{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-mistral-large-2512","slug":"mistralai-mistral-large-2512","name":"Mistral: Mistral Large 3 2512","type":"model","url":"https://openrouter.ai/models/mistralai~mistral-large-2512","page_url":"https://unfragile.ai/mistralai-mistral-large-2512","categories":["model-training"],"tags":["mistralai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-mistral-large-2512__cap_0","uri":"capability://text.generation.language.sparse.mixture.of.experts.text.generation.with.41b.active.parameters","name":"sparse-mixture-of-experts text generation with 41b active parameters","description":"Generates text using a sparse mixture-of-experts (MoE) architecture where only 41 billion parameters are active per forward pass out of 675 billion total, enabling efficient inference while maintaining capability parity with dense models. The routing mechanism dynamically selects expert subsets based on input tokens, reducing computational overhead compared to dense transformer architectures while preserving multi-domain reasoning depth.","intents":["Generate coherent multi-turn conversations with low latency and reduced inference cost","Process long-context documents (up to model's context window) without proportional compute scaling","Build production LLM applications where inference cost and speed are critical constraints","Perform complex reasoning tasks (math, code, analysis) with parameter efficiency"],"best_for":["teams building cost-sensitive production LLM applications requiring high throughput","developers deploying conversational AI at scale with latency constraints","builders creating multi-domain reasoning systems needing balanced capability-to-cost ratio"],"limitations":["Sparse routing adds non-deterministic latency variance depending on token complexity and expert load balancing","MoE architecture may show degraded performance on tasks requiring uniform expert knowledge (vs dense models)","Requires API access via OpenRouter; no local deployment option without separate licensing","Context window size not explicitly specified in artifact — verify against official Mistral documentation"],"requires":["OpenRouter API key or direct Mistral API credentials","HTTP/REST client capability or SDK wrapper (Python, JavaScript, etc.)","Understanding of token counting for cost estimation (sparse routing affects token efficiency)"],"input_types":["text (natural language prompts, code snippets, documents)","structured prompts with system instructions and few-shot examples"],"output_types":["text (natural language responses, code generation, structured text)","streaming token output for real-time applications"],"categories":["text-generation-language","large-language-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_1","uri":"capability://text.generation.language.multi.domain.instruction.following.with.chain.of.thought.reasoning","name":"multi-domain instruction-following with chain-of-thought reasoning","description":"Executes complex multi-step instructions across diverse domains (mathematics, coding, creative writing, analysis) by internally decomposing problems into reasoning chains before generating outputs. The model uses attention mechanisms trained on instruction-following datasets to parse user intent, maintain task context across multiple turns, and produce domain-appropriate responses with explicit reasoning steps when beneficial.","intents":["Solve multi-step math problems with intermediate reasoning steps shown","Generate production-quality code with explanations of architectural decisions","Analyze documents and extract structured insights with reasoning transparency","Maintain coherent multi-turn conversations with context awareness across 10+ exchanges"],"best_for":["developers building reasoning-heavy applications (code generation, technical documentation)","teams needing explainable AI outputs for compliance or user trust","educators and content creators requiring nuanced, multi-faceted responses"],"limitations":["Chain-of-thought reasoning increases token consumption by 20-40% compared to direct answers","Performance on highly specialized domains (medical diagnosis, legal interpretation) not independently validated","No built-in guardrails for hallucination detection — requires external validation for critical applications","Reasoning quality degrades on tasks requiring real-time information or knowledge cutoff-dependent facts"],"requires":["OpenRouter API key or Mistral API credentials","Prompt engineering expertise to elicit reasoning chains (system prompts, few-shot examples)","Token budget accounting for 1.3-1.4x token multiplier for reasoning-heavy tasks"],"input_types":["natural language instructions with varying complexity","code snippets for analysis or completion","multi-turn conversation history with context"],"output_types":["text with explicit reasoning steps","code with inline comments and architectural explanations","structured analysis with supporting evidence"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_2","uri":"capability://code.generation.editing.code.generation.and.technical.documentation.synthesis","name":"code generation and technical documentation synthesis","description":"Generates syntactically correct, idiomatic code across 40+ programming languages and produces technical documentation by understanding code semantics, API patterns, and domain conventions. The model leverages training on public code repositories and technical documentation to produce code that follows language-specific best practices, includes appropriate error handling, and generates explanatory comments aligned with code structure.","intents":["Generate boilerplate code and scaffolding for new projects in any major language","Complete partial code implementations with context-aware suggestions","Translate code between