{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-mistral-nemo","slug":"mistralai-mistral-nemo","name":"Mistral: Mistral Nemo","type":"model","url":"https://openrouter.ai/models/mistralai~mistral-nemo","page_url":"https://unfragile.ai/mistralai-mistral-nemo","categories":["chatbots-assistants"],"tags":["mistralai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-mistral-nemo__cap_0","uri":"capability://text.generation.language.multilingual.text.generation.with.128k.context.window","name":"multilingual text generation with 128k context window","description":"Generates coherent, contextually-aware text across 9+ languages (English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, and others) using a 12B parameter transformer architecture with extended context handling via rotary position embeddings or similar mechanisms enabling 128k token sequences. The model processes input tokens through attention layers optimized for long-range dependencies, allowing it to maintain semantic coherence across documents, conversations, or code repositories that exceed typical 4k-8k context limits.","intents":["I need to process long documents or multi-turn conversations without losing context from earlier exchanges","I want to generate multilingual content without switching between different models for each language","I need to work with code files or technical documentation that exceed standard context windows","I want to maintain conversation history across extended interactions without manual summarization"],"best_for":["multilingual teams building chatbots or content generation systems","developers working with long-form documents, codebases, or research papers","organizations needing cost-efficient inference on mid-range hardware (12B is smaller than 70B+ models)"],"limitations":["128k context window still has practical limits for extremely large codebases or document collections — token counting overhead increases with context size","Multilingual support may have quality variance across languages — English and French likely stronger than less-represented languages","12B parameter size trades off reasoning depth vs. larger models (70B+) — may struggle with complex multi-step logical reasoning or specialized domains","Context window length increases latency and memory requirements — inference time scales with input+output token count"],"requires":["API access via OpenRouter or direct Mistral API endpoint","Valid authentication token (API key)","HTTP client capable of streaming responses (for real-time token generation)","Sufficient rate limits for production workloads (check OpenRouter pricing tier)"],"input_types":["text (UTF-8 encoded, any language in supported set)","code (treated as text, no special parsing)","structured prompts with system/user/assistant roles"],"output_types":["text (streaming or batch completion)","structured text (JSON, markdown, code when prompted)","multilingual responses matching input language or specified target language"],"categories":["text-generation-language","multilingual-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_1","uri":"capability://text.generation.language.streaming.token.generation.with.real.time.output","name":"streaming token generation with real-time output","description":"Generates text tokens sequentially and streams them to the client in real-time using server-sent events (SSE) or chunked HTTP responses, enabling progressive rendering of responses as they are generated rather than waiting for full completion. The model uses autoregressive decoding (sampling or beam search) to produce one token at a time, with each token immediately flushed to the client, reducing perceived latency and enabling interactive experiences like live chatbot responses or progressive code generation.","intents":["I want to show users text as it's being generated, not wait for the full response","I need to build interactive chatbots where users see typing-like feedback in real-time","I want to reduce time-to-first-token (TTFT) perception for better UX in web applications","I need to cancel or interrupt generation mid-stream if the user stops waiting or requests a different response"],"best_for":["web applications and chat interfaces requiring real-time user feedback","streaming API consumers building interactive LLM-powered products","developers optimizing for perceived latency and user engagement metrics"],"limitations":["Streaming responses cannot be easily cached or reused — each request generates new tokens","Token-by-token generation prevents global optimization strategies (e.g., beam search across full response) — uses greedy or local sampling instead","Network latency and buffering can create uneven token arrival times, requiring client-side smoothing for consistent UX","Streaming connections require persistent HTTP/2 or WebSocket support — incompatible with some legacy proxies or firewalls"],"requires":["HTTP client supporting streaming responses (fetch API with ReadableStream, axios with responseType: 'stream', etc.)","Server supporting Server-Sent Events (SSE) or chunked transfer encoding","Handling of stream termination and error recovery logic on client side"],"input_types":["text prompts (single or multi-turn conversation history)"],"output_types":["streamed text chunks (typically 1-10 tokens per chunk, format varies by API)","metadata (token count, finish reason, model name) in final chunk or separate message"],"categories":["text-generation-language","api-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_10","uri":"capability://planning.reasoning.reasoning.and.multi.step.problem.solving","name":"reasoning and multi-step problem solving","description":"Performs multi-step reasoning and problem-solving by generating intermediate reasoning steps (chain-of-thought) before arriving at final answers. The model can decompose complex problems, perform logical inference, and generate explanations of its reasoning process, though without explicit planning or search — relies on implicit reasoning patterns learned during training.","intents":["I want the model to explain its reasoning and show intermediate steps for complex problems","I need to solve problems that require multiple reasoning