{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-mistral-large-2411","slug":"mistralai-mistral-large-2411","name":"Mistral Large 2411","type":"model","url":"https://openrouter.ai/models/mistralai~mistral-large-2411","page_url":"https://unfragile.ai/mistralai-mistral-large-2411","categories":["chatbots-assistants"],"tags":["mistralai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-mistral-large-2411__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.extended.context","name":"multi-turn conversational reasoning with extended context","description":"Processes multi-turn conversations with up to 32K token context window, maintaining coherent reasoning across dialogue turns through transformer-based attention mechanisms that track conversation history and user intent evolution. Implements sliding-window attention patterns to efficiently manage long contexts while preserving semantic relationships between early and recent exchanges.","intents":["Build a chatbot that remembers conversation context across 20+ exchanges without losing coherence","Create an AI assistant that can reason about complex problems discussed incrementally over multiple turns","Develop a customer support agent that maintains context across long support tickets"],"best_for":["Teams building conversational AI agents requiring sustained context","Developers creating multi-turn dialogue systems for customer support or tutoring","Builders prototyping complex reasoning assistants with iterative problem-solving"],"limitations":["32K token context window may be insufficient for very long document analysis or 100+ turn conversations","Attention computation scales quadratically with context length, causing latency increases on maximum-length inputs","No built-in conversation summarization — full history must be maintained in prompt for optimal performance"],"requires":["API key for Mistral AI or OpenRouter access","HTTP client capable of streaming responses","Token counting library to stay within 32K limit"],"input_types":["text","conversation history as formatted strings"],"output_types":["text","streaming text tokens"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_1","uri":"capability://text.generation.language.instruction.following.with.structured.output.formatting","name":"instruction-following with structured output formatting","description":"Executes complex multi-step instructions with high fidelity through fine-tuning on instruction-following datasets and reinforcement learning from human feedback (RLHF). Supports explicit output format requests (JSON, XML, markdown, code blocks) by conditioning generation on format tokens, enabling deterministic parsing of model outputs without post-processing regex.","intents":["Generate structured JSON responses from natural language prompts without parsing errors","Create a system that reliably produces code snippets, markdown documentation, or XML configurations on demand","Build an API wrapper that guarantees consistent output format for downstream processing"],"best_for":["Developers building LLM-powered APIs requiring deterministic output formats","Teams integrating LLM outputs into structured data pipelines","Builders creating code generation or documentation tools"],"limitations":["Format adherence is probabilistic, not guaranteed — edge cases may produce malformed JSON or incomplete structures","Complex nested structures (deeply nested JSON, mixed format requests) have higher error rates than simple formats","No schema validation — model may produce syntactically valid but semantically incorrect structured data"],"requires":["API key for Mistral AI or OpenRouter","Prompt engineering to explicitly specify desired output format","Optional: JSON schema library for post-generation validation"],"input_types":["text","natural language instructions with format specifications"],"output_types":["JSON","XML","markdown","code blocks","plain text"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_10","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batching","name":"api-based inference with streaming and batching","description":"Provides model access through REST API with support for streaming responses (token-by-token delivery) and batch processing (multiple requests in single API call). Implements request queuing, rate limiting, and load balancing on the backend to handle concurrent requests efficiently, with streaming enabled through server-sent events (SSE) for real-time token delivery.","intents":["Integrate the model into web applications with real-time streaming responses","Process large batches of requests efficiently without making individual API calls","Build scalable applications that handle concurrent user requests"],"best_for":["Web developers building real-time chat interfaces","Data engineers processing large document batches","Teams building production applications requiring high throughput"],"limitations":["Streaming adds latency for time-to-first-token compared to non-streaming requests","Batch processing requires requests to be queued — no guaranteed order of execution","Rate limiting may throttle requests during peak usage — requires backoff strategies"],"requires":["API key for Mistral AI or OpenRouter","HTTP client supporting streaming (for streaming