{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-mistral-large","slug":"mistralai-mistral-large","name":"Mistral Large","type":"model","url":"https://openrouter.ai/models/mistralai~mistral-large","page_url":"https://unfragile.ai/mistralai-mistral-large","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__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.context.preservation","name":"multi-turn conversational reasoning with context preservation","description":"Mistral Large maintains conversation state across multiple turns using a transformer-based architecture with extended context windows, enabling coherent multi-step reasoning and dialogue without losing prior context. The model processes entire conversation histories as input sequences, applying attention mechanisms to weight relevant prior exchanges when generating responses, supporting both stateless API calls with explicit history and streaming token generation for real-time interaction.","intents":["Build a chatbot that remembers conversation context across 10+ exchanges without losing coherence","Implement multi-turn reasoning where the model references earlier statements to refine answers","Create an interactive assistant that can handle follow-up questions and clarifications naturally"],"best_for":["Teams building conversational AI products with complex dialogue flows","Developers implementing customer support chatbots requiring context awareness","Researchers prototyping multi-turn reasoning systems"],"limitations":["Context window is finite (32K tokens for Mistral Large 2407) — very long conversations require summarization or pruning strategies","No built-in conversation memory persistence — requires external database or session management to maintain state across API calls","Streaming responses add latency overhead (~50-200ms) compared to batch generation"],"requires":["API key for Mistral AI or OpenRouter access","HTTP client capable of handling streaming responses (optional but recommended)","Conversation history management logic in application layer"],"input_types":["text (natural language queries)","structured conversation arrays with role/content pairs (OpenAI-compatible format)"],"output_types":["text (streamed or buffered)","structured JSON when prompted with schema"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"Mistral Large generates syntactically correct code across 40+ programming languages by leveraging transformer-based token prediction trained on diverse code repositories, with special optimization for Python, JavaScript, Java, C++, and Go. The model understands code context, function signatures, and library APIs, enabling both completion of partial code snippets and generation of complete functions or modules from natural language specifications or docstrings.","intents":["Generate boilerplate code or function implementations from docstrings or comments","Complete partial code snippets with context-aware suggestions","Translate algorithms between programming languages","Generate test cases or helper functions for existing code"],"best_for":["Solo developers and small teams using IDE plugins or API-based code assistants","Teams building code generation tools or internal developer productivity platforms","Developers working in polyglot codebases requiring cross-language code generation"],"limitations":["No real-time syntax validation — generated code may contain logical errors or use deprecated APIs without explicit verification","Limited to code patterns seen in training data (cutoff date July 2024) — may not generate code using very recent library versions or frameworks","No built-in dependency resolution — generated code may reference non-existent packages or incorrect import paths","Context window limits prevent generating very large files (>8K tokens) without chunking strategies"],"requires":["API key for Mistral AI or OpenRouter","Code linter or type checker in CI/CD pipeline to validate generated code","IDE integration or custom client for seamless code insertion"],"input_types":["text (natural language descriptions or docstrings)","code (partial snippets with cursor position or markers)","structured prompts with language specification"],"output_types":["code (single functions, classes, or modules)","code with explanatory comments","multiple code variants when prompted"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_10","uri":"capability://planning.reasoning.few.shot.learning.and.in.context.adaptation","name":"few-shot learning and in-context adaptation","description":"Mistral Large adapts to new tasks and styles by learning from examples provided in the prompt (few-shot learning), without requiring fine-tuning or retraining. The model uses attention mechanisms to identify patterns in provided examples and applies them to new inputs, enabling rapid task adaptation and style transfer within a single API call. This is particularly effective for domain-specific terminology, output formatting, and specialized reasoning patterns.","intents":["Adapt the model to domain-specific terminology by providing examples (e.g., medical or legal jargon)","Teach the model a new output format by showing examples (e.g., 'generate responses in this specific JSON structure')","Implement zero-shot or few-shot classification by providing category examples","Create task-specific behavior without fine-tuning (e.g., 'respond like a pirate' with examples)"],"best_for":["Teams building customizable AI systems without fine-tuning infrastructure","Developers implementing rapid prototyping or A/B testing of different model behaviors","Organizations with domain-specific needs (medical, legal, technical) that