{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501","slug":"mistralai-mistral-small-24b-instruct-2501","name":"Mistral: Mistral Small 3","type":"model","url":"https://openrouter.ai/models/mistralai~mistral-small-24b-instruct-2501","page_url":"https://unfragile.ai/mistralai-mistral-small-24b-instruct-2501","categories":["llm-apis"],"tags":["mistralai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_0","uri":"capability://text.generation.language.instruction.tuned.conversational.response.generation","name":"instruction-tuned conversational response generation","description":"Generates contextually appropriate responses to multi-turn conversations using a 24B parameter transformer architecture fine-tuned on instruction-following datasets. The model processes input tokens through attention mechanisms optimized for low-latency inference, producing coherent text completions that maintain conversation context across multiple exchanges without explicit memory management.","intents":["Build a chatbot that responds naturally to user queries without hallucinating","Create a conversational AI assistant that understands nuanced instructions","Deploy a lightweight chat interface that runs with minimal latency overhead"],"best_for":["Teams building cost-conscious chatbot applications requiring sub-second response times","Developers deploying on resource-constrained infrastructure (edge devices, serverless functions)","Organizations needing Apache 2.0 licensed models for commercial use without restrictions"],"limitations":["Context window limited to ~8K tokens, requiring conversation truncation for long multi-turn exchanges","No built-in memory persistence across sessions — requires external state management for conversation history","24B parameter size means lower reasoning depth compared to 70B+ models on complex multi-step problems","Instruction-tuning optimized for common tasks; may underperform on highly specialized domain-specific instructions"],"requires":["API key for OpenRouter or direct Mistral API access","HTTP client capable of streaming responses (for real-time token generation)","Minimum 24GB VRAM if self-hosting, or API quota for cloud inference"],"input_types":["text (natural language instructions, questions, conversation turns)"],"output_types":["text (streaming or batch completion tokens)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.language.agnostic.patterns","name":"code generation and completion with language-agnostic patterns","description":"Generates syntactically valid code snippets and completions across 20+ programming languages by learning language-specific token patterns during instruction-tuning. The model uses transformer attention to understand code context (variable scope, function signatures, imports) and produces contextually appropriate completions without explicit AST parsing or language-specific rules.","intents":["Auto-complete code functions based on docstrings and function signatures","Generate boilerplate code for common patterns (API handlers, database queries, test cases)","Translate pseudocode or natural language descriptions into working code"],"best_for":["Individual developers seeking lightweight code completion without IDE plugins","Teams building code generation features into custom applications (no dependency on Copilot/CodeWhisperer)","Organizations needing code generation with full source code transparency (Apache 2.0 license)"],"limitations":["No semantic understanding of code correctness — may generate syntactically valid but logically broken code","Limited to ~8K token context, making it unsuitable for generating code that requires understanding large existing codebases","No built-in linting or type-checking — generated code requires manual validation before execution","Performance degrades on domain-specific languages (Rust, Kotlin, Haskell) compared to mainstream languages (Python, JavaScript, Java)"],"requires":["API access to Mistral Small 3 via OpenRouter or self-hosted deployment","Code formatter/linter in downstream pipeline for quality assurance","Language-specific test suite to validate generated code correctness"],"input_types":["text (function signatures, docstrings, code comments, pseudocode)"],"output_types":["text (code snippets in target language)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_2","uri":"capability://data.processing.analysis.structured.data.extraction.and.summarization.from.unstructured.text","name":"structured data extraction and summarization from unstructured text","description":"Extracts key information and generates summaries from long-form text by leveraging instruction-tuning to follow structured output directives (JSON schemas, bullet points, key-value pairs). The model processes input text through attention mechanisms to identify salient information and reformat it according to specified output schemas without requiring explicit extraction rules or regex patterns.","intents":["Extract entities (names, dates, amounts) from documents and return as JSON","Summarize