{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-pixtral-large-2411","slug":"mistralai-pixtral-large-2411","name":"Mistral: Pixtral Large 2411","type":"model","url":"https://openrouter.ai/models/mistralai~pixtral-large-2411","page_url":"https://unfragile.ai/mistralai-pixtral-large-2411","categories":["image-generation"],"tags":["mistralai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-pixtral-large-2411__cap_0","uri":"capability://image.visual.multimodal.document.and.chart.understanding.with.vision.transformer.backbone","name":"multimodal document and chart understanding with vision transformer backbone","description":"Processes documents, charts, and natural images through a vision encoder integrated into a 124B parameter transformer architecture, enabling simultaneous text and image comprehension. The model uses a unified token embedding space where image patches are encoded alongside text tokens, allowing the transformer to reason across modalities in a single forward pass without separate vision-language fusion layers.","intents":["Extract structured data from scanned documents or PDFs with mixed text and images","Analyze business charts, graphs, and infographics to extract insights and trends","Answer questions about images that contain both visual elements and embedded text","Process multi-page documents with complex layouts including tables, diagrams, and photographs"],"best_for":["Enterprise document processing teams handling mixed-format inputs (PDFs, scans, charts)","Data extraction pipelines requiring simultaneous text and visual understanding","Developers building document intelligence applications without separate vision models"],"limitations":["Vision encoder resolution and patch size limit fine-grained detail extraction compared to specialized OCR models","No explicit document layout understanding — relies on learned spatial reasoning rather than explicit structure parsing","Multimodal processing adds computational overhead; slower inference than text-only models for text-only inputs","Image understanding quality degrades with very small text or complex nested diagrams"],"requires":["API access via OpenRouter or Mistral API endpoint","Image input in standard formats (JPEG, PNG, WebP, GIF)","Sufficient context window for document length (exact limit not specified in artifact)"],"input_types":["text","image (JPEG, PNG, WebP, GIF)","mixed text-image documents"],"output_types":["text","structured data (JSON-formatted extractions)","natural language descriptions"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-pixtral-large-2411__cap_1","uri":"capability://image.visual.natural.image.visual.question.answering.with.spatial.reasoning","name":"natural image visual question answering with spatial reasoning","description":"Answers natural language questions about images by performing spatial reasoning over visual features extracted by the integrated vision encoder. The model maps image regions to semantic concepts and grounds language generation in visual context, enabling questions about object relationships, scene composition, and visual attributes without requiring explicit region annotations or bounding box inputs.","intents":["Ask questions about photograph content and composition (e.g., 'What objects are in the foreground?')","Identify relationships between visual elements (e.g., 'Is the person holding the object?')","Describe visual attributes and properties (e.g., 'What color is the car?')","Perform visual reasoning tasks like counting, comparison, and scene understanding"],"best_for":["Developers building image understanding features into applications without dedicated vision APIs","Content moderation and analysis teams needing semantic image understanding","Accessibility applications requiring image-to-text conversion with reasoning"],"limitations":["Visual reasoning accuracy varies with image quality and complexity; struggles with highly abstract or artistic images","No explicit object detection or segmentation output — only natural language descriptions","Context window constraints limit ability to process multiple high-resolution images in single request","Spatial reasoning less precise than specialized object detection models for technical tasks"],"requires":["Image input in supported formats (JPEG, PNG, WebP, GIF)","Natural language question or prompt","API access to Mistral or OpenRouter endpoint"],"input_types":["image","text (natural language question)"],"output_types":["text (natural language answer)","structured descriptions"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-pixtral-large-2411__cap_2","uri":"capability://image.visual.optical.character.recognition.with.context.aware.text.extraction","name":"optical character recognition with context-aware text extraction","description":"Extracts text from images and documents using the vision encoder's ability to recognize character patterns and spatial layout, with context awareness from the 124B language model enabling correction of ambiguous characters and understanding of document structure. Unlike traditional OCR, the model understands semantic context to disambiguate similar-looking characters and infer document hierarchy from visual layout cues.","intents":["Extract text from scanned documents, photographs of documents, or screenshots","Preserve document structure and layout information during text extraction","Correct OCR errors using semantic context from surrounding text","Extract text from images with complex backgrounds or non-standard fonts"],"best_for":["Document digitization pipelines requiring semantic understanding alongside character recognition","Teams processing documents with varied quality, fonts, or layouts","Applications needing layout-aware text extraction rather than simple character recognition"],"limitations":["Slower inference than specialized OCR engines optimized for speed","No explicit bounding box or coordinate output for character positions","Struggles with very small text or heavily degraded document images","Context-aware correction may introduce hallucinations if semantic context is ambiguous"],"requires":["Image input in standard formats (JPEG, PNG, WebP, GIF)","Sufficient image resolution for text legibility (minimum ~100 DPI recommended)","API access to Mistral or OpenRouter endpoint"],"input_types":["image (document, screenshot, photograph)"],"output_types":["text","structured text with layout information","JSON-formatted extractions"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-pixtral-large-2411__cap_3","uri":"capability://image.visual.long.context.multimodal.reasoning.with.document.scale.understanding","name":"long-context multimodal reasoning with document-scale understanding","description":"Processes extended documents containing multiple images, charts, and text sections through a single model with sufficient context window to maintain coherence across document boundaries. The unified transformer architecture allows the model to reason about relationships between distant images