can't justify fine-tuning"],"limitations":["Few-shot learning effectiveness depends on example quality — poor examples degrade performance","Context window limits restrict number of examples (typically 3-10 examples before context exhaustion)","Learning is not persistent — examples must be provided in every API call","Performance on very specialized tasks may be lower than fine-tuned models"],"requires":["API key for Mistral AI or OpenRouter","High-quality examples representative of desired behavior","Sufficient context window to accommodate examples + input + output"],"input_types":["text (examples demonstrating desired behavior)","structured examples (input/output pairs)","new input to apply learned pattern to"],"output_types":["text (adapted to learned pattern)","structured data (if format was demonstrated in examples)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_11","uri":"capability://tool.use.integration.api.response.formatting.and.openai.compatible.interface","name":"api response formatting and openai-compatible interface","description":"Mistral Large is accessible through OpenAI-compatible API endpoints (via OpenRouter or direct Mistral API), enabling drop-in replacement for OpenAI models in existing applications. The API supports streaming responses, function calling, and structured output modes, with response formatting matching OpenAI's chat completion format (messages array, role-based structure, token counting).","intents":["Migrate from OpenAI to Mistral without rewriting application code","Use Mistral as a cost-effective alternative in existing LLM applications","Implement multi-model support by swapping model identifiers","Leverage existing OpenAI client libraries (Python, JavaScript, etc.) with Mistral backend"],"best_for":["Teams already using OpenAI APIs looking to reduce costs or improve performance","Developers building multi-model applications with model abstraction layers","Organizations with existing OpenAI integrations seeking vendor flexibility"],"limitations":["Not 100% API-compatible — some advanced OpenAI features (vision, embeddings) may not be available","Response latency and token counting may differ slightly from OpenAI","Rate limiting and quota management differ between providers","Fine-tuning is not available through OpenAI-compatible interface"],"requires":["API key for Mistral AI or OpenRouter","OpenAI-compatible client library (e.g., openai Python package v1.0+)","Minimal code changes to swap model identifier and endpoint"],"input_types":["messages array (OpenAI format with role/content pairs)","function definitions (OpenAI-compatible schema)","system prompts"],"output_types":["chat completion responses (OpenAI format)","streaming token responses","function call objects"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_2","uri":"capability://data.processing.analysis.structured.json.and.schema.compliant.output.generation","name":"structured json and schema-compliant output generation","description":"Mistral Large can generate valid JSON and schema-compliant structured data by constraining token generation to follow specified JSON schemas or format patterns, using either prompt engineering (schema in system message) or native structured output modes if available through the API provider. The model understands JSON syntax deeply and can extract information from unstructured text, transform it into typed objects, and validate against provided schemas without requiring post-processing.","intents":["Extract structured data from unstructured text (e.g., parse customer feedback into JSON with sentiment, category, priority fields)","Generate API responses that conform to OpenAPI schemas","Transform data between formats (CSV to JSON, XML to structured objects)","Create configuration files or data fixtures in JSON format"],"best_for":["Teams building data extraction pipelines or ETL workflows","Developers implementing API endpoints that need to normalize user input into typed structures","Data engineers prototyping schema-driven data transformation tools"],"limitations":["Schema validation is best-effort — complex nested schemas may occasionally produce invalid JSON that requires post-processing","No built-in type coercion — if schema requires integer but model generates string, downstream validation fails","Large schemas (>2K tokens) consume significant context, reducing available space for input data","Hallucination risk for fields not present in source data — model may invent plausible but false values"],"requires":["API key for Mistral AI or OpenRouter","JSON schema definition (JSON Schema, OpenAPI, or custom format)","JSON parser in application layer for validation and error handling"],"input_types":["text (unstructured data to extract from)","JSON schema (as string in prompt or structured parameter)","structured prompts with example input/output pairs"],"output_types":["valid JSON objects","JSON arrays","nested structures with typed fields"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_3","uri":"capability://tool.use.integration.function.calling.and.tool.invocation.with.schema.based.routing","name":"function calling and tool invocation with schema-based routing","description":"Mistral Large supports function calling by accepting a list of tool/function definitions (with parameters and descriptions) in the API request, then generating structured function calls as part of its response when appropriate. The model understands function signatures, parameter types, and constraints, routing user intents to the correct function and populating arguments based on conversation context. This enables agentic workflows