{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-mistral-medium-3","slug":"mistralai-mistral-medium-3","name":"Mistral: Mistral Medium 3","type":"model","url":"https://openrouter.ai/models/mistralai~mistral-medium-3","page_url":"https://unfragile.ai/mistralai-mistral-medium-3","categories":["llm-apis"],"tags":["mistralai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$4.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-mistral-medium-3__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.extended.context","name":"multi-turn conversational reasoning with extended context","description":"Mistral Medium 3 processes multi-turn conversations with extended context windows, maintaining coherence across long dialogue sequences through transformer-based attention mechanisms optimized for enterprise workloads. The model uses sliding-window attention patterns to reduce computational overhead while preserving long-range dependencies, enabling sustained reasoning across hundreds of exchanges without context collapse or token exhaustion.","intents":["Build customer support chatbots that maintain conversation history across 50+ exchanges without losing context","Develop multi-turn reasoning agents that decompose complex problems across sequential dialogue steps","Create interactive tutoring systems where the model recalls and builds upon previous explanations"],"best_for":["Enterprise teams building production chatbot systems with cost constraints","AI product builders needing frontier-level reasoning at 8× lower cost than GPT-4-class models","Teams deploying multi-turn agents where context window efficiency directly impacts operational costs"],"limitations":["Context window size not explicitly specified in artifact — requires vendor documentation for exact limits","Attention mechanism optimizations may introduce subtle differences in edge-case reasoning vs full-context models","No built-in conversation state persistence — requires external session management for production deployments"],"requires":["API key from Mistral or OpenRouter integration","HTTP/REST client capable of streaming responses","Conversation state management layer (external to model)"],"input_types":["text (natural language queries)","structured conversation history (JSON or message arrays)"],"output_types":["text (natural language responses)","streaming tokens (for real-time UI updates)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-medium-3__cap_1","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Mistral Medium 3 generates syntactically correct, production-ready code across multiple programming languages by leveraging transformer-based code understanding trained on diverse repositories and technical documentation. The model applies semantic reasoning to map natural language specifications to idiomatic code patterns, handling multi-file generation, API integration, and architectural decisions within a single inference pass.","intents":["Generate boilerplate code and scaffolding for new projects in Python, JavaScript, Go, Rust, and other languages","Solve algorithmic problems and provide optimized implementations with complexity analysis","Refactor existing code snippets with explanations of architectural improvements and performance implications"],"best_for":["Solo developers and small teams building prototypes and MVPs where development velocity is critical","Enterprise engineering teams using code generation as part of CI/CD pipelines","Technical educators creating coding tutorials and interactive problem-solving systems"],"limitations":["No built-in code execution or validation — generated code requires manual testing or integration with external linters/compilers","Context-dependent code generation may produce inconsistent results for complex multi-file projects without explicit architectural guidance","No specialized knowledge of proprietary or internal frameworks — requires additional context injection for domain-specific code patterns"],"requires":["API key for Mistral or OpenRouter","Code editor or IDE with API integration capability","Optional: linting/compilation tools for validation (external)"],"input_types":["text (natural language specifications)","code snippets (for refactoring or completion)","structured requirements (JSON or markdown specifications)"],"output_types":["code (multiple languages: Python, JavaScript, Go, Rust, Java, C++, etc.)","explanations (architectural rationale and optimization notes)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-medium-3__cap_2","uri":"capability://image.visual.multimodal.input.processing.with.vision.understanding","name":"multimodal input processing with vision understanding","description":"Mistral Medium 3 processes both text and image inputs simultaneously, enabling vision-language tasks through integrated multimodal transformer architecture that aligns visual and textual representations in a shared embedding space. The model can analyze images, extract structured information, answer visual questions, and reason about image content in conjunction with textual context, all within a single forward pass.","intents":["Build document processing pipelines that extract structured data from scanned PDFs, invoices, and forms","Create visual question-answering systems that analyze images and answer natural language queries about their content","Develop accessibility tools that generate alt-text, captions, and descriptions for