and text sections without requiring explicit document segmentation or multi-pass processing.","intents":["Analyze multi-page reports with mixed text, charts, and images in a single request","Extract insights from documents by reasoning across multiple visual and textual elements","Compare information across different sections of a document","Summarize complex documents with both visual and textual content"],"best_for":["Enterprise document analysis teams processing reports, proposals, and technical documentation","Researchers analyzing academic papers with figures and tables","Compliance and audit teams reviewing multi-page documents"],"limitations":["Exact context window size not specified in artifact; may require document chunking for very long documents","Multimodal processing increases token consumption compared to text-only analysis","Long-context reasoning may degrade with very large documents due to attention mechanism limitations","No explicit document structure parsing — relies on learned spatial and sequential patterns"],"requires":["Document input as sequence of text and images","API access to Mistral or OpenRouter endpoint","Sufficient API quota for multimodal token consumption"],"input_types":["text","image","mixed multi-page documents"],"output_types":["text","structured analysis","summaries"],"categories":["image-visual","text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-pixtral-large-2411__cap_4","uri":"capability://image.visual.batch.multimodal.inference.with.api.based.scaling","name":"batch multimodal inference with api-based scaling","description":"Supports batch processing of multiple image-text pairs through OpenRouter's API infrastructure, enabling efficient scaling of multimodal analysis workloads. The API abstracts away model serving complexity and provides automatic batching, load balancing, and request queuing without requiring local GPU infrastructure or model deployment.","intents":["Process large datasets of images with associated questions or analysis prompts","Scale document processing pipelines without managing GPU infrastructure","Integrate multimodal analysis into existing API-based workflows","Analyze image collections with consistent prompts or analysis templates"],"best_for":["Teams without GPU infrastructure needing to process large image datasets","Startups and small companies avoiding infrastructure management overhead","Workflows already built on API-based architecture (AWS Lambda, serverless functions)"],"limitations":["API latency adds overhead compared to local inference; typical response time 2-10 seconds per request","Batch processing not explicitly documented; may require sequential requests","API rate limits and quota constraints may throttle high-volume workloads","Multimodal token pricing higher than text-only; costs scale with image resolution and quantity"],"requires":["OpenRouter API key or Mistral API credentials","Network connectivity to API endpoint","Budget for API consumption (paid service)"],"input_types":["image","text","mixed multimodal requests"],"output_types":["text","structured JSON responses"],"categories":["image-visual","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-pixtral-large-2411__cap_5","uri":"capability://image.visual.cross.modal.semantic.search.and.retrieval.with.vision.language.embeddings","name":"cross-modal semantic search and retrieval with vision-language embeddings","description":"Generates unified semantic embeddings for both images and text through the shared transformer representation space, enabling search and retrieval operations across modalities. The model can rank images by text queries or find similar images without explicit embedding extraction, leveraging the language model's understanding of visual semantics.","intents":["Search image collections using natural language queries","Find visually similar images based on reference images","Rank images by relevance to text descriptions","Build multimodal search indexes for document collections"],"best_for":["Content management systems requiring multimodal search capabilities","Image library and asset management applications","E-commerce platforms with visual search features"],"limitations":["No explicit embedding extraction API documented; requires inference-based ranking","Search quality depends on query specificity and image diversity","Ranking all images in large collections requires multiple API calls, increasing latency","No built-in vector database integration for persistent index storage"],"requires":["Image collection accessible via API","Text query or reference image","API access to Mistral or OpenRouter endpoint"],"input_types":["image","text (search query)"],"output_types":["ranked image results","relevance scores","text descriptions"],"categories":["image-visual","search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or Mistral API endpoint","Image input in standard formats (JPEG, PNG, WebP, GIF)","Sufficient context window for document length (exact limit not specified in artifact)","Image input in supported formats (JPEG, PNG, WebP, GIF)","Natural language question or prompt","API access to Mistral or OpenRouter endpoint","Sufficient image resolution for text legibility (minimum ~100 DPI recommended)","Document input as sequence of text and images","Sufficient API quota for multimodal token consumption","OpenRouter API key or Mistral API credentials"],"failure_modes":["Vision encoder resolution and patch size limit fine-grained detail extraction compared to specialized OCR models","No explicit document layout understanding — relies on learned spatial reasoning rather than explicit structure parsing","Multimodal processing adds computational overhead; slower inference than text-only models for text-only inputs","Image understanding quality degrades with very small text or complex nested diagrams","Visual reasoning accuracy varies with image quality and complexity; struggles with highly abstract or artistic images","No explicit object detection or segmentation output — only natural language descriptions","Context window constraints limit ability to process multiple high-resolution images in single request","Spatial reasoning less precise than specialized object detection models for technical tasks","Slower inference than specialized OCR engines optimized for speed","No explicit bounding box or coordinate output for character positions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"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=mistralai-pixtral-large-2411","compare_url":"https://unfragile.ai/compare?artifact=mistralai-pixtral-large-2411"}},"signature":"oIwZgY6bAZ+s1zD/ygcsER1U7WQMtPrB2VktEl/UMKOMQZ343sHzflZ+p9jFzKGzXvdadJnH19+uy6fmrB78Aw==","signedAt":"2026-06-20T08:36:16.470Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-pixtral-large-2411","artifact":"https://unfragile.ai/mistralai-pixtral-large-2411","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-pixtral-large-2411","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"}}