{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mistral-small","slug":"mistral-small","name":"Mistral Small","type":"model","url":"https://mistral.ai/news/mistral-small-3/","page_url":"https://unfragile.ai/mistral-small","categories":["model-training","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mistral-small__cap_0","uri":"capability://text.generation.language.low.latency.instruction.following.text.generation","name":"low-latency instruction-following text generation","description":"Generates coherent text responses to natural language instructions using a 24B parameter decoder-only transformer optimized for reduced forward-pass latency through architectural simplification (fewer layers than competing models). Achieves ~150 tokens/second throughput on single GPU hardware, enabling real-time conversational interactions without cloud round-trips. Instruction-tuned variant available for direct deployment without additional fine-tuning.","intents":["I need to build a chatbot that responds to user queries in under 500ms latency","I want to run a language model locally on a single GPU for privacy-sensitive applications","I need to replace GPT-4o-mini with an open-source alternative that's faster and cheaper"],"best_for":["teams building real-time conversational AI requiring sub-second response times","developers deploying on resource-constrained hardware (single GPU)","organizations with privacy requirements preventing cloud API calls"],"limitations":["Not trained with reinforcement learning or synthetic data, limiting performance on complex multi-step reasoning tasks","Benchmark variance noted: internal evaluation pipeline may not align with public benchmarks; human judgement evaluations sometimes starkly differ from published scores","No built-in chain-of-thought reasoning capabilities; requires external prompting or fine-tuning for complex reasoning","Exact layer count and architectural modifications not publicly disclosed, limiting reproducibility"],"requires":["Single GPU with sufficient VRAM (specific VRAM requirement unknown; RTX 4 mentioned for quantized inference)","Inference framework supporting transformer models (vLLM, ollama, llama.cpp, or similar)","For API access: Mistral AI API credentials"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","language-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_1","uri":"capability://code.generation.editing.code.generation.and.review.with.competitive.benchmarking","name":"code generation and review with competitive benchmarking","description":"Generates and analyzes code across multiple programming languages using transformer-based pattern matching trained on diverse code corpora. Evaluated against GPT-4o-mini and Llama 3.3 70B using Human Eval benchmarks with 1000+ proprietary prompts; claims competitive performance despite 24B parameter count vs 70B+ alternatives. Supports function calling and structured output for programmatic code manipulation.","intents":["I need to generate code snippets or complete functions without sending code to external APIs","I want to review code quality and suggest refactorings using an open-source model","I need a code generation model that runs locally for proprietary codebase analysis"],"best_for":["developers building IDE plugins or code editors requiring local inference","teams with proprietary code that cannot be sent to cloud APIs","engineering teams needing cost-effective code review automation at scale"],"limitations":["Human Eval benchmark results based on internal evaluation methodology; external validation against public benchmarks (HumanEval, MBPP) not provided","No explicit support for language-specific optimizations or syntax-aware parsing mentioned","Evaluation used GPT-4o-2024-05-13 as judge in some benchmarks, introducing potential bias toward OpenAI model outputs","No documented performance on specialized domains (embedded systems, low-level systems programming)"],"requires":["Single GPU or CPU with sufficient compute for 24B parameter inference","Code context provided as text input (no AST parsing or structural analysis mentioned)","Inference framework supporting transformer models"],"input_types":["text","code"],"output_types":["text","code","structured data (via function calling)"],"categories":["code-generation-editing","language-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_10","uri":"capability://text.generation.language.apache.2.0.licensed.open.source.deployment","name":"apache 2.0 licensed open-source deployment","description":"Released under Apache 2.0 license (both pretrained and instruction-tuned checkpoints) enabling unrestricted commercial use, modification, and redistribution. Permits building proprietary products, internal tools, and commercial services without licensing fees or attribution requirements. Supports self-hosting, fine-tuning, and derivative works without legal restrictions.","intents":["I need to build a commercial product using an open-source language model without licensing restrictions","I want to modify or fine-tune a model for my specific use case without legal constraints","I need to ensure my AI infrastructure uses fully open-source components for transparency and control"],"best_for":["startups and companies building commercial AI products","organizations requiring fully open-source AI infrastructure","teams building proprietary models or products on top of open-source bases"],"limitations":["Apache 2.0 license requires inclusion of license notice and copyright attribution in distributions","No warranty or liability protection provided by Apache 2.0 license","Modifications must be documented and made available to users","No explicit acceptable