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The model uses instruction-tuning to follow complex directives about image analysis, enabling it to answer questions, describe content, and reason about visual relationships based on user prompts. Architecture combines a vision transformer (ViT) for image tokenization with a language model decoder for grounded text generation.","intents":["I need to ask questions about image content and get detailed answers","I want to generate captions that describe what's happening in images","I need to extract specific information from images based on natural language queries","I want to analyze visual relationships and spatial reasoning in images"],"best_for":["developers building document analysis pipelines","teams creating visual Q&A systems","builders prototyping multimodal RAG applications","non-technical users via API wrappers needing image understanding"],"limitations":["11B parameter size limits reasoning depth on complex multi-step visual tasks compared to larger models like GPT-4V","No video frame processing — single image input only, requires manual frame extraction for video analysis","Context window constraints may limit ability to process very high-resolution images or multiple images in single request","Instruction-tuning optimized for English; cross-lingual visual understanding performance not documented"],"requires":["API access via OpenRouter or compatible endpoint","Image input in standard formats (JPEG, PNG, WebP, GIF)","Text prompt/instruction in natural language","Network connectivity for inference (cloud-based, no local inference option documented)"],"input_types":["image (JPEG, PNG, WebP, GIF)","text (natural language instruction/question)"],"output_types":["text (natural language response)","structured text (captions, descriptions, answers)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-11b-vision-instruct__cap_1","uri":"capability://image.visual.visual.question.answering.with.spatial.reasoning","name":"visual question answering with spatial reasoning","description":"Answers natural language questions about image content by grounding language tokens to image regions through cross-attention mechanisms between vision and language embeddings. The model learns to identify relevant visual features corresponding to question terms, then generates answers that reference spatial relationships, object properties, and scene context. Instruction-tuning enables the model to handle diverse question types (what, where, why, how many) without explicit task-specific training.","intents":["I want to ask 'what is in this image?' and get accurate descriptions","I need to count objects or identify spatial relationships in images","I want to answer 'why' questions about visual content (causality, intent)","I need to extract factual information from images via natural language queries"],"best_for":["developers building accessibility tools for visually impaired users","teams creating content moderation systems with visual context","builders implementing image-based search or retrieval systems","researchers evaluating visual reasoning capabilities in multimodal models"],"limitations":["Reasoning about abstract concepts or implicit visual meaning may be less reliable than larger models","No explicit object detection output — answers are text-only, not bounding boxes or segmentation masks","Performance degrades on images with small text, dense layouts, or unusual perspectives","Single-image context only; cannot compare or reason across multiple images in one request"],"requires":["Image input (JPEG, PNG, WebP, GIF format)","Natural language question or instruction","API endpoint access (OpenRouter or compatible)","Reasonable image quality (minimum ~100x100 pixels recommended)"],"input_types":["image (JPEG, PNG, WebP, GIF)","text (natural language question)"],"output_types":["text (natural language answer)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-11b-vision-instruct__cap_2","uri":"capability://image.visual.image.captioning.and.description.generation","name":"image captioning and description generation","description":"Generates natural language captions and detailed descriptions of image content by encoding visual features through a vision transformer, then decoding them into coherent text sequences using an instruction-tuned language model. The model learns to identify salient objects, actions, and relationships, then articulate them in grammatically correct, contextually appropriate descriptions. Supports variable-length outputs from short captions to paragraph-length descriptions based on prompt guidance.","intents":["I need to generate alt-text for images automatically","I want to create captions for social media or documentation","I need detailed descriptions of image content for accessibility","I want to generate metadata descriptions for image indexing"],"best_for":["content creators automating image metadata generation","accessibility teams generating alt-text at scale","e-commerce platforms creating product descriptions from images","digital asset management systems indexing visual content"],"limitations":["Generated captions may hallucinate details not present in images, especially for ambiguous or low-quality images","Bias toward common object categories; rare or specialized objects may be misidentified or omitted","No control over caption length or style without explicit prompt engineering","Cannot generate captions in non-English languages reliably