long articles or reports into bullet-point summaries","Convert unstructured customer feedback into structured survey responses"],"best_for":["Data teams building ETL pipelines that need lightweight text-to-structured-data conversion","Content platforms requiring automated summarization without external NLP libraries","Organizations processing documents where extraction rules are too complex for regex/rule-based systems"],"limitations":["Accuracy degrades with documents longer than 8K tokens — requires chunking strategies for large documents","No guarantee of valid JSON output — may generate malformed structured data requiring post-processing validation","Hallucination risk when extracting information not explicitly present in source text","Performance on domain-specific terminology (medical, legal, financial) lower than specialized domain models"],"requires":["API access to Mistral Small 3","JSON schema validation library in downstream pipeline","Text chunking strategy for documents exceeding 8K tokens"],"input_types":["text (unstructured documents, articles, feedback, reports)"],"output_types":["text (JSON, CSV, markdown, bullet-point lists)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_3","uri":"capability://text.generation.language.multi.language.translation.with.context.preservation","name":"multi-language translation with context preservation","description":"Translates text between 50+ language pairs while preserving context, tone, and technical terminology through instruction-tuning on multilingual datasets. The model uses cross-lingual attention patterns to understand semantic meaning independent of source language and generates target-language text that maintains original intent without explicit back-translation or pivot languages.","intents":["Translate user-generated content (reviews, comments, support tickets) into English for analysis","Localize product documentation and UI strings into multiple languages","Enable cross-language customer support by translating incoming messages"],"best_for":["Global SaaS platforms needing lightweight, real-time translation without specialized MT infrastructure","Content platforms serving multilingual audiences with budget constraints","Teams building chatbots that need to support multiple languages from a single model"],"limitations":["Translation quality lower than specialized MT models (Google Translate, DeepL) for technical or domain-specific content","Context window of 8K tokens limits translation of long documents — requires document chunking","Hallucination risk when translating ambiguous phrases or idioms not well-represented in training data","Performance asymmetric across language pairs — high-resource languages (English, Spanish, French) translate better than low-resource languages (Swahili, Icelandic)"],"requires":["API access to Mistral Small 3","Language detection module to identify source language","Document chunking strategy for texts exceeding 8K tokens"],"input_types":["text (any language, any domain)"],"output_types":["text (translated content in target language)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_4","uri":"capability://text.generation.language.question.answering.over.provided.context.with.retrieval.augmented.generation.support","name":"question-answering over provided context with retrieval-augmented generation support","description":"Answers questions about provided text passages by using attention mechanisms to locate relevant information and generate answers grounded in the source material. The model integrates with retrieval systems (RAG pipelines) by accepting pre-retrieved context chunks and generating answers that cite or reference specific passages without requiring explicit knowledge base indexing or semantic search infrastructure.","intents":["Build a customer support chatbot that answers questions based on knowledge base articles","Create a document Q&A system where users ask questions about uploaded PDFs","Implement retrieval-augmented generation (RAG) where search results feed into answer generation"],"best_for":["Teams implementing RAG systems where retrieval is handled separately (vector databases, BM25 search)","Organizations building knowledge-base-driven chatbots with existing document repositories","Developers needing lightweight QA without fine-tuning on domain-specific data"],"limitations":["Accuracy depends entirely on retrieval quality — irrelevant context chunks cause incorrect answers","Context window of 8K tokens limits number of retrieved passages that can be processed simultaneously","No built-in fact verification — may generate plausible-sounding answers that contradict provided context","Performance degrades when context contains conflicting or ambiguous information"],"requires":["API access to Mistral Small 3","Separate retrieval system (vector database, BM25 search, or hybrid retrieval)","Context formatting strategy to structure retrieved passages for the model"],"input_types":["text (question + context passages)"],"output_types":["text (answer