON schema (field definitions and constraints)","text (optional: instructions for ambiguous fields)"],"output_types":["JSON (extracted data matching schema)","validation report (missing fields, type mismatches, constraint violations)","confidence scores (per-field extraction confidence)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_6","uri":"capability://code.generation.editing.image.to.code.generation.with.visual.layout.understanding","name":"image-to-code generation with visual layout understanding","description":"Converts images of user interfaces, wireframes, or design mockups into functional code (HTML/CSS, React, Vue, or other frameworks). The model analyzes the visual layout, component hierarchy, and styling to generate code that reproduces the design. The process involves visual understanding of spatial relationships, color extraction, typography analysis, and semantic identification of UI components (buttons, forms, cards, etc.), followed by code generation that respects the visual hierarchy and responsive design principles.","intents":["I need to convert a Figma screenshot into React components quickly","I want to generate HTML/CSS from a hand-drawn wireframe","I need to prototype a design by converting a mockup image to working code","I want to reverse-engineer the structure of a competitor's UI from a screenshot"],"best_for":["frontend developers and designers prototyping UI quickly","no-code/low-code platforms automating design-to-code workflows","design systems teams generating component code from mockups","teams migrating designs from one framework to another"],"limitations":["Generated code is a starting point; complex interactions and animations require manual refinement","Responsive design is approximated; mobile/tablet layouts may not be pixel-perfect","No support for complex state management or backend integration logic","Color extraction may be inaccurate on gradients or complex backgrounds","Component identification is limited to standard UI patterns; custom or unusual components may be misinterpreted","Generated code may not follow best practices for accessibility (ARIA labels, semantic HTML)"],"requires":["Image of UI design (screenshot, mockup, or wireframe)","Target framework specified (React, Vue, HTML/CSS, etc.)","OpenRouter API key","Manual review and refinement of generated code"],"input_types":["image (UI screenshot, design mockup, or wireframe)","text (target framework, specific requirements)"],"output_types":["code (HTML/CSS, JSX, Vue templates, etc.)","component tree (JSON representation of component hierarchy)","styling (CSS or framework-specific styling code)"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_7","uri":"capability://image.visual.real.time.visual.anomaly.detection.with.contextual.explanation","name":"real-time visual anomaly detection with contextual explanation","description":"Analyzes images or video streams to identify visual anomalies (defects, unusual patterns, out-of-place objects) and provides contextual explanations for why something is anomalous. The model uses a combination of visual feature extraction and reasoning to compare observed content against learned patterns of normality, then generates natural language explanations of detected anomalies. The approach involves implicit anomaly scoring (learned through contrastive training on normal vs. anomalous examples) and explicit reasoning about why something deviates from expected patterns.","intents":["I need to detect manufacturing defects in product images automatically","I want to identify unusual patterns in security camera footage that warrant investigation","I need to flag quality issues in food or pharmaceutical production","I want to detect structural damage or wear in infrastructure inspection images"],"best_for":["manufacturing and quality assurance teams automating defect detection","security operations centers monitoring surveillance feeds","infrastructure inspection companies automating damage assessment","medical imaging teams flagging unusual findings for radiologist review"],"limitations":["Anomaly detection is domain-specific; a model trained on manufacturing defects won't generalize to medical anomalies","Requires baseline examples of 'normal' content to establish what constitutes an anomaly","False positive rate increases in novel or rare scenarios not represented in training data","Contextual explanations may be generic or unhelpful for subtle anomalies","Real-time processing on video streams requires frame sampling, potentially missing brief anomalies","No support for detecting anomalies that are statistically rare but visually similar to normal content"],"requires":["Image or video in standard formats","Domain context (e.g., 'manufacturing defects in circuit boards' or 'security anomalies in retail')","OpenRouter API key","Optional: baseline examples of normal content for context"],"input_types":["image (single or