{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pixtral-large","slug":"pixtral-large","name":"Pixtral Large","type":"model","url":"https://mistral.ai/news/pixtral-large/","page_url":"https://unfragile.ai/pixtral-large","categories":["model-training","documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pixtral-large__cap_0","uri":"capability://image.visual.interleaved.image.text.multimodal.reasoning","name":"interleaved image-text multimodal reasoning","description":"Processes multiple images (minimum 30 high-resolution images documented to fit within 128K context) interleaved with text prompts in a single conversation, using a dedicated 1B-parameter vision encoder that tokenizes visual input alongside text tokens. The architecture maintains Mistral Large 2's text foundation while extending the attention mechanism to handle mixed modality sequences, enabling coherent reasoning across image-text pairs without requiring separate API calls per image.","intents":["I need to ask questions about multiple documents or screenshots in a single conversation without losing context","I want to reference different images at different points in a multi-turn dialogue","I need to compare visual content across several images while maintaining conversation history"],"best_for":["developers building document analysis workflows with multiple PDFs or screenshots","teams analyzing comparative visual data (charts, designs, screenshots) in single sessions","researchers working with multimodal datasets requiring sequential image reasoning"],"limitations":["128K context window is shared between images and text — 30 high-resolution images represents minimum capacity, not maximum; actual throughput depends on image resolution and text length","Vision encoder is 1B parameters with unknown resolution/detail limits; may struggle with extremely fine-grained visual details compared to larger dedicated vision models","Model is deprecated as of announcement date; no active maintenance or updates to vision capabilities"],"requires":["API access via Mistral API endpoint (pixtral-large-latest) or self-hosted deployment with sufficient GPU VRAM (exact requirements unknown)","Images in supported formats (specific formats not documented)","Mistral Commercial License for production use; Mistral Research License for research"],"input_types":["text prompts","multiple images (interleaved with text)","conversation history"],"output_types":["text responses","reasoning explanations"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_1","uri":"capability://image.visual.document.visual.question.answering.docvqa","name":"document visual question answering (docvqa)","description":"Analyzes scanned documents, PDFs, and forms by extracting text and visual layout information through the vision encoder, then answering natural language questions about document content, structure, and relationships. The model combines OCR-level text extraction with spatial reasoning about document layout, enabling it to locate and reason about specific information within complex multi-page or multi-section documents.","intents":["I need to extract specific information from a PDF form or invoice without manual parsing","I want to ask questions about document content and get answers that reference specific sections or pages","I need to validate document structure and completeness programmatically"],"best_for":["document processing teams automating invoice/receipt/form extraction","legal/compliance teams analyzing contracts and regulatory documents","data entry automation reducing manual document review"],"limitations":["Performance on DocVQA benchmark is not quantified in available documentation; only stated as 'surpasses GPT-4o and Gemini-1.5 Pro' without specific accuracy metrics","Multi-page document handling limited by 128K context window; very long documents may require chunking or page selection","Vision encoder resolution limits unknown; may struggle with small fonts or low-quality scans"],"requires":["Document images or PDF pages converted to image format (conversion tool not provided)","Mistral API access or self-hosted deployment","Mistral Commercial License for production document processing"],"input_types":["document images","scanned PDFs","form images","natural language questions about document content"],"output_types":["text answers","extracted field values","document validation results"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_10","uri":"capability://image.visual.multilingual.document.processing.and.analysis","name":"multilingual document processing and analysis","description":"Processes documents and images containing text in multiple languages, with demonstrated support for Swiss German and French. Vision encoder extracts text regardless of language, and language decoder applies multilingual understanding to answer questions and extract information. Specific language support list not documented, but multilingual OCR capability confirmed through receipt processing examples.","intents":["I need to process documents in languages other than English","I want to extract information from multilingual receipts or invoices","I need to analyze documents with mixed-language content"],"best_for":["international businesses processing documents in multiple languages","multinational teams analyzing documents from different regions","organizations with multilingual customer bases"],"limitations":["Specific language support list not provided — unclear which languages are supported","Performance on low-resource languages not documented","Language detection mechanism not documented","No examples beyond Swiss German and French","Potential bias toward high-resource languages (English, French, German) not addressed"],"requires":["Document in supported language (full list unknown)","API access via Mistral API or self-hosted deployment"],"input_types":["image (document in any supported language)","text (query in any supported language)"],"output_types":["text (response in language of query or document)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_2","uri":"capability://image.visual.chart.and.data.visualization.analysis","name":"chart