{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-ministral-3b-2512","slug":"mistralai-ministral-3b-2512","name":"Mistral: Ministral 3 3B 2512","type":"model","url":"https://openrouter.ai/models/mistralai~ministral-3b-2512","page_url":"https://unfragile.ai/mistralai-ministral-3b-2512","categories":["image-generation"],"tags":["mistralai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-ministral-3b-2512__cap_0","uri":"capability://text.generation.language.lightweight.multimodal.text.generation.with.vision.understanding","name":"lightweight multimodal text generation with vision understanding","description":"Generates coherent text responses to prompts while maintaining the ability to process and understand image inputs, using a 3B parameter architecture optimized for inference speed and memory efficiency. The model uses a transformer-based decoder with vision encoder integration that allows it to analyze images and incorporate visual context into text generation without requiring separate vision-language alignment layers typical of larger models.","intents":["I need a small, fast language model that can understand images and generate text responses about them for edge deployment","I want to run a multimodal AI locally or on resource-constrained devices without sacrificing vision capabilities","I need to process image+text queries with minimal latency for real-time applications like mobile assistants"],"best_for":["embedded systems and edge device developers building on-device AI","teams optimizing for inference cost and latency in production systems","mobile and IoT developers needing multimodal capabilities without cloud dependency"],"limitations":["3B parameter count limits reasoning depth and context window compared to 7B+ models, reducing performance on complex multi-step reasoning tasks","Vision capabilities are constrained by model size — struggles with dense text extraction from images or fine-grained visual reasoning","No built-in function calling or tool use — requires external orchestration for agent-based workflows","Context window size not specified in documentation — likely 8K or less, limiting long-document processing"],"requires":["API access via OpenRouter or direct Mistral API with valid authentication token","HTTP/REST client capability for inference requests","Support for multipart form data or base64 image encoding for vision inputs"],"input_types":["text (prompts, instructions, conversational context)","image (JPEG, PNG, WebP formats, typically up to 4-5MB)"],"output_types":["text (generated responses, completions)","structured text (JSON when prompted, though not guaranteed)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-3b-2512__cap_1","uri":"capability://text.generation.language.api.based.inference.with.streaming.response.support","name":"api-based inference with streaming response support","description":"Executes model inference through OpenRouter's REST API endpoints with support for token-by-token streaming responses, allowing real-time text generation without waiting for full completion. The implementation uses HTTP POST requests with JSON payloads and optional Server-Sent Events (SSE) streaming, enabling progressive output rendering in client applications and reduced perceived latency.","intents":["I want to integrate this model into my web app and stream responses to users for a chat-like experience","I need to call the model from a backend service without managing GPU infrastructure","I want to build a real-time assistant that shows text appearing word-by-word as it's generated"],"best_for":["web and mobile application developers building chat interfaces","backend engineers integrating LLMs without infrastructure management","teams building real-time AI features with streaming UX requirements"],"limitations":["API-based inference introduces network latency (typically 50-200ms per request) compared to local inference","Streaming responses require persistent HTTP connections, which may be problematic behind certain proxies or firewalls","Rate limiting and quota management required — OpenRouter enforces per-minute token limits based on pricing tier","No guaranteed response time SLA — inference speed depends on OpenRouter's backend load"],"requires":["OpenRouter API key (obtain from https://openrouter.ai)","HTTP client library supporting streaming (fetch API, requests, httpx, etc.)","Network connectivity to OpenRouter endpoints","Support for Server-Sent Events (SSE) if using streaming mode"],"input_types":["text (prompt, system message, conversation history)","image (base64-encoded or URL reference for vision inputs)"],"output_types":["text stream (token-by-token via SSE or chunked transfer encoding)","complete text response (non-streaming mode)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-3b-2512__cap_2","uri":"capability://image.visual.vision.aware.context.understanding.for.multimodal.prompts","name":"vision-aware context understanding for multimodal prompts","description":"Processes images alongside text prompts to extract visual context and incorporate it into response generation, using an integrated vision encoder that converts image pixels into embedding space compatible with the language model's token representations. The model can reason about image content, answer questions about visual elements, and generate text that references specific details from provided images.","intents":["I need to ask questions about images and get detailed text answers about what's in them","I want to analyze screenshots or diagrams and get explanations of their content","I need to process documents with mixed text and images and extract information from both modalities"],"best_for":["document processing workflows combining OCR with semantic understanding","customer support systems analyzing user-submitted screenshots","educational tools explaining visual content to students"],"limitations":["Vision performance degrades on small or low-resolution images — minimum effective resolution ~224x224 pixels","Cannot perform precise spatial reasoning or count small objects reliably due to 3B parameter constraint","No built-in OCR optimization — struggles with dense text extraction compared to specialized OCR models","Image understanding is contextual rather than pixel-perfect — may miss fine details in complex scenes"],"requires":["Image input in JPEG, PNG, or WebP format","Image size typically <5MB (OpenRouter enforces limits)","Text prompt describing what to analyze or question to answer about the image"],"input_types":["image (JPEG, PNG, WebP)","text (question or instruction related to the image)"],"output_types":["text (description, answer, analysis of image content)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-3b-2512__cap_3","uri":"capability://text.generation.language.conversation.history.management.with.context.preservation","name":"conversation history management with context preservation","description":"Maintains multi-turn conversation state by accepting arrays of message objects with role-based formatting (system, user, assistant), allowing the model to reference previous exchanges and maintain conversational coherence across multiple requests. The implementation uses a standard chat completion message format where each turn is encoded as a separate token sequence, with the model attending to all prior messages within its context window.","intents":["I want to build a chatbot that remembers what users said in previous messages","I need to