{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-ministral-14b-2512","slug":"mistralai-ministral-14b-2512","name":"Mistral: Ministral 3 14B 2512","type":"model","url":"https://openrouter.ai/models/mistralai~ministral-14b-2512","page_url":"https://unfragile.ai/mistralai-ministral-14b-2512","categories":["llm-apis"],"tags":["mistralai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-ministral-14b-2512__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.context.window.management","name":"multi-turn conversational reasoning with context window management","description":"Processes sequential user messages with full conversation history retention, maintaining semantic coherence across turns through transformer-based attention mechanisms. Implements sliding-window context management to handle extended dialogues within a 32K token context window, enabling stateful reasoning across multiple exchanges without losing prior conversation state or logical continuity.","intents":["Build a multi-turn chatbot that remembers conversation history and maintains context across 50+ exchanges","Create an interactive debugging assistant that can reference previous error messages and code snippets from earlier in the conversation","Develop a customer support agent that tracks customer issues across multiple messages without requiring explicit context re-injection"],"best_for":["Teams building conversational AI applications with extended user interactions","Developers creating stateful chatbots that need to maintain coherence without external memory systems","Builders prototyping interactive agents where conversation history is critical to response quality"],"limitations":["32K token context window limits conversation length before older messages are lost; conversations exceeding ~8,000 words may require external summarization","No built-in conversation persistence — requires external database to store and retrieve conversation history across sessions","Attention mechanism scales quadratically with context length, causing latency increases (~50-100ms per 10K additional tokens) as conversations grow"],"requires":["API access via OpenRouter or direct Mistral API endpoint","HTTP client capable of streaming or polling responses","Message formatting following OpenAI-compatible chat completion schema"],"input_types":["text (user messages)","structured conversation history (array of role/content pairs)"],"output_types":["text (assistant response)","streaming tokens (via SSE or chunked transfer encoding)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_1","uri":"capability://text.generation.language.instruction.following.with.structured.output.formatting","name":"instruction-following with structured output formatting","description":"Interprets natural language instructions and system prompts to generate responses in specified formats (JSON, XML, markdown, code blocks, etc.) through fine-tuning on instruction-following datasets. Uses prompt engineering patterns and token-level constraints to enforce output schema compliance, enabling deterministic structured responses suitable for downstream parsing and programmatic consumption.","intents":["Generate JSON responses from natural language queries for API integration without manual parsing","Create markdown documentation from unstructured requirements or code comments","Extract structured data (entities, relationships, classifications) from free-form text input"],"best_for":["Developers building LLM-powered data extraction pipelines","Teams integrating LLM outputs directly into structured workflows without post-processing","Builders prototyping applications where output format consistency is critical"],"limitations":["No guaranteed schema validation — model may occasionally deviate from requested format, requiring fallback parsing or retry logic","Complex nested structures (deeply nested JSON, recursive schemas) have higher failure rates; simple flat structures are most reliable","Format compliance degrades with very long outputs (>2K tokens); structured formatting becomes less consistent as response length increases"],"requires":["Clear system prompt or instruction specifying desired output format","API access to model via OpenRouter or Mistral endpoint","Client-side validation/retry logic to handle occasional format violations"],"input_types":["text (natural language instruction)","text (system prompt defining output schema)"],"output_types":["JSON","XML","markdown","code blocks","plain text with structured delimiters"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_2","uri":"capability://code.generation.editing.code.generation.and.completion.with.language.agnostic.support","name":"code generation and completion with language-agnostic support","description":"Generates syntactically correct code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) using transformer-based code understanding trained on large open-source repositories. Supports both full-function generation from docstrings and inline completion for partial code, with context-aware token prediction that respects language-specific syntax rules and common library patterns.","intents":["Generate boilerplate code or utility functions from natural language descriptions","Complete partial code snippets with context-aware suggestions that match existing code style","Translate code logic between programming languages while preserving algorithmic intent"],"best_for":["Solo developers building prototypes across multiple languages","Teams using Mistral as a code-generation backend in IDE plugins or CI/CD pipelines","Builders creating code-to-code transformation tools or language migration utilities"],"limitations":["Generated code may contain logical errors or inefficiencies; always requires human review and testing before production use","No built-in knowledge of proprietary or internal libraries; performs best with widely-used open-source frameworks","Context window limits prevent generation of very large files (>4K lines); multi-file generation requires separate API calls per file","No real-time linting or syntax validation; relies on downstream tools to catch syntax errors"],"requires":["API access to Mistral via OpenRouter or direct endpoint","Programming language specified in prompt or inferred from file extension","Optional: code context (surrounding