responses)","Network connectivity and HTTPS support"],"input_types":["JSON payloads","text prompts"],"output_types":["streaming text tokens","complete responses","batch results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_2","uri":"capability://code.generation.editing.code.understanding.and.generation.across.80.programming.languages","name":"code understanding and generation across 80+ programming languages","description":"Analyzes and generates code through transformer embeddings trained on diverse programming language corpora, supporting syntax-aware completion and bug detection across Python, JavaScript, Java, C++, Go, Rust, and 75+ other languages. Uses byte-pair encoding (BPE) tokenization optimized for code tokens, enabling efficient representation of variable names, operators, and language-specific syntax patterns.","intents":["Generate boilerplate code or complete partial implementations in any major programming language","Analyze code snippets to identify potential bugs, security issues, or performance problems","Translate code between languages or refactor existing code while preserving functionality"],"best_for":["Full-stack developers needing code generation across multiple tech stacks","DevOps engineers automating infrastructure-as-code generation","Security teams analyzing code for vulnerabilities"],"limitations":["Code generation quality varies significantly by language — well-represented languages (Python, JavaScript) perform better than niche languages","No execution environment — generated code is not validated for correctness, only syntactic plausibility","Context window limits analysis to files under ~8K tokens; large codebases require chunking strategies"],"requires":["API key for Mistral AI or OpenRouter","Code context provided as text (no AST parsing required)","Optional: linter or type checker for post-generation validation"],"input_types":["code snippets","partial code with comments","natural language descriptions of desired code"],"output_types":["code","code explanations","refactored code","bug reports"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_3","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.integration","name":"function calling with schema-based tool integration","description":"Enables tool use through structured function calling via JSON schema definitions, where the model generates function names and arguments as structured tokens rather than free-form text. Implements a function registry pattern where tools are declared with parameter schemas, and the model's output is parsed into executable function calls with type validation before invocation.","intents":["Build an AI agent that can call APIs, databases, or local functions based on user requests","Create a system where the model decides which tools to use and generates properly-typed arguments","Develop a multi-step workflow where the model chains function calls to solve complex tasks"],"best_for":["Developers building agentic systems with external tool dependencies","Teams creating AI-powered automation workflows","Builders integrating LLMs with REST APIs or microservices"],"limitations":["Function calling is probabilistic — model may hallucinate function names or generate invalid argument types","No automatic retry logic — failed function calls require explicit error handling and reprompting","Schema complexity has limits — deeply nested parameter schemas may confuse the model"],"requires":["API key for Mistral AI or OpenRouter","JSON schema definitions for each tool","Function registry or dispatcher to map generated calls to actual implementations"],"input_types":["text","JSON schema definitions"],"output_types":["function calls","function arguments","execution results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_4","uri":"capability://planning.reasoning.reasoning.and.chain.of.thought.decomposition","name":"reasoning and chain-of-thought decomposition","description":"Performs multi-step reasoning through implicit chain-of-thought patterns learned during training, where the model generates intermediate reasoning steps before producing final answers. Supports explicit prompting for step-by-step reasoning through techniques like 'think step by step' or structured reasoning templates, enabling the model to break complex problems into manageable sub-problems.","intents":["Solve complex math problems by having the model show its work step-by-step","Debug code by asking the model to reason through the execution flow before identifying bugs","Generate detailed explanations for technical concepts by requesting structured reasoning"],"best_for":["Educators building tutoring systems that require detailed explanations","Developers creating debugging or code review assistants","Teams building reasoning-heavy applications like math solvers or logic puzzles"],"limitations":["Reasoning quality degrades on problems requiring specialized domain knowledge or novel reasoning patterns","Chain-of-thought adds latency — reasoning steps consume tokens and increase response time by 2-4x","No guarantee of correctness — intermediate reasoning steps may contain logical errors"],"requires":["API key for Mistral AI or OpenRouter","Prompts explicitly requesting step-by-step reasoning","Sufficient context window to