where the model decides which tools to invoke and in what sequence.","intents":["Build an agent that decides which API to call based on user request (e.g., 'book a flight' → call flight_booking_api)","Create a multi-step workflow where the model chains function calls (e.g., search → filter → book)","Implement a chatbot that can execute actions (send email, update database) based on user commands","Delegate specific tasks to specialized functions while maintaining conversational context"],"best_for":["Teams building AI agents or autonomous systems with external tool integration","Developers implementing chatbots that need to perform actions beyond text generation","Builders creating no-code/low-code automation platforms with LLM-driven logic"],"limitations":["Function calling is non-deterministic — model may choose wrong function or miss opportunities to call functions in ambiguous cases","No built-in error handling or retry logic — if a function call fails, the model doesn't automatically recover or try alternatives","Function definitions consume context tokens — complex tool sets (>20 functions) may reduce available space for conversation history","Requires careful prompt engineering to guide function selection — vague function descriptions lead to incorrect routing"],"requires":["API key for Mistral AI or OpenRouter with function calling support","Function definitions in OpenAI-compatible format (name, description, parameters schema)","Application logic to execute functions and return results back to the model","Error handling and validation layer for function arguments"],"input_types":["text (user request or command)","function definitions (JSON schema with name, description, parameters)","prior function call results (for multi-step workflows)"],"output_types":["function call objects (name + arguments)","text responses interspersed with function calls","structured tool use sequences"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_4","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Mistral Large demonstrates strong performance on mathematical problem-solving by applying chain-of-thought reasoning patterns learned during training, breaking down complex problems into steps and showing intermediate calculations. The model can handle algebra, calculus, statistics, and logic problems, though it relies on token-by-token generation rather than symbolic computation engines, making it suitable for reasoning tasks but not for arbitrary-precision arithmetic.","intents":["Solve word problems or math questions with step-by-step explanations","Verify mathematical derivations or proofs","Generate practice problems or quizzes with solutions","Explain mathematical concepts or theorems in natural language"],"best_for":["Educational platforms building AI tutors for math subjects","Developers creating homework help or test preparation tools","Teams building research assistants that need to validate mathematical claims"],"limitations":["Arithmetic errors accumulate in long calculations — not suitable for high-precision numerical work without external calculator integration","Cannot perform symbolic computation (e.g., simplify complex algebraic expressions) without explicit step-by-step guidance","May struggle with novel problem types not well-represented in training data","No access to external math libraries or computational tools — all reasoning is token-based"],"requires":["API key for Mistral AI or OpenRouter","Optional: external calculator or symbolic math library (SymPy, Mathematica) for verification","Prompt engineering to encourage step-by-step reasoning (e.g., 'show your work')"],"input_types":["text (math problems in natural language or LaTeX)","structured problem descriptions with constraints"],"output_types":["text (step-by-step solutions with explanations)","LaTeX or mathematical notation","structured solutions with intermediate steps"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_5","uri":"capability://text.generation.language.instruction.following.and.task.decomposition","name":"instruction following and task decomposition","description":"Mistral Large interprets complex, multi-part instructions and decomposes them into subtasks, maintaining fidelity to specified constraints (tone, format, length, style). The model uses attention mechanisms to track multiple requirements simultaneously and generates responses that satisfy all stated conditions, making it effective for tasks requiring precise adherence to specifications rather than creative interpretation.","intents":["Generate content that must follow strict formatting rules (e.g., 'write a 500-word essay in APA format with 3 sources')","Execute multi-step workflows described in natural language (e.g., 'analyze this data, create a summary, then suggest improvements')","Implement complex business logic described in prose (e.g., 'calculate discount based on customer tier and purchase history')","Create outputs with multiple constraints (tone, audience, length, style, technical level)"],"best_for":["Teams building content generation platforms with strict output requirements","Developers implementing workflow automation where tasks are specified in natural language","Enterprise users needing reliable instruction following for compliance or quality control"],"limitations":["Constraint satisfaction degrades with instruction complexity — >5 simultaneous constraints may be partially violated","Ambiguous or conflicting instructions may be interpreted inconsistently across API calls","No built-in verification that output actually satisfies all stated requirements — requires post-generation validation","Very long or