images in real-time"],"best_for":["Enterprise document processing teams handling high-volume invoice, receipt, and form digitization","Product teams building accessibility features into web and mobile applications","Data extraction teams automating information retrieval from unstructured visual documents"],"limitations":["Image input format and resolution limits not specified in artifact — requires vendor documentation for supported formats and maximum dimensions","Multimodal alignment may introduce latency overhead compared to text-only inference","No specialized OCR optimization — general vision understanding may underperform specialized OCR engines on low-quality scans"],"requires":["API key for Mistral or OpenRouter with multimodal support enabled","Image preprocessing pipeline (format conversion, resizing if needed)","HTTP client supporting multipart form data or base64 image encoding"],"input_types":["text (natural language queries and context)","image (JPEG, PNG, WebP — exact formats TBD by vendor)"],"output_types":["text (descriptions, answers, extracted information)","structured data (JSON for form field extraction)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-medium-3__cap_3","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.output.generation","name":"structured data extraction and schema-based output generation","description":"Mistral Medium 3 generates structured outputs conforming to specified JSON schemas or data formats through constrained decoding mechanisms that enforce token-level adherence to schema constraints during generation. The model maps natural language inputs or unstructured documents to structured outputs (JSON, CSV, XML) by applying semantic understanding of the input combined with hard constraints on output format, eliminating post-processing parsing errors.","intents":["Extract structured fields from unstructured text (e.g., parse customer feedback into sentiment, topic, and action items)","Convert natural language specifications into API request payloads with guaranteed schema compliance","Generate database records from documents with validated field types and required field enforcement"],"best_for":["Data engineering teams building ETL pipelines that require guaranteed output schema compliance","API developers creating LLM-powered backends where response format must match OpenAPI schemas","Business intelligence teams automating data extraction from unstructured sources into data warehouses"],"limitations":["Schema complexity limits not documented — deeply nested or highly constrained schemas may reduce generation quality","Constrained decoding adds computational overhead — inference latency increases with schema complexity","No built-in schema validation against external systems — requires integration with validation frameworks for cross-system consistency checks"],"requires":["API key for Mistral or OpenRouter","JSON schema definition (OpenAPI 3.0 or JSON Schema format)","Schema validation library (external, e.g., jsonschema for Python)"],"input_types":["text (unstructured documents, natural language queries)","schema definitions (JSON Schema or OpenAPI format)"],"output_types":["structured data (JSON, CSV, XML with guaranteed schema compliance)","validated records (with type checking and required field enforcement)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-medium-3__cap_4","uri":"capability://planning.reasoning.reasoning.intensive.problem.decomposition.and.chain.of.thought","name":"reasoning-intensive problem decomposition and chain-of-thought","description":"Mistral Medium 3 performs multi-step reasoning by decomposing complex problems into intermediate reasoning steps, leveraging transformer-based chain-of-thought mechanisms that explicitly model problem decomposition and solution synthesis. The model generates intermediate reasoning traces that can be inspected for transparency, enabling verification of logic and identification of reasoning errors before final output generation.","intents":["Build AI agents that solve multi-step math problems with visible reasoning steps for educational transparency","Create decision-support systems that decompose business problems into sub-problems with explicit reasoning traces","Develop debugging assistants that trace through code logic step-by-step to identify root causes of failures"],"best_for":["Educational technology teams building transparent AI tutoring systems","Enterprise decision-support teams requiring explainable AI for compliance and audit purposes","AI safety researchers studying reasoning transparency and failure modes"],"limitations":["Reasoning trace quality varies with problem complexity — highly abstract or novel problems may produce incomplete or circular reasoning","Chain-of-thought generation adds significant latency (2-5× longer inference time) compared to direct answer generation","No built-in mechanism to validate intermediate reasoning steps — requires external verification or human review for critical applications"],"requires":["API key for Mistral or OpenRouter","Prompt engineering to explicitly request reasoning traces (e.g., 'Think step-by-step')","Optional: reasoning trace parsing and validation framework"],"input_types":["text (complex problems, questions requiring multi-step reasoning)","structured problem definitions (JSON or markdown with constraints)"],"output_types":["reasoning traces (intermediate steps in natural language or