accommodate reasoning steps (recommend 8K+ tokens available)"],"input_types":["text","math problems","code snippets","logic puzzles"],"output_types":["reasoning steps","final answers","explanations"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_5","uri":"capability://text.generation.language.multilingual.text.generation.and.translation","name":"multilingual text generation and translation","description":"Generates and translates text across 40+ languages through multilingual transformer embeddings trained on parallel corpora and monolingual text in diverse languages. Uses language-specific tokenization patterns and cross-lingual transfer learning to maintain semantic consistency during translation while preserving cultural nuances and idiomatic expressions.","intents":["Translate content between languages while preserving tone and meaning","Generate marketing copy, documentation, or creative content in multiple languages from a single prompt","Build a multilingual chatbot that responds naturally in the user's preferred language"],"best_for":["Global teams building products for international audiences","Content creators needing multilingual content generation","Developers building translation or localization tools"],"limitations":["Translation quality varies by language pair — high-resource pairs (English-Spanish) perform better than low-resource pairs","Idioms and cultural references may not translate accurately without explicit context","Language mixing in prompts may confuse the model — best results with single-language inputs"],"requires":["API key for Mistral AI or OpenRouter","Language specification in prompts (e.g., 'respond in French')","Optional: language detection library for automatic language identification"],"input_types":["text","language-tagged prompts"],"output_types":["translated text","multilingual text","language-specific content"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_6","uri":"capability://text.generation.language.content.summarization.and.extraction","name":"content summarization and extraction","description":"Extracts key information and generates summaries from long documents through attention mechanisms that identify salient content and abstractive summarization patterns learned during training. Supports multiple summarization styles (bullet points, paragraphs, executive summaries) and information extraction (named entities, key facts, relationships) through prompt-based control without requiring fine-tuning.","intents":["Summarize long articles, research papers, or documents into concise overviews","Extract structured information (entities, dates, relationships) from unstructured text","Generate key takeaways or highlights from meeting transcripts or news articles"],"best_for":["Content teams managing large document volumes","Researchers analyzing academic papers or technical documentation","Developers building document processing or knowledge extraction systems"],"limitations":["Summarization quality degrades on highly technical or domain-specific content without domain context","Extractive summarization may miss important nuances present only in full text","Context window limits — documents larger than 30K tokens require chunking strategies"],"requires":["API key for Mistral AI or OpenRouter","Document text provided as input","Optional: chunking strategy for documents exceeding context window"],"input_types":["text","long documents","articles"],"output_types":["summaries","extracted entities","key facts","structured data"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_7","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Generates creative text including stories, poetry, marketing copy, and dialogue through language modeling trained on diverse creative corpora. Uses temperature and sampling parameters to control creativity levels, enabling deterministic outputs for structured content (product descriptions) or highly variable outputs for creative exploration (story variations).","intents":["Generate creative story ideas, plot outlines, or full narratives","Create marketing copy, product descriptions, or advertising content","Write poetry, dialogue, or other creative text in specific styles or genres"],"best_for":["Content creators and copywriters augmenting creative workflows","Game developers generating narrative content or dialogue","Marketing teams creating variations of ad copy or product descriptions"],"limitations":["Generated content may contain clichés or lack originality compared to human-written content","Consistency across long narratives degrades — characters may behave inconsistently in multi-page stories","Style imitation requires explicit prompting — model may not capture subtle stylistic nuances without examples"],"requires":["API key for Mistral AI or OpenRouter","Detailed prompts with style, tone, and content specifications","Optional: temperature/sampling parameter tuning for desired creativity level"],"input_types":["text","creative briefs","style specifications"],"output_types":["creative text","stories","poetry","marketing copy"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_8","uri":"capability://text.generation.language.question.answering.with.knowledge.grounding","name":"question-answering with knowledge grounding","description":"Answers