programming languages while preserving logic and idioms","Generate API documentation, README files, and technical guides from code context"],"best_for":["full-stack developers accelerating development velocity across polyglot codebases","teams generating technical documentation at scale from code repositories","developers learning new programming languages or frameworks through example generation"],"limitations":["Code generation quality varies by language popularity — excellent for Python/JavaScript, degraded for niche languages (Rust, Go, Kotlin)","No static analysis or type-checking — generated code requires testing and linting before production use","Security vulnerabilities in generated code not guaranteed to be avoided; requires security review for sensitive applications","Context window limitations may truncate large files — requires file chunking for codebases >8K tokens"],"requires":["OpenRouter API key or Mistral API credentials","IDE or editor integration for seamless code insertion (VS Code extension, etc.)","Linting and testing infrastructure to validate generated code quality"],"input_types":["partial code with context comments","natural language descriptions of desired functionality","existing code files for refactoring or translation","API specifications or type definitions"],"output_types":["syntactically correct code in target language","code with inline comments and docstrings","markdown documentation with code examples","refactored code with improved structure or performance"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_3","uri":"capability://text.generation.language.long.context.document.processing.and.summarization","name":"long-context document processing and summarization","description":"Processes extended documents (up to model's context window limit) and generates summaries, extracts key information, or answers questions about content by maintaining coherent understanding across thousands of tokens. The sparse MoE architecture enables efficient processing of long contexts by selectively activating expert parameters relevant to document structure and query type, reducing memory overhead compared to dense models.","intents":["Summarize research papers, legal documents, or technical specifications into executive summaries","Extract structured data (entities, relationships, key facts) from unstructured documents","Answer specific questions about document content with cited evidence","Analyze document collections for patterns, contradictions, or thematic consistency"],"best_for":["knowledge workers processing large document volumes (legal, research, compliance)","teams building document analysis pipelines with cost constraints","developers creating RAG systems requiring efficient context processing"],"limitations":["Summarization quality degrades on documents >50K tokens due to attention distribution challenges","No built-in citation tracking — requires post-processing to verify evidence attribution","Performance on domain-specific jargon (medical, legal, technical) depends on training data coverage","Context window size not explicitly specified — verify against official documentation for exact limits"],"requires":["OpenRouter API key or Mistral API credentials","Document preprocessing pipeline (text extraction from PDFs, chunking for context limits)","Token counting utilities to estimate cost before processing large documents"],"input_types":["plain text documents","extracted text from PDFs or structured documents","multi-document collections with cross-document queries"],"output_types":["abstractive summaries at various compression ratios","structured extraction (JSON, CSV) of key information","question-answer pairs with evidence citations","analytical reports with pattern identification"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_4","uri":"capability://text.generation.language.conversational.ai.with.multi.turn.context.management","name":"conversational ai with multi-turn context management","description":"Maintains coherent multi-turn conversations by preserving conversation history, tracking context across exchanges, and generating contextually appropriate responses that reference prior statements. The model uses attention mechanisms to weight relevant prior context, enabling natural dialogue flow while managing token efficiency through selective context compression for extended conversations.","intents":["Build chatbots and conversational agents with natural dialogue flow across 20+ turns","Create customer support systems that maintain context across multiple user interactions","Develop interactive tutoring systems with persistent learning context","Implement multi-turn reasoning assistants for complex problem-solving workflows"],"best_for":["teams building conversational AI products with emphasis on natural dialogue","customer support platforms requiring context-aware response generation","educational technology companies creating interactive learning experiences"],"limitations":["Context window limits force conversation truncation after 50-100+ turns depending on message length","No built-in conversation state persistence — requires external database for multi-session continuity","Performance degrades on conversations with conflicting or contradictory prior statements","Token efficiency decreases with conversation length — requires periodic summarization for cost control"],"requires":["OpenRouter API key or Mistral API credentials","Conversation state management system (in-memory or database-backed)","Message formatting protocol (system/user/assistant roles) for conversation structure"],"input_types":["user messages in natural language","conversation history with speaker attribution","system prompts defining conversation tone and constraints"],"output_types":["natural language responses contextually appropriate to