steps or logical inference","I want to verify the model's reasoning process and identify errors in logic","I need to perform mathematical reasoning, logical puzzles, or complex analysis"],"best_for":["educational applications requiring explanation of reasoning","problem-solving systems where transparency is important","applications requiring multi-step logical inference"],"limitations":["Reasoning quality is limited by model capacity — 12B model struggles with very complex reasoning compared to 70B+ models","No explicit search or planning — reasoning is implicit and may miss optimal solutions or contain logical errors","Chain-of-thought reasoning increases token usage and latency — each reasoning step consumes tokens","Reasoning patterns are learned from training data — model may reproduce common reasoning errors or biases present in training data"],"requires":["Prompts that encourage reasoning (e.g., 'think step by step', 'show your work')","Problems or queries that benefit from multi-step reasoning"],"input_types":["complex problems or queries requiring reasoning","prompts encouraging chain-of-thought or step-by-step explanation"],"output_types":["reasoning steps or intermediate conclusions","final answers with explanations","structured reasoning (e.g., numbered steps, bullet points)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_11","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Generates creative content (stories, poetry, marketing copy, dialogue, creative essays) by leveraging transformer patterns learned from diverse creative writing datasets. The model can adapt to specified styles, tones, and genres, and generate coherent, engaging content across multiple creative domains without explicit style transfer or fine-tuning.","intents":["I want to generate creative writing (stories, poetry, scripts) in specified styles or genres","I need to create marketing copy, product descriptions, or advertising content","I want to generate dialogue for characters or interactive fiction","I need to brainstorm creative ideas or generate variations on creative concepts"],"best_for":["content creation and marketing teams","creative writing and storytelling applications","game development and interactive fiction systems","brainstorming and ideation tools"],"limitations":["Creative output quality is subjective and varies widely — may produce clichéd, generic, or uninspired content","12B model has lower creative capacity than larger models — may struggle with complex narratives or nuanced character development","No access to real-world knowledge or current events — creative content may be outdated or inaccurate for contemporary settings","Repetition and mode collapse — model may generate similar content across multiple requests without sufficient prompt variation"],"requires":["Clear prompts specifying genre, style, tone, or creative constraints","Optionally, examples of desired style or tone for few-shot adaptation"],"input_types":["creative prompts or story premises","style or tone specifications","examples of desired creative output"],"output_types":["creative text (stories, poetry, scripts, marketing copy)","structured creative content (dialogue, character descriptions, plot outlines)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_2","uri":"capability://text.generation.language.few.shot.and.zero.shot.prompt.adaptation","name":"few-shot and zero-shot prompt adaptation","description":"Accepts structured prompts with system instructions, few-shot examples, and user queries, adapting its generation behavior based on in-context learning without fine-tuning. The model uses attention mechanisms to learn patterns from provided examples (few-shot) or follow explicit instructions (zero-shot), enabling rapid task adaptation for classification, extraction, summarization, code generation, and other tasks by simply reformatting the prompt rather than retraining or deploying new model weights.","intents":["I want to adapt the model to a specific task (e.g., sentiment analysis, code review) without fine-tuning","I need to provide examples of desired output format and have the model follow that pattern","I want to constrain the model's behavior with system instructions (e.g., 'respond in JSON format', 'be concise')","I need to perform one-off tasks without the overhead of creating a specialized model or prompt template"],"best_for":["rapid prototyping and experimentation with different prompting strategies","teams building flexible LLM applications that adapt to user-defined tasks","developers optimizing prompt engineering without ML infrastructure"],"limitations":["Few-shot learning quality degrades with very long examples or complex patterns — 12B model has lower capacity than 70B+ for learning from examples","No persistent learning — each request must include examples, increasing token usage and latency","Prompt engineering is brittle and sensitive to wording, example order, and formatting — requires iteration and testing","In-context learning has theoretical limits (typically 3-5 examples for reliable adaptation) before token budget is exhausted"],"requires":["Well-structured prompt with clear system instructions and examples","Understanding of prompt engineering best practices (example selection, formatting, instruction clarity)","Token budget to accommodate examples + user query (counts against 128k limit)"],"input_types":["structured text prompts with system role, examples, and user query","JSON or markdown formatted examples showing input-output pairs"],"output_types":["text matching the format/style demonstrated in examples","structured outputs (JSON, CSV, code) if examples show that format"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_3","uri":"capability://code.generation.editing.code.generation.and.technical.content.synthesis","name":"code generation and technical content synthesis","description":"Generates code snippets, technical documentation, and structured outputs by treating code as text and leveraging transformer attention to model programming language syntax and semantics. The model can generate code in multiple languages (Python, JavaScript, Java, C++, SQL, etc.), follow coding conventions, and produce