nested instructions may exceed context window or lose fidelity in interpretation"],"requires":["API key for Mistral AI or OpenRouter","Clear, well-structured instructions (ambiguity reduces reliability)","Output validation logic to verify constraint satisfaction"],"input_types":["text (natural language instructions with constraints)","structured task specifications with required fields","examples of desired output format"],"output_types":["text (formatted according to specifications)","structured data (if format is specified)","multi-part responses (if task decomposition is required)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_6","uri":"capability://text.generation.language.knowledge.synthesis.and.information.summarization","name":"knowledge synthesis and information summarization","description":"Mistral Large synthesizes information from provided context (documents, articles, conversation history) to generate summaries, answer questions, or create new content that combines insights from multiple sources. The model uses attention mechanisms to identify relevant passages and integrates information across sources without requiring explicit retrieval or ranking steps, making it effective for in-context learning and few-shot prompting scenarios.","intents":["Summarize long documents or articles into concise overviews","Answer questions based on provided context (e.g., 'based on this document, what is the company's revenue growth?')","Synthesize insights from multiple sources to create comparative analyses","Extract key points or action items from meeting transcripts or reports"],"best_for":["Teams building document analysis or knowledge management tools","Developers implementing question-answering systems over proprietary documents","Researchers creating literature review or synthesis tools"],"limitations":["Context window limits prevent processing very large documents (>32K tokens) without chunking or summarization","No built-in relevance ranking — may weight irrelevant passages equally with important ones","Hallucination risk when synthesizing across sources — may invent connections or facts not present in source material","No persistent memory of previous documents — each API call is stateless unless conversation history is maintained"],"requires":["API key for Mistral AI or OpenRouter","Source documents or context provided in prompt (no built-in document storage)","Document chunking strategy for files exceeding context window"],"input_types":["text (documents, articles, or context passages)","natural language questions or summarization requests","structured prompts with examples of desired synthesis"],"output_types":["text (summaries, answers, or synthesized insights)","structured data (if format is specified)","bullet points or key takeaways"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_7","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.style.control","name":"creative writing and content generation with style control","description":"Mistral Large generates creative content (stories, poetry, marketing copy, dialogue) while respecting specified style constraints (tone, voice, genre, audience level). The model learns stylistic patterns from training data and applies them consistently across generated text, enabling both unconstrained creative generation and style-guided content creation for specific use cases.","intents":["Generate marketing copy or product descriptions with specific tone (professional, casual, humorous)","Create fictional stories or dialogue with consistent character voices","Write poetry or creative content in specified styles or genres","Generate multiple variations of content with different tones or approaches"],"best_for":["Content creators and marketing teams using AI to accelerate copywriting","Game developers generating NPC dialogue or narrative content","Educational platforms creating engaging learning materials"],"limitations":["Style consistency degrades in very long outputs (>2K tokens) — tone may drift or become inconsistent","Originality is limited to patterns in training data — truly novel styles or genres may not be achievable","No built-in fact-checking — creative content may contain false claims or outdated information","Requires careful prompt engineering to achieve desired style — vague style descriptions lead to generic output"],"requires":["API key for Mistral AI or OpenRouter","Clear style specifications or examples in prompt","Human review for fact-checking and quality control"],"input_types":["text (creative prompts or story seeds)","style specifications (tone, genre, audience, length)","examples of desired style or voice"],"output_types":["text (stories, poetry, marketing copy)","multiple variations of content","structured content (e.g., dialogue with speaker labels)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_8","uri":"capability://text.generation.language.multilingual.translation.and.cross.language.understanding","name":"multilingual translation and cross-language understanding","description":"Mistral Large supports translation between 50+ languages and demonstrates cross-language understanding, enabling it to answer questions about non-English content, translate code comments, and generate multilingual responses. The model uses shared token embeddings across languages and learns translation patterns during training, supporting both direct translation and code-switching (mixing languages in single response).","intents":["Translate documents or text between major languages","Answer questions about content in non-English languages","Generate multilingual customer support responses","Translate code