conversation history","streaming responses for real-time conversation experience","structured conversation metadata (sentiment, intent, entities)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_5","uri":"capability://text.generation.language.creative.content.generation.with.style.and.tone.control","name":"creative content generation with style and tone control","description":"Generates creative text (stories, poetry, marketing copy, creative writing) with controllable style, tone, and narrative structure by leveraging training on diverse creative writing datasets and understanding of rhetorical devices, narrative patterns, and stylistic conventions. The model responds to explicit style instructions and few-shot examples to adapt output to specific creative requirements.","intents":["Generate marketing copy and advertising content with brand-specific tone","Create story outlines, plot summaries, or full narrative content","Produce poetry, creative writing, or artistic text in specified styles","Adapt existing content to different tones (formal to casual, technical to accessible)"],"best_for":["marketing and content creation teams generating copy at scale","creative writers using AI as a brainstorming and drafting tool","game developers and interactive fiction creators generating narrative content"],"limitations":["Creative output quality highly dependent on prompt specificity and style examples provided","No guarantee of originality — generated content may inadvertently echo training data","Consistency across long-form creative works (novels, scripts) degrades beyond 10K tokens","Subjective quality assessment required — no automated metrics for creative appropriateness"],"requires":["OpenRouter API key or Mistral API credentials","Clear style guidelines and tone examples in prompts","Human editorial review for brand consistency and originality verification"],"input_types":["natural language prompts with style and tone specifications","few-shot examples of desired creative style","partial content for continuation or adaptation","structured briefs (target audience, key messages, constraints)"],"output_types":["creative text in specified style and tone","multiple variations for A/B testing","structured content (outlines, summaries, scene descriptions)","streaming output for interactive creative workflows"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_6","uri":"capability://text.generation.language.multilingual.text.generation.and.translation","name":"multilingual text generation and translation","description":"Generates and translates text across 50+ languages with language-specific grammar, idiom, and cultural context preservation by leveraging multilingual training data and language-specific token vocabularies. The model maintains semantic meaning across language boundaries while adapting to target language conventions, enabling both direct translation and cross-lingual content generation.","intents":["Translate content between major languages while preserving tone and cultural nuance","Generate content directly in target languages without intermediate translation","Build multilingual chatbots and customer support systems","Localize marketing content and user-facing text for international audiences"],"best_for":["global companies localizing products and content for international markets","translation services augmenting human translators with AI assistance","teams building multilingual applications with cost constraints"],"limitations":["Translation quality varies significantly by language pair — excellent for major languages (Spanish, French, German), degraded for low-resource languages","Cultural context and idiom adaptation requires human review for sensitive content","No specialized domain knowledge for technical translation (medical, legal, financial) — requires domain-specific fine-tuning","Language detection may fail on code-mixed text or transliterated content"],"requires":["OpenRouter API key or Mistral API credentials","Language specification in prompts for target language selection","Human review process for quality assurance on critical translations"],"input_types":["text in any supported language","language pair specification (source and target)","context or glossary for domain-specific terminology","tone and style guidelines for localization"],"output_types":["translated text in target language","multiple translation variations for selection","localized content with cultural adaptation","language-specific formatting (RTL languages, character encoding)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.and.json.schema.compliance","name":"structured data extraction and json schema compliance","description":"Extracts structured information from unstructured text and generates output conforming to specified JSON schemas through schema-aware generation that constrains output to valid JSON structures matching provided type definitions. The model understands schema constraints and generates only valid structured data without requiring post-processing validation or repair.","intents":["Extract entities (names, dates, amounts) from documents into structured JSON","Parse natural language input into structured form data for applications","Generate API responses conforming to OpenAPI schemas","Convert unstructured data into database-ready structured formats"],"best_for":["data engineering teams building ETL pipelines with LLM-based extraction","developers building form-filling and data collection systems","teams automating document processing with structured output requirements"],"limitations":["Schema compliance requires explicit schema definition in prompts — no automatic schema inference","Extraction accuracy depends on schema clarity and field naming conventions","Complex nested schemas (3+ levels) may produce incomplete or malformed output","No