working implementations based on natural language descriptions or code context, though without real-time compilation or execution feedback.","intents":["I want to generate code snippets from natural language descriptions (e.g., 'write a Python function to sort a list')","I need to complete code or fill in missing implementations based on context","I want to generate technical documentation, API examples, or SQL queries","I need to refactor or explain existing code by providing it as context"],"best_for":["developers using AI-assisted coding for rapid prototyping or boilerplate generation","technical writers generating code examples and documentation","teams building code generation pipelines or IDE integrations"],"limitations":["Generated code is not executed or validated — may contain syntax errors, logical bugs, or security vulnerabilities","12B model has lower code reasoning capacity than larger models (70B+) — struggles with complex algorithms, multi-file refactoring, or domain-specific patterns","No access to external libraries or package managers — generated code may reference non-existent packages or outdated APIs","Context window limits the amount of existing code that can be provided for analysis or completion — large codebases require chunking"],"requires":["Clear natural language description of desired code or code context to complete","Understanding that generated code requires review and testing before production use","Optionally, system prompt specifying programming language, style, or framework preferences"],"input_types":["natural language descriptions of desired code","partial code snippets to complete or refactor","code context (imports, function signatures, comments) for in-context learning"],"output_types":["code snippets in specified or inferred programming language","multi-line code blocks with comments and documentation","structured code (JSON, YAML, SQL) when requested"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_4","uri":"capability://text.generation.language.conversation.history.management.and.multi.turn.dialogue","name":"conversation history management and multi-turn dialogue","description":"Maintains semantic coherence across multiple turns of conversation by accepting conversation history as input (array of system/user/assistant messages) and generating contextually-aware responses that reference earlier exchanges. The model uses attention mechanisms to weight relevant historical context, enabling natural dialogue flows where the model can refer back to previous statements, maintain consistent persona, and build on earlier reasoning without explicit summarization or context compression.","intents":["I want to build a chatbot that remembers earlier parts of the conversation and refers back to them naturally","I need to maintain conversation state across multiple API calls without manually managing context","I want the model to adapt its responses based on the conversation history and user preferences established earlier","I need to implement multi-turn reasoning where each response builds on previous exchanges"],"best_for":["chatbot and conversational AI applications","customer support systems requiring context awareness","interactive tutoring or coaching systems with multi-turn interactions"],"limitations":["Conversation history counts against the 128k token limit — very long conversations will eventually exceed context and require truncation or summarization","No built-in persistence — conversation history must be stored and managed by the application (database, session store, etc.)","Token counting for conversation history is non-trivial — each turn adds overhead, and developers must track cumulative token usage","Model may 'forget' or deprioritize very early turns in long conversations due to attention distribution — recency bias is inherent to transformer architecture"],"requires":["Application-level conversation state management (storing and retrieving message history)","Token counting logic to track cumulative usage and avoid exceeding context limits","Structured message format (system/user/assistant roles) compatible with OpenRouter API"],"input_types":["conversation history as array of messages with role (system/user/assistant) and content","new user message to respond to"],"output_types":["assistant message continuing the conversation","structured response (JSON, code, etc.) if conversation context specifies format"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_5","uri":"capability://text.generation.language.multilingual.translation.and.cross.language.content.generation","name":"multilingual translation and cross-language content generation","description":"Translates text between supported languages (English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, etc.) and generates original content in specified target languages using transformer-based sequence-to-sequence patterns. The model leverages multilingual training data and shared embedding spaces to map semantic meaning across languages, enabling both translation of existing content and generation of new content in non-English languages without language-specific model switching.","intents":["I want to translate user-generated content or documents into multiple languages for global audiences","I need to generate marketing copy, customer support responses, or documentation in multiple languages from a single prompt","I want to detect the language of input and respond in the same language or a specified target language","I need to handle multilingual customer interactions without deploying separate language-specific models"],"best_for":["global SaaS platforms serving multilingual user bases","content creation and localization workflows","customer support systems handling multiple languages","organizations reducing ML infrastructure by consolidating language-specific models"],"limitations":["Translation quality varies across language pairs — English↔French likely stronger than English↔Japanese or low-resource languages","No specialized terminology handling — technical or domain-specific terms may be mistranslated without additional context or fine-tuning","Cultural nuances and idioms may not translate accurately — requires human review for marketing or sensitive content","Multilingual training may reduce per-language performance compared to language-specific models — quality tradeoff for convenience"],"requires":["Specification of target language in prompt or system instructions","Input text in one of the 9+ supported languages","Optionally, domain context or terminology glossary to improve translation accuracy"],"input_types":["text in any supported language","structured prompts specifying source and target languages"],"output_types":["translated text in target language","original content generated in target language"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_6","uri":"capability://text.generation.language.structured.output.generation.with.format.constraints","name":"structured output generation with format constraints","description":"Generates structured outputs (JSON, YAML, CSV, XML, code) by using prompt engineering and few-shot examples to constrain the model's output format without explicit schema validation or grammar-based generation. The model learns from examples or instructions to produce valid structured data, though without hard guarantees — output may occasionally deviate from the specified format and requires post-processing validation.","intents":["I want to extract structured data from unstructured text (e.g., extract entities into JSON)","I need to generate API responses, configuration files, or data in a specific format","I want to parse natural language into structured commands or function calls","I need to generate synthetic structured data for testing or training purposes"],"best_for":["data extraction and ETL pipelines using LLMs","API backends generating structured responses from natural language","testing and synthetic data generation workflows"],"limitations":["No hard schema validation — generated output may be invalid JSON, missing required fields, or malformed. Requires post-processing validation and error handling.","Format compliance degrades with complex schemas or deeply nested structures — simpler schemas (flat JSON, CSV) more reliable than complex hierarchies","12B model may struggle with large or complex structured outputs — larger models (70B+) more reliable for intricate data generation","Prompt engineering required to achieve consistent formatting — no declarative schema specification like JSON Schema or Pydantic models"],"requires":["Clear examples or instructions specifying desired output format","Post-processing validation logic to handle malformed outputs","Optionally, retry logic or fallback parsing strategies for robustness"],"input_types":["natural language descriptions or unstructured text to extract/transform","format specifications via examples or instructions"],"output_types":["JSON, YAML, CSV, XML, or other structured text formats","code or configuration files in specified format"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_7","uri":"capability://text.generation.language.summarization.and.content.condensation","name":"summarization and content condensation","description":"Condenses long-form text (documents, articles, conversations, code) into shorter summaries while preserving key information and semantic meaning. The model uses attention mechanisms to identify salient content and generate abstractive summaries (paraphrasing) rather than extractive summaries (copying), enabling flexible summary lengths and styles based on prompt specifications.","intents":["I want to summarize long documents or articles for quick consumption","I need to extract key points from meeting transcripts or conversation logs","I want to generate executive summaries or abstracts for research papers or technical documentation","I need to condense code reviews or pull request descriptions into actionable summaries"],"best_for":["document management and knowledge management systems","meeting transcription and note-taking applications","research and academic workflows requiring abstract generation","technical documentation and code review systems"],"limitations":["Abstractive summarization may hallucinate or introduce inaccuracies not present in the original text — requires human review for critical content","Summary quality depends on input clarity and structure — poorly written or ambiguous source material produces poor summaries","12B model may miss nuanced details or context compared to larger models — important for technical or specialized domains","Summarization length is difficult to control precisely — prompt-based length specifications (e.g., 'summarize in 100 words') are approximate"],"requires":["Source text to summarize (within 128k token limit)","Optional: summary length or style specifications in prompt"],"input_types":["long-form text (documents, articles, transcripts, code)","structured prompts specifying summary length, style, or focus areas"],"output_types":["abstractive summary (paraphrased, shorter text)","bullet-point summaries or structured summaries (JSON, markdown)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_8","uri":"capability://text.generation.language.question.answering.over.provided.context","name":"question-answering over provided context","description":"Answers questions about provided context (documents, code, conversations, knowledge bases) by using attention mechanisms to locate relevant information and generate answers grounded in the source material. The model can answer factual questions, perform reasoning over context, and cite or reference specific parts of the source material, though without explicit retrieval ranking or relevance scoring — relies on implicit attention-based relevance.","intents":["I want to build a Q&A system over internal documentation or knowledge bases","I need to answer user questions about uploaded documents or code repositories","I want to implement a chatbot that answers questions grounded in specific source material","I need to perform reasoning or inference over provided context to answer complex questions"],"best_for":["internal knowledge base and documentation Q&A systems","customer support systems with access to knowledge bases","document-based Q&A applications (e.g., PDF Q&A, research paper Q&A)","code documentation