structured format)","final answers (with explicit connection to reasoning steps)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-medium-3__cap_5","uri":"capability://memory.knowledge.knowledge.grounded.response.generation.with.context.injection","name":"knowledge-grounded response generation with context injection","description":"Mistral Medium 3 generates responses grounded in provided context documents or knowledge bases by applying attention mechanisms that prioritize relevant context passages during generation, reducing hallucination through explicit grounding in supplied information. The model integrates retrieval-augmented generation (RAG) patterns by accepting context as input and weighting its attention toward context-supported facts, enabling knowledge-grounded answers without fine-tuning.","intents":["Build customer support systems that answer questions exclusively from company documentation and knowledge bases","Create fact-checking tools that verify claims against provided source documents","Develop research assistants that synthesize information from multiple papers or documents with explicit source attribution"],"best_for":["Customer support teams implementing knowledge-grounded chatbots with company-specific information","Legal and compliance teams building document-aware systems that must cite sources","Research teams building literature synthesis tools with explicit source tracking"],"limitations":["Context window size limits the amount of grounding material — large knowledge bases require external retrieval to select relevant passages","Attention mechanism may still hallucinate when context is ambiguous or contradictory","No built-in source attribution or citation tracking — requires prompt engineering or post-processing to extract and format citations"],"requires":["API key for Mistral or OpenRouter","Context documents or knowledge base passages (pre-retrieved or embedded in prompt)","Optional: vector database or retrieval system for passage selection (e.g., Pinecone, Weaviate)"],"input_types":["text (user queries)","context documents (knowledge base passages, documentation, source material)"],"output_types":["text (grounded responses with reduced hallucination)","structured citations (with source document references if post-processed)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-medium-3__cap_6","uri":"capability://tool.use.integration.api.integration.and.tool.calling.with.function.schemas","name":"api integration and tool-calling with function schemas","description":"Mistral Medium 3 supports function calling through schema-based tool definitions, enabling the model to generate structured function calls that can be executed by external systems or agents. The model understands function signatures, parameter types, and constraints, generating valid function calls that integrate with REST APIs, webhooks, or local function registries without requiring manual prompt engineering for each tool.","intents":["Build autonomous agents that call external APIs (weather, maps, payment processors) based on user intent","Create workflow automation systems where the model decides which tools to invoke and in what sequence","Develop multi-step task executors that chain function calls to accomplish complex goals"],"best_for":["AI agent developers building autonomous systems with external tool integration","Workflow automation teams implementing LLM-driven process orchestration","API platform teams exposing their services through LLM-native function calling interfaces"],"limitations":["Function calling accuracy depends on schema clarity — ambiguous or poorly documented schemas reduce correct function selection","No built-in error handling or retry logic — failed function calls require external agent loop management","Limited to synchronous function calls — asynchronous or long-running operations require external orchestration"],"requires":["API key for Mistral or OpenRouter","Function schema definitions (OpenAPI 3.0 or JSON Schema format)","Agent framework or orchestration layer to execute returned function calls (e.g., LangChain, AutoGPT)"],"input_types":["text (user intent and queries)","function schemas (tool definitions with parameters and constraints)"],"output_types":["function calls (structured JSON with function name and parameters)","text (reasoning about which tools to use)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-medium-3__cap_7","uri":"capability://text.generation.language.multilingual.understanding.and.translation","name":"multilingual understanding and translation","description":"Mistral Medium 3 processes and generates text across multiple languages through multilingual transformer training, understanding semantic meaning across language boundaries and enabling translation, cross-lingual question-answering, and multilingual content generation. The model maintains semantic consistency across language pairs without requiring separate translation models or language-specific fine-tuning.","intents":["Build global customer support systems that handle queries in 20+ languages with consistent quality","Create content localization pipelines that translate and culturally adapt marketing materials","Develop cross-lingual search and question-answering systems that answer queries in any language using multilingual knowledge"],"best_for":["Global enterprises serving customers across multiple language regions","Content platforms requiring