questions by retrieving relevant knowledge from training data and generating contextually appropriate responses through attention mechanisms that identify question-relevant information. Supports open-domain QA (general knowledge questions) and closed-domain QA (questions about provided documents) through prompt-based context injection without requiring external retrieval systems.","intents":["Build a FAQ system that answers user questions about products, services, or documentation","Create a knowledge assistant that answers general knowledge questions","Develop a document-based QA system that answers questions about specific texts"],"best_for":["Teams building customer support chatbots","Developers creating knowledge base systems","Builders integrating QA into larger applications"],"limitations":["Knowledge cutoff — model cannot answer questions about events after training data cutoff (April 2024)","Hallucination risk — model may generate plausible-sounding but incorrect answers, especially on obscure topics","No source attribution — answers lack citations or references to source documents"],"requires":["API key for Mistral AI or OpenRouter","Question text as input","Optional: document context for closed-domain QA"],"input_types":["text","questions","document context"],"output_types":["answers","explanations","follow-up suggestions"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large-2411__cap_9","uri":"capability://text.generation.language.sentiment.analysis.and.text.classification","name":"sentiment analysis and text classification","description":"Classifies text sentiment (positive, negative, neutral) and assigns topic categories through learned semantic representations and classification patterns from training data. Supports multi-label classification (assigning multiple categories to single text) and fine-grained sentiment analysis (emotion detection, aspect-based sentiment) through prompt-based classification without requiring separate fine-tuned models.","intents":["Analyze customer reviews or feedback to understand sentiment and identify common complaints","Classify support tickets by topic to route to appropriate teams","Detect emotions or attitudes in social media posts or user-generated content"],"best_for":["Customer success teams analyzing feedback and reviews","Content moderation teams categorizing user-generated content","Developers building sentiment-aware applications"],"limitations":["Sentiment analysis accuracy varies by domain — sarcasm and irony are frequently misclassified","Multi-label classification may assign too many or too few categories without explicit constraints","No confidence scores — model provides classifications without uncertainty estimates"],"requires":["API key for Mistral AI or OpenRouter","Text to classify","Optional: category definitions or examples for improved accuracy"],"input_types":["text","reviews","social media posts"],"output_types":["sentiment labels","category assignments","confidence explanations"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["API key for Mistral AI or OpenRouter access","HTTP client capable of streaming responses","Token counting library to stay within 32K limit","API key for Mistral AI or OpenRouter","Prompt engineering to explicitly specify desired output format","Optional: JSON schema library for post-generation validation","HTTP client supporting streaming (for streaming responses)","Network connectivity and HTTPS support","Code context provided as text (no AST parsing required)","Optional: linter or type checker for post-generation validation"],"failure_modes":["32K token context window may be insufficient for very long document analysis or 100+ turn conversations","Attention computation scales quadratically with context length, causing latency increases on maximum-length inputs","No built-in conversation summarization — full history must be maintained in prompt for optimal performance","Format adherence is probabilistic, not guaranteed — edge cases may produce malformed JSON or incomplete structures","Complex nested structures (deeply nested JSON, mixed format requests) have higher error rates than simple formats","No schema validation — model may produce syntactically valid but semantically incorrect structured data","Streaming adds latency for time-to-first-token compared to non-streaming requests","Batch processing requires requests to be queued — no guaranteed order of execution","Rate limiting may throttle requests during peak usage — requires backoff strategies","Code generation quality varies significantly by language — well-represented languages (Python, JavaScript) perform better than niche languages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.24,"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-large-2411","compare_url":"https://unfragile.ai/compare?artifact=mistralai-mistral-large-2411"}},"signature":"pYSczs4izDa3onXTChEFDdjfAebA0KeHJeX/ikLHg4i7pe9Jk0tnh8MI5kyPn645n+jLtlX1efGl7cwAqZpoBg==","signedAt":"2026-06-23T00:34:51.659Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-mistral-large-2411","artifact":"https://unfragile.ai/mistralai-mistral-large-2411","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-mistral-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"}}