maintain conversation state across multiple API calls without managing session storage myself","I want to set system instructions that persist across the entire conversation"],"best_for":["conversational AI and chatbot developers","teams building multi-turn dialogue systems","customer service automation platforms"],"limitations":["Context window size is finite (likely 8K tokens) — long conversations will eventually exceed capacity and require truncation or summarization","No automatic conversation summarization — developers must manually manage context length or implement sliding window strategies","Each API call includes full conversation history in the request, increasing token consumption and latency as conversations grow","No built-in conversation persistence — requires external database to store message history across sessions"],"requires":["Message array formatted as [{role: 'user'|'assistant'|'system', content: string}]","Total token count of all messages must fit within model's context window","Stateful client application to maintain conversation history between API calls"],"input_types":["text (message content)","metadata (role: system/user/assistant, optional timestamp)"],"output_types":["text (assistant response)","metadata (token usage, finish reason)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-3b-2512__cap_4","uri":"capability://text.generation.language.parameter.controlled.generation.with.sampling.and.temperature.tuning","name":"parameter-controlled generation with sampling and temperature tuning","description":"Exposes inference parameters (temperature, top_p, top_k, max_tokens) that control the randomness and length of generated text, allowing developers to tune output behavior from deterministic (temperature=0) to highly creative (temperature=2.0). The implementation uses standard sampling techniques where temperature scales logit distributions before softmax, and top_p/top_k apply nucleus and k-sampling filters to the token probability distribution.","intents":["I need deterministic, reproducible outputs for structured data generation or code","I want more creative and diverse outputs for brainstorming or content generation","I need to control response length to fit specific UI constraints or token budgets"],"best_for":["developers building deterministic pipelines (code generation, data extraction)","content creators needing creative variation control","teams optimizing token consumption and cost"],"limitations":["Temperature=0 does not guarantee identical outputs across runs due to floating-point precision variations in different hardware","Very high temperatures (>1.5) often produce incoherent or nonsensical outputs with a 3B model","max_tokens parameter is hard limit — model cannot exceed it, potentially cutting off mid-sentence","No built-in token counting before generation — developers must estimate token usage to avoid truncation"],"requires":["Understanding of temperature semantics (0=deterministic, 1=default, >1=creative)","Knowledge of model's typical token-to-character ratio for length estimation","API support for parameter passing (all standard LLM APIs support this)"],"input_types":["text (prompt)","numeric parameters (temperature: 0-2, top_p: 0-1, top_k: 1-100, max_tokens: 1-8192)"],"output_types":["text (generated response, length bounded by max_tokens)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-ministral-3b-2512__cap_5","uri":"capability://text.generation.language.cost.optimized.inference.with.transparent.per.token.pricing","name":"cost-optimized inference with transparent per-token pricing","description":"Executes inference through OpenRouter's pricing model which charges separately for input and output tokens, with published rates visible before API calls. The model's 3B parameter size results in lower per-token costs compared to larger models, and OpenRouter's aggregation model allows price comparison across providers without switching infrastructure.","intents":["I need to estimate and control inference costs for my application","I want to compare this model's cost-effectiveness against larger alternatives","I need to optimize token usage to reduce operational expenses"],"best_for":["cost-conscious startups and indie developers","teams building high-volume inference applications","projects with tight budget constraints requiring efficient model selection"],"limitations":["Pricing is subject to OpenRouter's rate changes — no long-term price guarantees","Per-token billing means high-volume applications still accumulate significant costs despite low per-token rates","No volume discounts or reserved capacity pricing — cost scales linearly with usage","Smaller model size may require more tokens to achieve equivalent quality, offsetting per-token savings"],"requires":["OpenRouter account with payment method on file","Monitoring of token usage to track costs","Understanding of input vs output token pricing (typically output tokens cost 2-3x input tokens)"],"input_types":["text (prompt)"],"output_types":["text (response with token usage metadata)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Mistral API with valid authentication token","HTTP/REST client capability for inference requests","Support for multipart form data or base64 image encoding for vision inputs","OpenRouter API key (obtain from https://openrouter.ai)","HTTP client library supporting streaming (fetch API, requests, httpx, etc.)","Network connectivity to OpenRouter endpoints","Support for Server-Sent Events (SSE) if using streaming mode","Image input in JPEG, PNG, or WebP format","Image size typically <5MB (OpenRouter enforces limits)","Text prompt describing what to analyze or question to answer about the image"],"failure_modes":["3B parameter count limits reasoning depth and context window compared to 7B+ models, reducing performance on complex multi-step reasoning tasks","Vision capabilities are constrained by model size — struggles with dense text extraction from images or fine-grained visual reasoning","No built-in function calling or tool use — requires external orchestration for agent-based workflows","Context window size not specified in documentation — likely 8K or less, limiting long-document processing","API-based inference introduces network latency (typically 50-200ms per request) compared to local inference","Streaming responses require persistent HTTP connections, which may be problematic behind certain proxies or firewalls","Rate limiting and quota management required — OpenRouter enforces per-minute token limits based on pricing tier","No guaranteed response time SLA — inference speed depends on OpenRouter's backend load","Vision performance degrades on small or low-resolution images — minimum effective resolution ~224x224 pixels","Cannot perform precise spatial reasoning or count small objects reliably due to 3B parameter constraint","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.27,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mistralai-ministral-3b-2512","compare_url":"https://unfragile.ai/compare?artifact=mistralai-ministral-3b-2512"}},"signature":"LMUOnsTrghQijueZxPTuPyUn9UXVMY5oZedgilKM4VLBR5SbJ864ambIgr6sSm9OAbYhdVUvrSdzu1SkG/f8Cw==","signedAt":"2026-06-19T22:24:31.400Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-ministral-3b-2512","artifact":"https://unfragile.ai/mistralai-ministral-3b-2512","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-ministral-3b-2512","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}