use policy or content filtering guarantees"],"requires":["Compliance with Apache 2.0 license terms (attribution, license notice inclusion)","No proprietary licensing agreements or restrictions"],"input_types":[],"output_types":[],"categories":["text-generation-language","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_11","uri":"capability://text.generation.language.multi.turn.conversation.management.with.state.retention","name":"multi-turn conversation management with state retention","description":"Maintains conversation context across multiple turns through instruction-tuned design that preserves prior messages and user intent. Supports natural dialogue flow with coherent reference resolution and context-aware responses without explicit state management code. Enables building stateful chatbots and conversational agents without external session storage (though persistence requires external state store).","intents":["I need to build a chatbot that remembers previous messages and maintains conversation context","I want to create a conversational agent that understands references to earlier parts of the conversation","I need to support multi-turn interactions where user intent evolves across messages"],"best_for":["conversational AI applications requiring natural dialogue flow","customer support and helpdesk automation with context awareness","interactive tutoring or educational applications"],"limitations":["Context retention depends on conversation history fitting within 128K token window","No explicit conversation state management or session persistence built-in; requires external storage","Long conversations may degrade performance as context window fills","No documented handling of context conflicts or contradictory information across turns"],"requires":["Conversation history management (external state store for persistence)","Instruction-tuned checkpoint for optimal multi-turn performance","Inference framework supporting transformer models"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_2","uri":"capability://text.generation.language.mathematical.reasoning.and.problem.solving","name":"mathematical reasoning and problem-solving","description":"Solves mathematical problems and performs symbolic reasoning using transformer-based pattern matching on mathematical corpora. Benchmarked against larger models (Llama 3.3 70B, GPT-4o-mini) on mathematical reasoning tasks; claims outperformance despite smaller parameter count. Supports step-by-step reasoning through text generation without explicit symbolic math engines.","intents":["I need to solve math problems or verify mathematical solutions programmatically","I want to build educational tools that explain mathematical reasoning without cloud dependencies","I need to extract and validate numerical answers from unstructured text"],"best_for":["educational technology platforms requiring local math problem solving","financial or scientific applications needing mathematical reasoning without API latency","teams building domain-specific tools for STEM education or research"],"limitations":["No explicit symbolic math capabilities or integration with computer algebra systems (SymPy, Mathematica) mentioned","Mathematical reasoning performance not quantified with specific benchmarks (MATH, GSM8K scores unknown)","Not trained with reinforcement learning, potentially limiting performance on complex multi-step proofs","No documented handling of edge cases (division by zero, undefined operations, numerical precision)"],"requires":["Single GPU for inference","Mathematical problems provided as natural language text input","Inference framework supporting transformer models"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_3","uri":"capability://tool.use.integration.function.calling.with.schema.based.dispatch","name":"function calling with schema-based dispatch","description":"Enables agentic workflows by supporting function calling through schema-based function registries, allowing the model to invoke external tools and APIs based on natural language instructions. Integrates with Mistral AI API and self-hosted deployments to parse structured function calls and dispatch them to registered handlers. Supports multiple function definitions per request with conditional logic for tool selection.","intents":["I need to build an AI agent that can call external APIs or tools based on user requests","I want to enable the model to retrieve real-time data (weather, stock prices) or perform actions (send emails, update databases)","I need to orchestrate multi-step workflows where the model decides which tools to use and in what order"],"best_for":["developers building AI agents requiring tool orchestration","teams implementing agentic RAG systems with external knowledge bases","applications needing real-time data integration (APIs, databases, webhooks)"],"limitations":["Function calling format and schema specification not detailed in provided documentation; exact API contract unknown","No documented support for nested function calls or recursive tool invocation","Latency overhead for function calling dispatch not quantified; may impact real-time performance claims","No explicit error handling or fallback mechanisms documented for failed function calls"],"requires":["Mistral AI API access or self-hosted inference setup","Function schema definitions in supported format (likely JSON Schema, but unconfirmed)","External tool/API endpoints registered and accessible from inference environment"],"input_types":["text","structured data (function schemas)"],"output_types":["structured data (function calls)","text"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_4","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Generates