grounded in provided context)"],"categories":["text-generation-language","memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_5","uri":"capability://text.generation.language.creative.text.generation.with.style.and.tone.control","name":"creative text generation with style and tone control","description":"Generates creative content (stories, marketing copy, social media posts, poetry) with controllable style and tone through instruction-following prompts that specify desired voice, length, and format. The model uses learned patterns from instruction-tuning to adapt output style without requiring separate fine-tuning or style-specific model variants.","intents":["Generate multiple variations of marketing copy with different tones (formal, casual, humorous)","Create social media content calendars with varied post styles","Write creative fiction or poetry with specified themes or constraints"],"best_for":["Content creators and marketing teams needing rapid ideation and variation generation","Platforms automating content generation for user-generated content (reviews, descriptions, captions)","Teams building creative writing assistants without specialized fine-tuning"],"limitations":["Output quality and originality lower than specialized creative models or human writers","Tendency to produce generic or formulaic content when style constraints are vague","Limited ability to maintain consistent character voice or narrative arc across long-form content","May generate content that inadvertently copies training data or produces clichéd phrases"],"requires":["API access to Mistral Small 3","Clear style/tone specifications in prompts for consistent output","Human review process for quality assurance before publishing"],"input_types":["text (style specifications, themes, constraints, prompts)"],"output_types":["text (creative content in specified style)"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_6","uri":"capability://planning.reasoning.reasoning.and.step.by.step.problem.decomposition.with.chain.of.thought.prompting","name":"reasoning and step-by-step problem decomposition with chain-of-thought prompting","description":"Solves complex problems by generating intermediate reasoning steps before final answers, using chain-of-thought prompting patterns learned during instruction-tuning. The model produces explicit reasoning traces that decompose problems into sub-steps, enabling verification of logic and improving accuracy on multi-step reasoning tasks without requiring specialized reasoning architectures.","intents":["Solve math problems by showing work and intermediate calculations","Debug code by walking through execution logic step-by-step","Explain complex concepts by breaking them into digestible reasoning steps"],"best_for":["Educational platforms needing explainable problem-solving with visible reasoning","Teams building debugging assistants that show reasoning traces","Organizations requiring transparent decision-making in AI-assisted workflows"],"limitations":["Reasoning depth limited by 8K token context — complex problems requiring many reasoning steps may exceed context","No guarantee of correct reasoning — model may produce plausible-sounding but incorrect intermediate steps","Performance on mathematical reasoning lower than specialized math models or symbolic solvers","Reasoning traces may be verbose or redundant, increasing token consumption and latency"],"requires":["API access to Mistral Small 3","Prompting strategy that explicitly requests step-by-step reasoning","Verification mechanism to validate reasoning correctness (human review or symbolic checking)"],"input_types":["text (problem statements, questions, code to debug)"],"output_types":["text (reasoning steps + final answer)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_7","uri":"capability://text.generation.language.sentiment.analysis.and.emotion.detection.from.text","name":"sentiment analysis and emotion detection from text","description":"Classifies text sentiment (positive, negative, neutral) and detects emotional undertones (anger, joy, frustration, confusion) through instruction-tuned classification patterns. The model uses attention mechanisms to identify sentiment-bearing words and phrases, then generates structured sentiment labels or detailed emotion descriptions without requiring separate classification layers or fine-tuning.","intents":["Analyze customer feedback and support tickets to identify sentiment trends","Monitor social media mentions for brand sentiment in real-time","Detect emotional distress in user messages to trigger escalation workflows"],"best_for":["Customer success teams monitoring support ticket sentiment at scale","Social media monitoring platforms needing lightweight sentiment analysis","Organizations building emotion-aware chatbots that adapt responses based on user sentiment"],"limitations":["Accuracy lower than specialized sentiment models (BERT-based classifiers) on domain-specific language","Struggles with sarcasm, irony, and implicit sentiment — may misclassify