and data visualization analysis","description":"Interprets charts, graphs, tables, and other data visualizations by analyzing visual elements (axes, legends, data points, trends) and answering questions about data relationships, trends, and specific values. The vision encoder extracts visual structure while the language model reasons about the underlying data semantics, enabling both factual queries ('what is the value at X') and analytical questions ('what trend does this show').","intents":["I need to extract data points and trends from charts without manual transcription","I want to ask analytical questions about chart data (comparisons, growth rates, anomalies)","I need to validate chart accuracy or identify data inconsistencies programmatically"],"best_for":["business intelligence teams automating chart analysis from reports and dashboards","financial analysts extracting data from earnings reports and market charts","data teams validating visualization accuracy in automated reporting pipelines"],"limitations":["ChartQA benchmark performance not quantified; only stated as 'surpasses GPT-4o and Gemini-1.5 Pro' without specific accuracy percentages","Complex multi-axis or 3D charts may exceed vision encoder's reasoning capacity (unknown limits)","Chart legend and label readability depends on image resolution and font size"],"requires":["Chart images in supported formats (specific formats not documented)","Mistral API access or self-hosted deployment","Mistral Commercial License for production analytics use"],"input_types":["chart images","graph images","table images","natural language analytical questions"],"output_types":["extracted data values","trend descriptions","analytical insights","text answers"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_3","uri":"capability://image.visual.multilingual.optical.character.recognition.with.reasoning","name":"multilingual optical character recognition with reasoning","description":"Extracts text from images across multiple languages (documented with Swiss German example) while simultaneously reasoning about extracted content, context, and relationships. Unlike traditional OCR engines that output raw text, this capability integrates text extraction with language understanding, enabling the model to correct OCR errors, understand context-dependent meaning, and answer questions about extracted text in a single pass.","intents":["I need to extract text from images in non-English languages with context-aware error correction","I want to understand the meaning and context of extracted text, not just get raw character sequences","I need to process multilingual documents and answer questions about their content"],"best_for":["international teams processing documents in multiple languages","organizations handling multilingual customer documents (contracts, IDs, receipts)","research teams analyzing historical or non-English text documents"],"limitations":["Supported language list not documented; only Swiss German explicitly mentioned as tested","OCR accuracy varies by language, font, and image quality; no per-language accuracy metrics provided","Handwritten text support unknown; examples only show printed text","Vision encoder resolution limits may affect small or degraded text in any language"],"requires":["Image containing text in supported language","Mistral API access or self-hosted deployment","Mistral Commercial License for production OCR use"],"input_types":["images containing text","natural language questions about extracted text","multilingual document images"],"output_types":["extracted text","text with context-aware corrections","answers about text content","structured data from extracted text"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_4","uri":"capability://image.visual.mathematical.reasoning.over.visual.data","name":"mathematical reasoning over visual data","description":"Solves mathematical problems presented in visual form (equations in images, mathematical diagrams, geometry problems, word problems with visual context) by combining visual understanding with mathematical reasoning. The model achieves 69.4% on MathVista benchmark, outperforming all tested alternatives, through integrated visual parsing and symbolic/numerical reasoning without requiring separate math engines.","intents":["I need to solve math problems from textbook images or handwritten equations","I want to analyze geometry or spatial reasoning problems presented visually","I need to extract and solve mathematical expressions from documents or worksheets"],"best_for":["educational technology platforms automating homework or test grading","research teams analyzing mathematical content in papers or documents","tutoring platforms providing step-by-step solutions to visual math problems"],"limitations":["MathVista benchmark shows 69.4% accuracy; remaining 30.6% represents failure cases (specific problem types not documented)","Handwritten equation recognition quality unknown; examples likely use printed text","Complex multi-step problems may exceed reasoning depth or context window","Symbolic math output format not specified; may return natural language explanations rather than LaTeX or structured notation"],"requires":["Image containing mathematical problem or equation","Mistral API access or self-hosted deployment","Mistral Commercial License for production educational use"],"input_types":["images of mathematical equations","geometry diagrams","word problems with visual context","textbook problem images"],"output_types":["mathematical solutions","step-by-step reasoning","numerical answers","symbolic expressions"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_5","uri":"capability://tool.use.integration.visual.tool.use.and.function.calling","name":"visual tool use and function calling","description":"Integrates visual understanding with tool-use capabilities, enabling the model to analyze images and invoke external functions or APIs based on visual content understanding. The model can interpret visual data, extract relevant parameters from images, and call appropriate tools with image-derived context, supporting