functions, imports) for better completion accuracy"],"input_types":["text (natural language description or docstring)","code (partial code snippet for completion)","structured metadata (language, framework, style guidelines)"],"output_types":["code (function, class, or file)","code comments and documentation","multiple code variants (if requested)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_3","uri":"capability://planning.reasoning.semantic.reasoning.with.chain.of.thought.decomposition","name":"semantic reasoning with chain-of-thought decomposition","description":"Performs multi-step logical reasoning by generating intermediate reasoning steps before producing final answers, using transformer-based token prediction to simulate step-by-step problem decomposition. Trained on reasoning datasets (math, logic puzzles, code analysis) to naturally produce 'thinking' tokens that break complex problems into manageable sub-problems, improving accuracy on tasks requiring multi-hop reasoning.","intents":["Solve multi-step math or logic problems with transparent reasoning steps","Analyze code for bugs by walking through execution flow step-by-step","Answer complex questions requiring synthesis of multiple information sources"],"best_for":["Developers building reasoning-heavy applications (tutoring systems, code analysis tools)","Teams needing explainable AI outputs where reasoning steps are valuable for debugging or user trust","Builders creating agents that must decompose complex tasks before execution"],"limitations":["Chain-of-thought reasoning increases token generation by 2-5x, raising latency and API costs proportionally","Reasoning quality degrades on highly specialized domains (proprietary algorithms, domain-specific math) where training data is sparse","No guarantee of correct reasoning — intermediate steps may contain logical errors even if final answer appears correct","Reasoning tokens are generated sequentially, preventing parallelization of reasoning steps"],"requires":["Prompt structure that encourages step-by-step reasoning (e.g., 'Let me think through this step by step')","API access with sufficient token budget to accommodate 2-5x token overhead","Client capable of parsing and displaying intermediate reasoning steps"],"input_types":["text (problem statement or question)","structured context (relevant facts, constraints, examples)"],"output_types":["text (reasoning steps)","text (final answer)","structured reasoning trace (if parsed from response)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_4","uri":"capability://memory.knowledge.knowledge.grounded.text.generation.with.factual.consistency","name":"knowledge-grounded text generation with factual consistency","description":"Generates text responses grounded in provided context or knowledge documents, using attention mechanisms to reference specific passages and maintain factual consistency with source material. Implements context-aware generation where the model learns to cite or reference provided information rather than hallucinating, reducing false claims through training on question-answering datasets with explicit source attribution.","intents":["Build a customer support chatbot that answers questions using company documentation without fabricating policies","Create a research assistant that summarizes papers while citing specific sections","Generate product descriptions from structured data without inventing features"],"best_for":["Teams building fact-critical applications (customer support, legal/compliance, medical information)","Developers creating RAG (Retrieval-Augmented Generation) systems where grounding is essential","Builders needing to reduce hallucination risk in production LLM applications"],"limitations":["Hallucination is reduced but not eliminated — model may still invent details not present in provided context","Context length limits prevent grounding on very large documents (>20K tokens); requires document chunking and retrieval","No explicit citation mechanism — model may reference context implicitly without clear attribution, requiring post-processing to extract sources","Performance degrades when provided context is contradictory or ambiguous; model may default to training data rather than context"],"requires":["Relevant context documents or knowledge base passages provided in prompt","Clear instructions to reference provided context and avoid external knowledge","API access to model via OpenRouter or Mistral endpoint","Optional: external retrieval system (vector DB, BM25 search) to select relevant context passages"],"input_types":["text (user query or question)","text (context documents or knowledge passages)","structured metadata (document titles, sources, relevance scores)"],"output_types":["text (grounded response)","text with implicit citations (references to context)","structured response with source attribution (if post-processed)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_5","uri":"capability://text.generation.language.multilingual.text.generation.and.translation.with.cross.lingual.understanding","name":"multilingual text generation and translation with cross-lingual understanding","description":"Generates and translates text across 50+ languages using multilingual transformer embeddings trained on diverse language corpora. Supports both direct translation (source-to-target) and cross-lingual reasoning where the model understands semantic meaning across languages, enabling tasks like 'answer this question in Spanish' or 'summarize this French document in English' with semantic preservation rather than word-for-word translation.","intents":["Translate user-generated content across multiple languages for global applications","Answer questions in the user's preferred language regardless of training data language","Summarize or analyze documents in one language and output in another"],"best_for":["Teams building globally-distributed applications serving multiple language markets","Developers creating multilingual chatbots or customer support systems","Builders needing cost-effective translation without dedicated translation APIs"],"limitations":["Translation quality varies significantly by language pair; high-resource languages (English, Spanish, French) are more accurate than low-resource languages (Icelandic, Swahili)","Idioms and cultural references