despite multilingual training"],"requires":["Image input (JPEG, PNG, WebP, GIF)","Optional: prompt specifying caption style or length preference","API access via OpenRouter or compatible endpoint","Image resolution sufficient for object recognition (minimum ~200x200 pixels recommended)"],"input_types":["image (JPEG, PNG, WebP, GIF)","text (optional prompt for caption style)"],"output_types":["text (caption or description)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-11b-vision-instruct__cap_3","uri":"capability://image.visual.document.and.text.extraction.from.images","name":"document and text extraction from images","description":"Extracts and recognizes text content from images containing documents, signs, screenshots, or printed material by processing visual features through the vision encoder and generating structured text output. The model learns to identify text regions, recognize characters, and preserve layout information (to a limited degree) through instruction-tuning on OCR-like tasks. Handles various document types including forms, tables, receipts, and handwritten text with varying success depending on image quality and text clarity.","intents":["I need to extract text from scanned documents or photos of documents","I want to read text from screenshots or images containing code","I need to extract information from receipts, invoices, or forms","I want to recognize and extract handwritten text from images"],"best_for":["document processing pipelines for data entry automation","teams digitizing paper records or archives","mobile app developers adding document scanning features","businesses automating invoice or receipt processing"],"limitations":["Accuracy significantly degrades on low-resolution, blurry, or rotated images compared to specialized OCR engines","No structured output format for tables or forms — returns text only without layout preservation","Struggles with handwritten text, especially cursive or non-English scripts","Cannot handle multi-page documents; requires per-image processing","Performance on dense text (small font sizes) is unreliable"],"requires":["Image containing text (JPEG, PNG, WebP, GIF)","Reasonable image quality and resolution (minimum ~300 DPI equivalent recommended for OCR tasks)","API access via OpenRouter or compatible endpoint","Text in supported languages (English optimized; other languages may have lower accuracy)"],"input_types":["image (JPEG, PNG, WebP, GIF) containing text"],"output_types":["text (extracted text content)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-11b-vision-instruct__cap_4","uri":"capability://image.visual.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 by processing visual features and generating natural language assessments of image safety. The model can be prompted to classify content across multiple safety dimensions (violence, adult content, hate symbols, etc.) and provide reasoning for classifications. Leverages instruction-tuning to follow detailed safety assessment prompts without requiring fine-tuning on proprietary safety datasets.","intents":["I need to flag potentially harmful images in user-generated content","I want to classify images by safety category (violence, adult, hate speech, etc.)","I need to provide explanations for content moderation decisions","I want to audit image datasets for policy violations"],"best_for":["social media platforms moderating user-uploaded images","content platforms automating safety reviews","teams building trust and safety systems","researchers studying multimodal safety and alignment"],"limitations":["No fine-grained confidence scores — outputs are text-based assessments, not probability distributions","Moderation decisions depend heavily on prompt engineering; inconsistent prompts yield inconsistent results","May exhibit cultural bias in safety classifications due to training data imbalances","Cannot detect subtle or context-dependent harms (e.g., coordinated harassment, deepfakes)","Requires human review for high-stakes moderation decisions; not suitable as sole arbiter"],"requires":["Image input (JPEG, PNG, WebP, GIF)","Well-crafted safety assessment prompt specifying classification dimensions","API access via OpenRouter or compatible endpoint","Human review process for moderation decisions"],"input_types":["image (JPEG, PNG, WebP, GIF)"],"output_types":["text (safety assessment and reasoning)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-11b-vision-instruct__cap_5","uri":"capability://image.visual.visual.reasoning.and.scene.understanding","name":"visual reasoning and scene understanding","description":"Performs multi-step reasoning about image content by analyzing spatial relationships, object interactions, and scene context to answer complex questions or make inferences. The model processes visual features through cross-attention mechanisms that link objects and relationships, then generates reasoning chains that explain how visual elements relate to answer questions. Instruction-tuning enables the model to follow explicit reasoning prompts (e.g., 'explain step-by-step') without task-specific training.","intents":["I need to understand complex scenes with multiple objects and relationships","I want to answer 'why' or 'how' questions that require reasoning about visual content","I need to infer information not explicitly visible in images","I want to explain visual reasoning decisions to users"],"best_for":["developers building intelligent image analysis systems","teams