structured data (JSON, XML, or other formats) that conforms to user-specified schemas, enabling reliable extraction of machine-readable outputs from natural language instructions. Parses schema definitions and constrains generation to valid outputs matching the schema, reducing post-processing and validation overhead. Supports complex nested structures and conditional fields.","intents":["I need to extract structured data from unstructured text (e.g., extract entities, relationships, or attributes)","I want to generate JSON responses that conform to my API schema without manual validation","I need to populate database records or forms from natural language descriptions"],"best_for":["data extraction pipelines requiring reliable structured output","API backends needing consistent JSON responses from language model outputs","ETL workflows extracting information from documents or user input"],"limitations":["Structured output format specification and schema language not detailed; exact validation mechanism unknown","No documented performance impact of schema constraints on generation speed","Handling of schema violations or invalid outputs not specified (fallback behavior unknown)","No explicit support for recursive or self-referential schemas"],"requires":["Schema definition in supported format (likely JSON Schema, but unconfirmed)","Inference framework or API supporting structured output constraints","Clear schema documentation for model to understand output requirements"],"input_types":["text","structured data (schema definitions)"],"output_types":["structured data (JSON, XML, or other formats)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_5","uri":"capability://text.generation.language.classification.and.sentiment.analysis","name":"classification and sentiment analysis","description":"Classifies text into predefined categories or analyzes sentiment using transformer-based pattern matching trained on diverse text corpora. Supports multi-class and multi-label classification through natural language prompting or structured output schemas. Optimized for low-latency classification enabling real-time content moderation, intent detection, and sentiment analysis at scale.","intents":["I need to classify customer support tickets into categories (billing, technical, sales) in real-time","I want to detect user intent from chat messages without cloud API latency","I need to perform sentiment analysis on social media or review data at scale"],"best_for":["customer support teams automating ticket routing and triage","content moderation platforms requiring real-time classification","analytics platforms analyzing sentiment or intent from user-generated content"],"limitations":["No explicit multi-label classification benchmarks or performance metrics provided","Domain-specific classification performance not documented (e.g., financial sentiment, medical intent)","No documented handling of ambiguous or borderline cases","Classification accuracy may degrade on out-of-domain text or novel categories"],"requires":["Text input in supported language (language support not explicitly documented)","Classification categories or labels defined in prompt or schema","Single GPU for inference or API access"],"input_types":["text"],"output_types":["text","structured data (classification labels)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_6","uri":"capability://text.generation.language.customer.support.automation.with.context.awareness","name":"customer support automation with context awareness","description":"Powers conversational customer support agents by combining instruction-following text generation with low-latency inference, enabling real-time responses to customer inquiries. Supports multi-turn conversations with context retention across messages, function calling for ticket creation or knowledge base lookup, and structured output for routing decisions. Deployable on single GPU for on-premises support infrastructure.","intents":["I need to build a customer support chatbot that responds instantly without cloud latency","I want to automate first-level support triage while maintaining conversation context","I need to integrate support automation with internal systems (ticketing, knowledge bases) without exposing customer data to external APIs"],"best_for":["enterprises with privacy requirements preventing cloud-based support automation","support teams needing sub-second response times for real-time chat","organizations building on-premises AI infrastructure"],"limitations":["Context window of 128K tokens may be insufficient for very long conversation histories or large knowledge base integration","No explicit multi-language support documented for global support teams","Handling of complex escalation logic or human handoff not specified","No documented performance on domain-specific support terminology or jargon"],"requires":["Single GPU for inference","Integration with ticketing system or knowledge base (via function calling)","Conversation history management (external state store required)"],"input_types":["text"],"output_types":["text","structured data (routing decisions, ticket creation)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_7","uri":"capability://code.generation.editing.fine.tuning.and.domain.specialization","name":"fine-tuning and domain specialization","description":"Serves as a base model for community fine-tuning and customization on domain-specific tasks (legal, medical, technical support). Released as both pretrained and instruction-tuned checkpoints under Apache 2.0 license, enabling researchers and practitioners to adapt the model to specialized