workflows where visual analysis triggers downstream automation.","intents":["I need to analyze an image and automatically trigger relevant API calls based on what the image contains","I want to extract parameters from visual content and pass them to external tools or functions","I need to build workflows where image analysis determines which tools to invoke"],"best_for":["automation engineers building image-triggered workflows","teams integrating visual analysis with external APIs or microservices","developers building multi-step processes where image understanding determines next actions"],"limitations":["Tool/function calling implementation details not documented; integration approach with vision encoder unknown","No examples provided of supported tool schemas or function signatures","Tool calling latency impact from vision encoding unknown","Error handling for tool invocation failures not specified"],"requires":["Tool/function definitions in supported schema format (schema format not documented)","Mistral API access or self-hosted deployment","Integration with external APIs or local function handlers","Mistral Commercial License for production tool use"],"input_types":["images","tool/function definitions","text prompts describing desired actions"],"output_types":["tool invocation calls","function parameters extracted from images","execution results from called tools"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_6","uri":"capability://text.generation.language.text.only.language.understanding.inherited.from.mistral.large.2","name":"text-only language understanding (inherited from mistral large 2)","description":"Maintains full text-only language capabilities from Mistral Large 2 foundation model without documented performance degradation, supporting general language understanding, reasoning, and generation tasks. The 124B architecture extends Mistral Large 2 with vision capabilities while preserving text-only performance, enabling the model to handle pure text tasks alongside multimodal inputs in the same conversation.","intents":["I need a multimodal model that doesn't sacrifice text-only performance for vision capabilities","I want to use the same model for both text and image tasks without switching models","I need to maintain conversation context across text-only and multimodal turns"],"best_for":["teams using Mistral Large 2 who want to add vision without model switching","applications with mixed text and image workloads requiring single model","developers building agents that handle both modalities seamlessly"],"limitations":["Text-only performance benchmarks (MMLU, HellaSwag, etc.) not provided; only claimed as 'without compromising text performance'","No comparative analysis vs. Mistral Large 2 on text tasks; performance equivalence unverified in documentation","Vision encoder adds 1B parameters; inference latency impact on text-only tasks unknown"],"requires":["Mistral API access or self-hosted deployment","Mistral Commercial License for production use"],"input_types":["text prompts","conversation history","code snippets","structured text data"],"output_types":["text responses","code generation","structured text output"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_7","uri":"capability://automation.workflow.self.hosted.deployment.with.open.weights","name":"self-hosted deployment with open weights","description":"Distributes model weights via HuggingFace (referenced as 'Mistral Large 24.11') enabling local deployment without API dependency, subject to Mistral Research License (research/educational) or Mistral Commercial License (production). The open-weights distribution enables organizations to run inference on their own infrastructure, avoiding cloud API latency and data transmission, though specific deployment formats (GGUF, safetensors, etc.) and hardware requirements are not documented.","intents":["I need to run this model locally without sending data to Mistral's servers","I want to deploy the model on my own GPU infrastructure for cost control","I need to use this model in an air-gapped or regulated environment"],"best_for":["organizations with data privacy requirements preventing cloud API use","teams with existing GPU infrastructure seeking to optimize inference costs","researchers needing local model access for fine-tuning or analysis"],"limitations":["GPU VRAM requirements unknown; 124B model likely requires 80GB+ VRAM for full precision, unknown quantization support","Deployment format not specified (GGUF, safetensors, or other); compatibility with common inference frameworks unknown","Inference latency and throughput on typical hardware not documented","Model is deprecated; no active maintenance or updates to weights","Mistral Commercial License required for production use; licensing verification and enforcement mechanisms unknown"],"requires":["HuggingFace account to download weights","GPU with sufficient VRAM (exact requirements unknown; estimate 80GB+ for full precision)","Inference framework compatible with model format (framework compatibility unknown)","Mistral Research License for research or Mistral Commercial License for production"],"input_types":["model weights from HuggingFace","inference framework configuration"],"output_types":["locally deployed model instance","inference results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_8","uri":"capability://memory.knowledge.128k.context.window.with.multimodal.content","name":"128k context window with multimodal content","description":"Supports 128K token context window accommodating both text and image tokens, with documented capacity for minimum 30 high-resolution images alongside text. The context window is shared between images (which consume multiple tokens per image depending on resolution) and text, enabling long-form conversations with multiple images without context resets, though actual maximum image count depends on image resolution and text length.","intents":["I need to analyze multiple high-resolution images in a single conversation without losing context","I want to maintain conversation history across many image-text exchanges","I need to process long