may not translate accurately; requires human review for marketing or sensitive content","No explicit language detection — ambiguous text may be misidentified, requiring explicit language specification in prompt","Performance degrades on code-mixed text (mixing multiple languages in single document) or transliteration tasks"],"requires":["API access to Mistral model","Source language specified or inferred from input","Target language explicitly specified in prompt","Optional: language-specific tokenizer for optimal performance on non-Latin scripts"],"input_types":["text (any language)","structured metadata (source language, target language)"],"output_types":["text (translated or generated in target language)","multilingual response (if code-switching is requested)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_6","uri":"capability://tool.use.integration.api.integration.and.function.calling.with.schema.based.dispatch","name":"api integration and function calling with schema-based dispatch","description":"Executes external API calls and tool invocations through structured function-calling interface, where the model predicts function names and parameters as structured JSON based on user intent. Implements schema-based dispatch where function signatures are provided as context, enabling the model to select appropriate tools and format parameters correctly for downstream execution without requiring explicit prompt engineering for each tool.","intents":["Build an agent that can call weather APIs, database queries, or third-party services based on user requests","Create a workflow automation system where the model decides which tools to invoke and in what order","Develop a chatbot that can perform actions (send emails, create calendar events) by calling backend APIs"],"best_for":["Teams building agentic systems that need to orchestrate multiple tools","Developers creating automation workflows where tool selection is dynamic","Builders integrating LLMs with existing API ecosystems (CRM, databases, third-party services)"],"limitations":["Model may hallucinate function names or parameters not in provided schema; requires validation before execution","No built-in error handling — failed API calls require explicit retry logic and error message feedback to model","Function calling adds latency (additional token generation for structured output) compared to pure text generation","Complex parameter types (nested objects, arrays) may be formatted incorrectly; simple flat parameters are most reliable","No transaction support — multiple sequential tool calls cannot be rolled back if later calls fail"],"requires":["Function schema definitions (names, parameters, descriptions) provided in system prompt or context","API endpoints or tool implementations available for execution","Client capable of parsing function-call responses and executing tools","Error handling and retry logic for failed tool invocations"],"input_types":["text (user request or intent)","structured function schemas (JSON schema format)","execution results from previous tool calls (for multi-step workflows)"],"output_types":["structured function calls (JSON with function name and parameters)","text response (if model chooses to respond without tool use)","execution results (if client executes tools and returns results)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_7","uri":"capability://safety.moderation.content.moderation.and.safety.filtering.with.configurable.thresholds","name":"content moderation and safety filtering with configurable thresholds","description":"Evaluates text for harmful content (hate speech, violence, sexual content, misinformation) using learned safety classifiers and can refuse to generate harmful content based on configurable safety guidelines. Implements safety filtering through training on moderation datasets and explicit refusal patterns, enabling the model to decline requests for illegal content, personal information exposure, or other harmful outputs while maintaining usability for legitimate requests.","intents":["Deploy a chatbot in production that refuses to generate hateful content or assist with illegal activities","Moderate user-generated content at scale by classifying harmful inputs","Build applications where safety compliance is required (healthcare, finance, education)"],"best_for":["Teams deploying LLMs in regulated industries (healthcare, finance, education)","Developers building public-facing applications requiring content moderation","Builders needing to reduce legal/compliance risk from harmful model outputs"],"limitations":["Safety filtering is not perfect — model may still generate harmful content in edge cases or when prompted with adversarial techniques","Overly aggressive safety settings may refuse legitimate requests (e.g., refusing to discuss historical atrocities for educational purposes)","No fine-grained control over safety thresholds via API — safety behavior is fixed at model level, not configurable per request","Safety filtering adds latency (~50-100ms) due to additional classification overhead","Adversarial prompts (jailbreaks, prompt injection) may bypass safety mechanisms"],"requires":["API access to Mistral model with safety features enabled","Understanding of model's safety guidelines and refusal patterns","Client-side logging and monitoring to detect safety-related refusals"],"input_types":["text (user input or request)"],"output_types":["text (response or refusal message)","safety classification (if exposed via API)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_8","uri":"capability://text.generation.language.long.document.summarization.with.abstractive.and.extractive.modes","name":"long-document summarization with abstractive and extractive modes","description":"Condenses long documents (up to 32K tokens) into concise summaries using abstractive summarization (generating new text capturing key ideas) or extractive summarization (selecting and reordering important sentences). Implements both modes through transformer-based attention that learns to identify salient information and generate coherent summaries, with configurable summary length and detail level.","intents":["Summarize research papers, legal documents, or meeting transcripts into executive summaries","Extract key points from long articles for quick