comments or documentation"],"best_for":["Global teams building multilingual applications or support systems","Developers working with international codebases","Content platforms serving multiple language markets"],"limitations":["Translation quality varies significantly by language pair — high-resource pairs (English-French) are more accurate than low-resource pairs (English-Swahili)","Idioms and cultural context may be lost in translation — literal translation without cultural adaptation","No built-in language detection — must specify source and target languages or rely on model inference","Code-switching may produce mixed-language output when not explicitly requested"],"requires":["API key for Mistral AI or OpenRouter","Source and target language specification (recommended for accuracy)","Optional: language detection library for automatic source language identification"],"input_types":["text in any supported language","language pair specification (source → target)","context about domain or terminology preferences"],"output_types":["text in target language","multilingual responses (if requested)","code with translated comments"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-large__cap_9","uri":"capability://safety.moderation.adversarial.robustness.and.prompt.injection.resistance","name":"adversarial robustness and prompt injection resistance","description":"Mistral Large demonstrates resistance to common adversarial attacks and prompt injection attempts through training on adversarial examples and safety-focused datasets, though it is not immune to sophisticated attacks. The model maintains instruction fidelity even when user input contains conflicting directives, and it can identify and decline requests that violate safety guidelines without being easily tricked by obfuscation or jailbreak attempts.","intents":["Deploy a chatbot in production without excessive concern about prompt injection attacks","Build systems where user input cannot easily override system instructions","Create moderation-aware applications that decline harmful requests reliably","Implement guardrails that persist across multi-turn conversations"],"best_for":["Teams deploying LLMs in production with user-facing interfaces","Developers building systems handling sensitive data or high-stakes decisions","Security-conscious organizations requiring adversarial robustness"],"limitations":["No absolute immunity to prompt injection — sophisticated attacks may still succeed","Robustness varies by attack type — some jailbreak techniques are more effective than others","Safety training may reduce model helpfulness on edge cases or legitimate requests","No built-in audit logging or attack detection — requires external monitoring"],"requires":["API key for Mistral AI or OpenRouter","Input validation and sanitization in application layer (defense in depth)","Monitoring and logging for detecting potential attacks"],"input_types":["text (user input with potential adversarial content)","system instructions (to test override resistance)"],"output_types":["text (responses that maintain instruction fidelity)","refusal messages (when appropriate)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["API key for Mistral AI or OpenRouter access","HTTP client capable of handling streaming responses (optional but recommended)","Conversation history management logic in application layer","API key for Mistral AI or OpenRouter","Code linter or type checker in CI/CD pipeline to validate generated code","IDE integration or custom client for seamless code insertion","High-quality examples representative of desired behavior","Sufficient context window to accommodate examples + input + output","OpenAI-compatible client library (e.g., openai Python package v1.0+)","Minimal code changes to swap model identifier and endpoint"],"failure_modes":["Context window is finite (32K tokens for Mistral Large 2407) — very long conversations require summarization or pruning strategies","No built-in conversation memory persistence — requires external database or session management to maintain state across API calls","Streaming responses add latency overhead (~50-200ms) compared to batch generation","No real-time syntax validation — generated code may contain logical errors or use deprecated APIs without explicit verification","Limited to code patterns seen in training data (cutoff date July 2024) — may not generate code using very recent library versions or frameworks","No built-in dependency resolution — generated code may reference non-existent packages or incorrect import paths","Context window limits prevent generating very large files (>8K tokens) without chunking strategies","Few-shot learning effectiveness depends on example quality — poor examples degrade performance","Context window limits restrict number of examples (typically 3-10 examples before context exhaustion)","Learning is not persistent — examples must be provided in every API call","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.24,"match_graph":0.25,"freshness":0.9,"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":"2026-05-03T15:20:45.777Z","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","compare_url":"https://unfragile.ai/compare?artifact=mistralai-mistral-large"}},"signature":"IBS3i3sPy9mz7kh+6MNoBYCIKT3Y/EZXa4vZuxV55Hv5gFXHNGzvaza2AdNHXF/TwEO/yLATEUHmE/gebJ4PDw==","signedAt":"2026-06-16T07:00:37.611Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-mistral-large","artifact":"https://unfragile.ai/mistralai-mistral-large","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-mistral-large","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}