documents with multiple images and extensive text analysis"],"best_for":["document analysis workflows requiring multi-page context","comparative analysis tasks examining many images in single session","long-form research or investigation requiring image and text context preservation"],"limitations":["128K token limit is shared between images and text; 30 high-resolution images is documented minimum, not maximum capacity","Image tokenization cost unknown; actual image capacity depends on resolution, format, and text length","No guidance on optimal image resolution for context efficiency","Inference latency for full 128K context unknown; may be significantly slower than shorter contexts"],"requires":["Mistral API access or self-hosted deployment with sufficient GPU VRAM","Images in supported formats (formats not documented)","Mistral Commercial License for production use"],"input_types":["text prompts","multiple images","conversation history"],"output_types":["text responses","context-aware analysis"],"categories":["memory-knowledge","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__cap_9","uri":"capability://memory.knowledge.128k.context.window.for.extended.image.text.reasoning","name":"128k context window for extended image-text reasoning","description":"Supports 128K token context window enabling extended conversations with multiple images and long text passages. Context window is shared between image tokens (approximately 4.3K tokens per high-resolution image) and text tokens, allowing up to 30 high-resolution images or proportionally more text. Enables multi-turn conversations where previous context is maintained across turns without re-uploading images.","intents":["I need to analyze a long document with multiple images without losing context","I want to ask follow-up questions about images without re-uploading them","I need to maintain conversation history across many turns with visual content"],"best_for":["document analysis teams processing lengthy reports with embedded images","research teams analyzing multiple papers with figures and tables","customer service teams handling complex multi-image support requests"],"limitations":["30 high-resolution image maximum creates hard ceiling for image-heavy workloads","Image resolution vs quantity trade-off not specified — unclear if 30 images at full resolution","Context window shared between images and text — adding more images reduces text capacity","No documentation on how context is managed across API calls (stateless vs stateful)"],"requires":["API or interface supporting 128K context window (all Mistral API, self-hosted, and Le Chat)","Images and text within combined token budget"],"input_types":["image (up to 30 high-resolution)","text (up to remaining tokens after image tokenization)"],"output_types":["text"],"categories":["memory-knowledge","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pixtral-large__headline","uri":"capability://data.processing.analysis.multimodal.ai.model.for.document.understanding.and.visual.reasoning","name":"multimodal ai model for document understanding and visual reasoning","description":"Pixtral Large is a cutting-edge multimodal AI model that excels in processing images and text together, making it ideal for tasks like document understanding, visual reasoning, and OCR, all while supporting self-hosted deployment.","intents":["best multimodal AI model","multimodal model for document analysis","top AI for visual reasoning tasks","OCR solutions for complex documents","self-hosted multimodal AI options"],"best_for":["document understanding","chart analysis","visual reasoning"],"limitations":[],"requires":[],"input_types":["text","images"],"output_types":["text responses"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API access via Mistral API endpoint (pixtral-large-latest) or self-hosted deployment with sufficient GPU VRAM (exact requirements unknown)","Images in supported formats (specific formats not documented)","Mistral Commercial License for production use; Mistral Research License for research","Document images or PDF pages converted to image format (conversion tool not provided)","Mistral API access or self-hosted deployment","Mistral Commercial License for production document processing","Document in supported language (full list unknown)","API access via Mistral API or self-hosted deployment","Chart images in supported formats (specific formats not documented)","Mistral Commercial License for production analytics use"],"failure_modes":["128K context window is shared between images and text — 30 high-resolution images represents minimum capacity, not maximum; actual throughput depends on image resolution and text length","Vision encoder is 1B parameters with unknown resolution/detail limits; may struggle with extremely fine-grained visual details compared to larger dedicated vision models","Model is deprecated as of announcement date; no active maintenance or updates to vision capabilities","Performance on DocVQA benchmark is not quantified in available documentation; only stated as 'surpasses GPT-4o and Gemini-1.5 Pro' without specific accuracy metrics","Multi-page document handling limited by 128K context window; very long documents may require chunking or page selection","Vision encoder resolution limits unknown; may struggle with small fonts or low-quality scans","Specific language support list not provided — unclear which languages are supported","Performance on low-resource languages not documented","Language detection mechanism not documented","No examples beyond Swiss German and French","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":null,"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=pixtral-large","compare_url":"https://unfragile.ai/compare?artifact=pixtral-large"}},"signature":"2AXpncVfXUOWxGTF20kd6OHtMsbGh5EurdkiMoSiv9E8W/DcaGktj/zY9M9n2KkATKns2AIeZo+IFdB3CwzLBw==","signedAt":"2026-06-22T10:30:24.709Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pixtral-large","artifact":"https://unfragile.ai/pixtral-large","verify":"https://unfragile.ai/api/v1/verify?slug=pixtral-large","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"}}