consumption","Generate table-of-contents or outline from unstructured documents"],"best_for":["Teams processing large volumes of documents (legal discovery, research synthesis, content curation)","Developers building document management systems with automatic summarization","Builders creating knowledge workers' tools (research assistants, legal tech)"],"limitations":["Abstractive summaries may contain minor factual errors or omissions; extractive summaries are more faithful but less coherent","Summary quality degrades for highly technical documents with domain-specific terminology","No guarantee of capturing all important information — model may miss nuanced details or context","Summarization adds latency proportional to document length; 32K token documents may take 10-30 seconds","No built-in multi-document summarization — requires separate calls per document"],"requires":["Full document text (up to 32K tokens)","Optional: summary length specification (e.g., 'summarize in 100 words')","Optional: domain context or key topics to emphasize","API access to Mistral model"],"input_types":["text (full document)","structured metadata (document type, domain, desired summary length)"],"output_types":["text (abstractive summary)","text (extractive summary with source citations)","structured summary (if post-processed into sections)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-14b-2512__cap_9","uri":"capability://memory.knowledge.question.answering.over.documents.with.retrieval.augmented.generation","name":"question-answering over documents with retrieval-augmented generation","description":"Answers questions about provided documents by combining retrieval (identifying relevant passages) with generation (synthesizing answers from those passages). Implements RAG pattern where document passages are provided as context, and the model generates answers grounded in those passages using attention mechanisms to reference specific sections while maintaining answer coherence.","intents":["Build a document Q&A system where users ask questions about uploaded PDFs or documents","Create a knowledge base chatbot that answers questions using company documentation","Develop a research assistant that answers questions about academic papers"],"best_for":["Teams building document-centric applications (knowledge bases, help systems, research tools)","Developers creating RAG systems with existing vector databases","Builders needing to ground answers in specific documents without hallucination"],"limitations":["Answer quality depends heavily on retrieval quality — if relevant passages aren't provided, answers will be poor","Model may still hallucinate details not in provided documents, especially for complex questions","No explicit citation mechanism — answers may reference documents implicitly without clear source attribution","Performance degrades when documents are poorly structured or contain contradictory information","Requires external retrieval system (vector DB, BM25 search) for production use; context-only approach doesn't scale to large document collections"],"requires":["Relevant document passages or context provided in prompt","External retrieval system (vector database, BM25 search, or similar) to select relevant passages","API access to Mistral model","Question in natural language"],"input_types":["text (user question)","text (document passages or context)","structured metadata (document titles, sources, relevance scores)"],"output_types":["text (answer grounded in documents)","text with implicit citations (references to context)","structured answer with source attribution (if post-processed)"],"categories":["memory-knowledge","search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Mistral API endpoint","HTTP client capable of streaming or polling responses","Message formatting following OpenAI-compatible chat completion schema","Clear system prompt or instruction specifying desired output format","API access to model via OpenRouter or Mistral endpoint","Client-side validation/retry logic to handle occasional format violations","API access to Mistral via OpenRouter or direct endpoint","Programming language specified in prompt or inferred from file extension","Optional: code context (surrounding functions, imports) for better completion accuracy","Prompt structure that encourages step-by-step reasoning (e.g., 'Let me think through this step by step')"],"failure_modes":["32K token context window limits conversation length before older messages are lost; conversations exceeding ~8,000 words may require external summarization","No built-in conversation persistence — requires external database to store and retrieve conversation history across sessions","Attention mechanism scales quadratically with context length, causing latency increases (~50-100ms per 10K additional tokens) as conversations grow","No guaranteed schema validation — model may occasionally deviate from requested format, requiring fallback parsing or retry logic","Complex nested structures (deeply nested JSON, recursive schemas) have higher failure rates; simple flat structures are most reliable","Format compliance degrades with very long outputs (>2K tokens); structured formatting becomes less consistent as response length increases","Generated code may contain logical errors or inefficiencies; always requires human review and testing before production use","No built-in knowledge of proprietary or internal libraries; performs best with widely-used open-source frameworks","Context window limits prevent generation of very large files (>4K lines); multi-file generation requires separate API calls per file","No real-time linting or syntax validation; relies on downstream tools to catch syntax errors","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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-ministral-14b-2512","compare_url":"https://unfragile.ai/compare?artifact=mistralai-ministral-14b-2512"}},"signature":"tCLxoc5EVzQorbwuKcr4kPrJSOyDrY+5UeE58uSv3lHkcLlarz6oKZQ4fsIYARYoPXmOxPNU8Hnwpa6+fXWUAg==","signedAt":"2026-06-20T08:07:06.487Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-ministral-14b-2512","artifact":"https://unfragile.ai/mistralai-ministral-14b-2512","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-ministral-14b-2512","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"}}