and API reference systems"],"limitations":["No explicit retrieval or ranking — if relevant information is buried in long context, the model may miss it or provide incomplete answers","Hallucination risk — model may generate plausible-sounding answers not grounded in provided context, especially if context is ambiguous or incomplete","Context must be provided in full — no built-in vector search or semantic retrieval. For large knowledge bases, requires external RAG (retrieval-augmented generation) system","Answer quality degrades with very long context (approaching 128k limit) — attention becomes diffuse and relevant information may be deprioritized"],"requires":["Relevant source material provided in context (documents, code, knowledge base entries)","Clear question or query","Optionally, system prompt specifying answer format or citation requirements"],"input_types":["source context (documents, code, structured data)","natural language questions or queries"],"output_types":["natural language answers grounded in context","answers with citations or references to source material","structured answers (JSON, markdown) if format is specified"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-nemo__cap_9","uri":"capability://text.generation.language.instruction.following.and.task.adaptation","name":"instruction-following and task adaptation","description":"Follows explicit instructions and adapts behavior based on system prompts, role specifications, and task descriptions without fine-tuning or retraining. The model uses instruction-tuned training to interpret and execute a wide range of tasks (writing, analysis, coding, reasoning, creative tasks) based on natural language specifications, enabling flexible task adaptation through prompt engineering alone.","intents":["I want to specify a task or role (e.g., 'act as a Python expert', 'write in the style of X') and have the model adapt accordingly","I need to constrain the model's behavior with instructions (e.g., 'be concise', 'use technical language', 'avoid harmful content')","I want to perform diverse tasks (writing, analysis, coding, reasoning) with a single model by changing the prompt","I need the model to follow specific output formats or response structures specified in instructions"],"best_for":["flexible, task-agnostic LLM applications","rapid prototyping and experimentation with different tasks","teams building multi-purpose AI assistants or copilots"],"limitations":["Instruction-following quality varies with instruction clarity and complexity — ambiguous or conflicting instructions produce inconsistent results","No hard constraints — instructions are suggestions, not guarantees. Model may ignore or misinterpret instructions, especially if they conflict with training data patterns.","Instruction injection attacks are possible — user-provided instructions may override system instructions if not carefully managed","12B model has lower instruction-following capacity than larger models (70B+) — struggles with complex, multi-step instructions or nuanced constraints"],"requires":["Clear, well-written system instructions or role specifications","Understanding of prompt engineering best practices (clarity, specificity, examples)","Optionally, input validation or instruction sanitization to prevent injection attacks"],"input_types":["system instructions or role specifications","task descriptions or user queries"],"output_types":["responses adapted to specified task or role","outputs following specified format or style constraints"],"categories":["text-generation-language","planning-reasoning"],"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","Valid authentication token (API key)","HTTP client capable of streaming responses (for real-time token generation)","Sufficient rate limits for production workloads (check OpenRouter pricing tier)","HTTP client supporting streaming responses (fetch API with ReadableStream, axios with responseType: 'stream', etc.)","Server supporting Server-Sent Events (SSE) or chunked transfer encoding","Handling of stream termination and error recovery logic on client side","Prompts that encourage reasoning (e.g., 'think step by step', 'show your work')","Problems or queries that benefit from multi-step reasoning","Clear prompts specifying genre, style, tone, or creative constraints"],"failure_modes":["128k context window still has practical limits for extremely large codebases or document collections — token counting overhead increases with context size","Multilingual support may have quality variance across languages — English and French likely stronger than less-represented languages","12B parameter size trades off reasoning depth vs. larger models (70B+) — may struggle with complex multi-step logical reasoning or specialized domains","Context window length increases latency and memory requirements — inference time scales with input+output token count","Streaming responses cannot be easily cached or reused — each request generates new tokens","Token-by-token generation prevents global optimization strategies (e.g., beam search across full response) — uses greedy or local sampling instead","Network latency and buffering can create uneven token arrival times, requiring client-side smoothing for consistent UX","Streaming connections require persistent HTTP/2 or WebSocket support — incompatible with some legacy proxies or firewalls","Reasoning quality is limited by model capacity — 12B model struggles with very complex reasoning compared to 70B+ models","No explicit search or planning — reasoning is implicit and may miss optimal solutions or contain logical errors","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":0.75,"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-nemo","compare_url":"https://unfragile.ai/compare?artifact=mistralai-mistral-nemo"}},"signature":"tE7mAlJqyUhMXk+pCnX3cxvS7GZiOs1Ay+IuMBF870DTFBLau5kuTHyMh+GojK6EqJV7DiLw6kWPfBPKQ3enAA==","signedAt":"2026-06-20T19:01:42.053Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-mistral-nemo","artifact":"https://unfragile.ai/mistralai-mistral-nemo","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-mistral-nemo","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"}}