batch)","video (short clips)","text (domain context, anomaly type to detect)"],"output_types":["structured data (JSON with anomaly location, type, and confidence score)","text (natural language explanation of detected anomaly)","visual annotations (bounding boxes or heatmaps highlighting anomalies)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-vl-235b-a22b-thinking__cap_8","uri":"capability://search.retrieval.cross.modal.semantic.search.with.image.and.text.queries","name":"cross-modal semantic search with image and text queries","description":"Enables searching for images using natural language queries or finding similar images using image queries. The model uses a shared embedding space where images and text are encoded into comparable vector representations, allowing semantic matching across modalities. Internally, images are encoded through a vision transformer and text through a language model, with both projections aligned to a common embedding space through contrastive learning. Similarity is computed as cosine distance in this shared space, enabling flexible search across modalities.","intents":["I want to search my image library using natural language descriptions","I need to find similar product images across my e-commerce catalog","I want to build a visual search feature where users upload an image to find similar items","I need to organize images by semantic similarity rather than metadata"],"best_for":["e-commerce platforms building visual search features","digital asset management systems enabling semantic search","content creators organizing large image libraries","research teams analyzing visual datasets"],"limitations":["Semantic search quality depends on embedding space alignment; misaligned modalities reduce accuracy","Requires pre-computing embeddings for large image collections (scalability challenge for >1M images)","Cross-modal search is less precise than single-modality search; text-to-image search has ~5-10% lower accuracy than image-to-image","Embedding space is fixed; cannot adapt to domain-specific similarity notions without retraining","No support for searching by visual attributes (e.g., 'red objects' or 'outdoor scenes') without explicit text queries"],"requires":["Image collection pre-processed and embedded (one-time cost)","Vector database or similarity search index (e.g., Pinecone, Weaviate, Milvus)","OpenRouter API key for embedding generation","Text or image query for search"],"input_types":["text (natural language search query)","image (reference image for similarity search)","mixed (text + image for refined search)"],"output_types":["ranked list of similar images with similarity scores","embeddings (vector representations for custom downstream tasks)"],"categories":["search-retrieval","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key or direct Qwen API access","Images in JPEG/PNG/WebP format (max 10MB per image)","Video inputs as MP4/WebM (max 100MB, automatically sampled to key frames)","HTTP/2 capable client for handling extended response times","Support for processing thinking tokens in response parsing (non-standard token type)","Video file in MP4, WebM, or MOV format","Minimum resolution 480p; 1080p or higher recommended for text-in-video extraction","File size under 100MB (larger files auto-compressed, may lose detail)","OpenRouter API key with video processing enabled","Multiple images in JPEG/PNG/WebP format"],"failure_modes":["Extended thinking adds latency (typically 5-15 seconds per query) due to intermediate token generation","Thinking tokens consume additional API credits/tokens, increasing per-request cost by 3-5x vs non-thinking models","Visual reasoning quality degrades on low-resolution images (<256px) or heavily compressed video frames","No streaming support for thinking tokens — full response must be generated before output begins","Context window for video is limited to ~30 seconds of footage or ~10 key frames per request","Automatic frame sampling may miss important details in fast-paced videos (>30 fps action sequences)","Maximum video duration is ~2 minutes; longer videos must be split into segments","Temporal reasoning is limited to ~10 key frames per request; dense temporal understanding requires multiple passes","No support for 3D video formats or stereoscopic content","Audio track is ignored; only visual content is processed","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-235b-a22b-thinking","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-vl-235b-a22b-thinking"}},"signature":"8tVPmcsRmWFSQSRCD4wBXrkzAW4LcVK7SGRk97N51EEJ2oZypLZK25US3NwVsMaLyb33aflgYgzVxllLhyC6AQ==","signedAt":"2026-06-20T22:09:24.935Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-vl-235b-a22b-thinking","artifact":"https://unfragile.ai/qwen-qwen3-vl-235b-a22b-thinking","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-vl-235b-a22b-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"}}