built-in validation of extracted values against domain constraints (e.g., valid email format)"],"requires":["OpenRouter API key or Mistral API credentials","JSON schema definition for target output structure","Post-processing validation to verify extracted values meet domain constraints"],"input_types":["unstructured text (documents, emails, forms)","JSON schema definition specifying output structure","examples of desired extraction format"],"output_types":["valid JSON conforming to specified schema","structured data ready for database insertion","API responses matching OpenAPI specifications"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_8","uri":"capability://search.retrieval.semantic.search.and.relevance.ranking.over.text.collections","name":"semantic search and relevance ranking over text collections","description":"Ranks text documents by semantic relevance to queries by understanding query intent and document content semantics, enabling effective search without explicit keyword matching. The model can be used to score document relevance, identify most similar documents, or rank search results by semantic similarity rather than keyword overlap, supporting both retrieval and re-ranking workflows.","intents":["Re-rank search results from keyword search engines by semantic relevance","Find most similar documents in a collection without keyword overlap","Score document relevance for information retrieval systems","Implement semantic search over unstructured document collections"],"best_for":["teams building semantic search systems over document collections","search platforms augmenting keyword search with semantic re-ranking","information retrieval systems requiring relevance scoring"],"limitations":["Ranking quality depends on query clarity and document diversity","Computational cost scales linearly with collection size — not suitable for real-time ranking of millions of documents without pre-computed embeddings","No built-in caching of document representations — requires external vector store for efficient re-ranking","Semantic understanding may fail on domain-specific terminology without context"],"requires":["OpenRouter API key or Mistral API credentials","Document collection in text format","Query specification with context or examples","External vector store (optional, for efficiency) like Pinecone or Weaviate"],"input_types":["query text with optional context","document collection (list of texts)","ranking criteria or relevance guidelines"],"output_types":["ranked list of documents by relevance score","relevance scores for each document","explanation of relevance reasoning"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2512__cap_9","uri":"capability://text.generation.language.question.answering.with.evidence.citation.and.source.attribution","name":"question-answering with evidence citation and source attribution","description":"Answers questions about provided documents or knowledge by generating responses with explicit citations to source material, enabling users to verify answers and trace reasoning to original sources. The model identifies relevant passages, synthesizes information across sources, and attributes claims to specific documents or sections, supporting both single-document and multi-document question-answering workflows.","intents":["Answer questions about documents with citations to specific passages","Build FAQ systems that cite source material for each answer","Create research assistants that synthesize information across multiple sources","Implement fact-checking systems that verify claims against source documents"],"best_for":["knowledge workers requiring cited answers for research and analysis","teams building fact-checking and verification systems","customer support platforms providing sourced answers from knowledge bases"],"limitations":["Citation accuracy depends on source document clarity and question specificity","Multi-document synthesis may produce incomplete answers if relevant information spans multiple sources","No built-in verification that citations actually support claims — requires human review","Performance degrades on questions requiring inference beyond explicit source content"],"requires":["OpenRouter API key or Mistral API credentials","Source documents in text format with clear structure","Question specification with context about expected answer scope"],"input_types":["question in natural language","source documents (single or multiple)","context about expected answer scope or constraints"],"output_types":["answer text with inline citations","structured citations with document and passage references","confidence scores for answer quality","alternative answers with different citation paths"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key or direct Mistral API credentials","HTTP/REST client capability or SDK wrapper (Python, JavaScript, etc.)","Understanding of token counting for cost estimation (sparse routing affects token efficiency)","OpenRouter API key or Mistral API credentials","Prompt engineering expertise to elicit reasoning chains (system prompts, few-shot examples)","Token budget accounting for 1.3-1.4x token multiplier for reasoning-heavy tasks","IDE or editor integration for seamless code insertion (VS Code extension, etc.)","Linting and testing infrastructure to validate generated code quality","Document preprocessing pipeline (text extraction from PDFs, chunking for context limits)","Token counting utilities to estimate cost before processing large documents"],"failure_modes":["Sparse routing adds non-deterministic latency variance depending on token complexity and expert load balancing","MoE architecture may show degraded performance on tasks requiring uniform expert knowledge (vs dense models)","Requires API access via OpenRouter; 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