multilingual support without separate model deployments","International teams building products that must support diverse language communities"],"limitations":["Language coverage not explicitly specified — likely strong for major languages (Spanish, French, German, Chinese, Japanese) but weaker for low-resource languages","Translation quality varies by language pair — distant language pairs may produce less idiomatic translations than specialized translation models","Cultural context and idioms may not translate perfectly — requires human review for culturally sensitive content"],"requires":["API key for Mistral or OpenRouter","Language detection or explicit language specification in prompts","Optional: human review process for critical translations"],"input_types":["text in any supported language","language specification (explicit or auto-detected)"],"output_types":["text in target language","translations with semantic preservation"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-medium-3__cap_8","uri":"capability://text.generation.language.instruction.following.and.task.specific.adaptation","name":"instruction-following and task-specific adaptation","description":"Mistral Medium 3 follows complex, multi-part instructions and adapts its behavior based on explicit task specifications provided in prompts, enabling zero-shot task adaptation without fine-tuning. The model interprets detailed instructions about tone, format, constraints, and output structure, applying them consistently across multiple generations without requiring separate model versions or training.","intents":["Create customizable content generation systems where users specify tone, style, and format requirements","Build prompt-driven automation where complex business rules are encoded as instructions rather than code","Develop interactive systems that adapt behavior based on user-provided guidelines and preferences"],"best_for":["No-code automation teams building instruction-driven workflows","Content teams creating style-consistent outputs across multiple channels","Product teams building customizable AI features without model fine-tuning"],"limitations":["Instruction following quality degrades with instruction complexity — highly nested or contradictory instructions may produce inconsistent results","No persistent instruction memory — instructions must be repeated in each request or managed externally","Instruction injection attacks possible if user input is not properly sanitized before inclusion in prompts"],"requires":["API key for Mistral or OpenRouter","Well-structured prompt templates with clear instruction formatting","Input validation and sanitization for user-provided instructions"],"input_types":["text (task instructions and user input)","structured specifications (JSON or markdown with constraints)"],"output_types":["text (outputs formatted according to instructions)","structured data (if instruction specifies format)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API key from Mistral or OpenRouter integration","HTTP/REST client capable of streaming responses","Conversation state management layer (external to model)","API key for Mistral or OpenRouter","Code editor or IDE with API integration capability","Optional: linting/compilation tools for validation (external)","API key for Mistral or OpenRouter with multimodal support enabled","Image preprocessing pipeline (format conversion, resizing if needed)","HTTP client supporting multipart form data or base64 image encoding","JSON schema definition (OpenAPI 3.0 or JSON Schema format)"],"failure_modes":["Context window size not explicitly specified in artifact — requires vendor documentation for exact limits","Attention mechanism optimizations may introduce subtle differences in edge-case reasoning vs full-context models","No built-in conversation state persistence — requires external session management for production deployments","No built-in code execution or validation — generated code requires manual testing or integration with external linters/compilers","Context-dependent code generation may produce inconsistent results for complex multi-file projects without explicit architectural guidance","No specialized knowledge of proprietary or internal frameworks — requires additional context injection for domain-specific code patterns","Image input format and resolution limits not specified in artifact — requires vendor documentation for supported formats and maximum dimensions","Multimodal alignment may introduce latency overhead compared to text-only inference","No specialized OCR optimization — general vision understanding may underperform specialized OCR engines on low-quality scans","Schema complexity limits not documented — deeply nested or highly constrained schemas may reduce generation quality","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":"2026-05-24T12:16:24.484Z","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-mistral-medium-3","compare_url":"https://unfragile.ai/compare?artifact=mistralai-mistral-medium-3"}},"signature":"7no74bHUWou5mz0uGHVF89oo5JlNPRN64uo+hutGdQPLE54+ziE/9YxDklnjq/wek3KGFLfuYy9lD4BH3gcXAQ==","signedAt":"2026-06-20T09:03:58.106Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-mistral-medium-3","artifact":"https://unfragile.ai/mistralai-mistral-medium-3","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-mistral-medium-3","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"}}