vocabularies, reasoning patterns, and task-specific behaviors. Supports standard fine-tuning approaches (supervised fine-tuning, LoRA) on single GPU.","intents":["I need to fine-tune a language model on legal documents or medical terminology","I want to adapt a base model to my company's specific domain or jargon","I need to create specialized models for technical support or domain-specific Q&A"],"best_for":["researchers and practitioners building domain-specific language models","organizations with proprietary data requiring model customization","teams building specialized applications (legal tech, medical AI, technical support)"],"limitations":["Fine-tuning methodology and best practices not documented in provided materials","No explicit guidance on data requirements, training time, or convergence criteria","Instruction-tuned variant may require different fine-tuning approaches than pretrained checkpoint","No documented performance degradation or catastrophic forgetting risks when fine-tuning"],"requires":["Pretrained or instruction-tuned checkpoint (available under Apache 2.0 license)","Domain-specific training data in supported format","GPU with sufficient VRAM for fine-tuning (specific requirements unknown)","Fine-tuning framework (Hugging Face Transformers, Axolotl, or similar)"],"input_types":["text","structured data (training datasets)"],"output_types":["model checkpoint"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_8","uri":"capability://text.generation.language.private.local.inference.with.quantization.support","name":"private local inference with quantization support","description":"Enables private, on-premises deployment by supporting quantization to run on single consumer GPUs (RTX 4 mentioned) without cloud connectivity. Quantized variants reduce memory footprint and latency while maintaining competitive performance, enabling deployment in air-gapped environments or privacy-sensitive applications. Apache 2.0 license permits unrestricted commercial self-hosting.","intents":["I need to run a language model completely offline without sending data to cloud APIs","I want to deploy AI on consumer hardware (RTX 4090) for cost-effective inference","I need to comply with data residency requirements or privacy regulations (GDPR, HIPAA)"],"best_for":["organizations with strict data privacy or regulatory requirements","teams building on-premises AI infrastructure","developers deploying on consumer or edge hardware"],"limitations":["Specific quantization formats (GGUF, int8, int4) and tools not documented","Quantization performance impact on accuracy and latency not quantified","VRAM requirements for different quantization levels not specified","No documented support for distributed inference or multi-GPU setups"],"requires":["Single GPU (RTX 4 or equivalent) with sufficient VRAM","Quantization tool (llama.cpp, ollama, or similar)","Inference framework supporting quantized models","No internet connectivity required for inference (only for model download)"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__cap_9","uri":"capability://text.generation.language.128k.context.window.for.long.document.processing","name":"128k context window for long-document processing","description":"Processes documents and conversations up to 128K tokens in length, enabling analysis of entire books, long conversations, or large codebases without chunking or summarization. Context window enables few-shot learning with extensive examples and retrieval-augmented generation with large knowledge bases. Maintains coherence and reference resolution across long-range dependencies.","intents":["I need to analyze entire documents (contracts, research papers, books) without splitting into chunks","I want to provide extensive context or examples for few-shot learning without token limits","I need to build RAG systems with large knowledge bases without aggressive chunking"],"best_for":["document analysis and legal review applications","research and academic applications requiring long-form understanding","RAG systems with large knowledge bases or extensive retrieval results"],"limitations":["128K context window claimed in artifact but not verified in raw documentation","No documented performance degradation at maximum context length","Handling of context overflow or truncation not specified","Memory requirements for 128K context inference not quantified","No explicit guidance on optimal context utilization or prompt engineering for long contexts"],"requires":["Inference framework supporting 128K context window","Sufficient GPU VRAM for processing 128K tokens (specific requirements unknown)","Documents or context provided as text input"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mistral-small__headline","uri":"capability://code.generation.editing.efficient.ai.model.for.coding.and.reasoning.tasks","name":"efficient ai model for coding and reasoning tasks","description":"Mistral Small is a powerful 24B parameter AI model optimized for coding, math, and reasoning tasks, offering strong performance at low cost and latency, making it ideal for production workloads.","intents":["best AI model for coding","AI model for customer support","fast AI model for data extraction","AI model for reasoning tasks","efficient AI model for production"],"best_for":["production workloads","fast response applications"],"limitations":["limited to 128K context window"],"requires":["single GPU for deployment"],"input_types":["text","code"],"output_types":["structured output"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Single GPU with sufficient VRAM (specific VRAM requirement unknown; 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