creating educational tools that explain visual content","researchers evaluating visual reasoning in multimodal models","builders implementing explainable AI systems for image understanding"],"limitations":["Reasoning depth limited by 11B parameter scale; struggles with multi-step reasoning chains longer than 3-4 steps","May make incorrect inferences about causality or intent based on visual correlation alone","No explicit reasoning trace output — reasoning is implicit in generated text","Performance degrades on images with ambiguous or unusual compositions","Cannot access external knowledge to supplement visual reasoning"],"requires":["Image input (JPEG, PNG, WebP, GIF)","Natural language prompt requesting reasoning or explanation","API access via OpenRouter or compatible endpoint","Clear, well-composed images for reliable reasoning"],"input_types":["image (JPEG, PNG, WebP, GIF)","text (reasoning prompt or question)"],"output_types":["text (reasoning explanation or answer)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-11b-vision-instruct__cap_6","uri":"capability://image.visual.batch.image.processing.via.api.with.streaming.responses","name":"batch image processing via api with streaming responses","description":"Processes multiple images sequentially through OpenRouter API with support for streaming text responses, enabling efficient batch workflows for image analysis at scale. The API integration handles image encoding, request batching, and response streaming, allowing developers to process image collections without managing model inference directly. Supports concurrent requests within API rate limits, with streaming responses reducing perceived latency for long-form outputs.","intents":["I need to process hundreds of images for captioning or analysis","I want to stream responses for real-time image analysis applications","I need to integrate image understanding into existing API-based workflows","I want to monitor inference costs and usage across image processing jobs"],"best_for":["developers building image processing pipelines","teams integrating multimodal AI into existing applications","builders creating batch processing workflows for content analysis","non-technical users via API wrappers or no-code platforms"],"limitations":["API-only access; no local inference option for offline processing or data privacy","Rate limits and quota constraints may throttle high-volume batch processing","Streaming responses add complexity to client implementations","Network latency adds ~100-500ms overhead per request compared to local inference","No built-in retry logic or fault tolerance; requires client-side error handling"],"requires":["OpenRouter API key or compatible endpoint","HTTP client library (Python requests, Node.js fetch, etc.)","Image files in supported formats (JPEG, PNG, WebP, GIF)","Network connectivity for API calls","Handling for API rate limits and quota management"],"input_types":["image (JPEG, PNG, WebP, GIF) via API","text (prompts/instructions)"],"output_types":["text (streaming or buffered responses)"],"categories":["image-visual","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or compatible endpoint","Image input in standard formats (JPEG, PNG, WebP, GIF)","Text prompt/instruction in natural language","Network connectivity for inference (cloud-based, no local inference option documented)","Image input (JPEG, PNG, WebP, GIF format)","Natural language question or instruction","API endpoint access (OpenRouter or compatible)","Reasonable image quality (minimum ~100x100 pixels recommended)","Image input (JPEG, PNG, WebP, GIF)","Optional: prompt specifying caption style or length preference"],"failure_modes":["11B parameter size limits reasoning depth on complex multi-step visual tasks compared to larger models like GPT-4V","No video frame processing — single image input only, requires manual frame extraction for video analysis","Context window constraints may limit ability to process very high-resolution images or multiple images in single request","Instruction-tuning optimized for English; cross-lingual visual understanding performance not documented","Reasoning about abstract concepts or implicit visual meaning may be less reliable than larger models","No explicit object detection output — answers are text-only, not bounding boxes or segmentation masks","Performance degrades on images with small text, dense layouts, or unusual perspectives","Single-image context only; cannot compare or reason across multiple images in one request","Generated captions may hallucinate details not present in images, especially for ambiguous or low-quality images","Bias toward common object categories; rare or specialized objects may be misidentified or omitted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"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":"2026-05-24T12:16:24.484Z","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=meta-llama-llama-3.2-11b-vision-instruct","compare_url":"https://unfragile.ai/compare?artifact=meta-llama-llama-3.2-11b-vision-instruct"}},"signature":"E/qmsnnADfyVXFPZZptDZwoowLO8r6J0M5rmL+YR64g2+G5ppszpPyzYGlQzPONKIgF0vLee9oS3uD+flK6CDw==","signedAt":"2026-06-20T16:13:30.557Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama-llama-3.2-11b-vision-instruct","artifact":"https://unfragile.ai/meta-llama-llama-3.2-11b-vision-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama-llama-3.2-11b-vision-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"}}