sarcastic positive statements as negative","No multi-label sentiment support — cannot simultaneously classify text as both positive and negative","Performance degrades on code-mixed text (mixing multiple languages) or heavy slang/dialect usage"],"requires":["API access to Mistral Small 3","Clear sentiment label definitions in prompts (e.g., 'positive, negative, neutral, mixed')","Validation dataset to measure accuracy on domain-specific content"],"input_types":["text (customer feedback, social media posts, support messages)"],"output_types":["text (sentiment label + confidence, or detailed emotion description)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-mistral-small-24b-instruct-2501__cap_8","uri":"capability://safety.moderation.content.moderation.and.safety.filtering.with.configurable.policies","name":"content moderation and safety filtering with configurable policies","description":"Detects and flags potentially harmful content (hate speech, violence, adult content, misinformation) by applying instruction-tuned classification patterns that can be customized via prompts. The model uses attention mechanisms to identify harmful content patterns and generates moderation decisions (approve, flag, reject) with optional explanations, without requiring separate moderation models or rule-based filters.","intents":["Filter user-generated content in community platforms before publication","Detect harmful outputs from language models in production systems","Classify content for age-appropriate filtering (PG, PG-13, R ratings)"],"best_for":["Platform teams building content moderation pipelines with customizable policies","Organizations needing lightweight safety filtering without specialized moderation APIs","Teams building guardrails for LLM outputs in production"],"limitations":["Moderation accuracy lower than specialized moderation models (Perspective API, Azure Content Moderator) on edge cases","Difficulty distinguishing between harmful content and legitimate discussion of sensitive topics","No built-in context awareness — may flag educational content about harmful topics as harmful","Cultural and regional bias — moderation policies optimized for English may not apply to other languages"],"requires":["API access to Mistral Small 3","Clear moderation policy definitions in prompts (what constitutes harmful content)","Human review process to validate moderation decisions and refine policies"],"input_types":["text (user-generated content, model outputs, comments)"],"output_types":["text (moderation decision + explanation)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API key for OpenRouter or direct Mistral API access","HTTP client capable of streaming responses (for real-time token generation)","Minimum 24GB VRAM if self-hosting, or API quota for cloud inference","API access to Mistral Small 3 via OpenRouter or self-hosted deployment","Code formatter/linter in downstream pipeline for quality assurance","Language-specific test suite to validate generated code correctness","API access to Mistral Small 3","JSON schema validation library in downstream pipeline","Text chunking strategy for documents exceeding 8K tokens","Language detection module to identify source language"],"failure_modes":["Context window limited to ~8K tokens, requiring conversation truncation for long multi-turn exchanges","No built-in memory persistence across sessions — requires external state management for conversation history","24B parameter size means lower reasoning depth compared to 70B+ models on complex multi-step problems","Instruction-tuning optimized for common tasks; may underperform on highly specialized domain-specific instructions","No semantic understanding of code correctness — may generate syntactically valid but logically broken code","Limited to ~8K token context, making it unsuitable for generating code that requires understanding large existing codebases","No built-in linting or type-checking — generated code requires manual validation before execution","Performance degrades on domain-specific languages (Rust, Kotlin, Haskell) compared to mainstream languages (Python, JavaScript, Java)","Accuracy degrades with documents longer than 8K tokens — requires chunking strategies for large documents","No guarantee of valid JSON output — may generate malformed structured data requiring post-processing validation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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-small-24b-instruct-2501","compare_url":"https://unfragile.ai/compare?artifact=mistralai-mistral-small-24b-instruct-2501"}},"signature":"6Ipn0Jt6JJSrADYNi7hTXk+aAQSbnyjMHA1o188Ikd6zwaU+6ET/xhhYFhLkekXVJ+uW6KrA6mWBFKwnTYzGAA==","signedAt":"2026-06-20T02:24:56.859Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-mistral-small-24b-instruct-2501","artifact":"https://unfragile.ai/mistralai-mistral-small-24b-instruct-2501","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-mistral-small-24b-instruct-2501","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"}}