{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-o4-mini","slug":"openai-o4-mini","name":"OpenAI: o4 Mini","type":"model","url":"https://openrouter.ai/models/openai~o4-mini","page_url":"https://unfragile.ai/openai-o4-mini","categories":["model-training"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.10e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-o4-mini__cap_0","uri":"capability://planning.reasoning.multimodal.reasoning.with.extended.chain.of.thought","name":"multimodal reasoning with extended chain-of-thought","description":"Processes both text and image inputs through an extended reasoning pipeline that generates intermediate reasoning steps before producing final outputs. The model uses an internal chain-of-thought mechanism similar to o1/o3 architecture but optimized for inference speed and cost, allowing it to handle complex reasoning tasks across modalities without exposing reasoning tokens to the user by default.","intents":["I need to solve a complex problem that requires step-by-step reasoning across text and image inputs","I want faster reasoning model inference than o1 but better quality than standard GPT-4","I need to analyze images with deep reasoning about spatial relationships or complex visual patterns"],"best_for":["teams building reasoning-heavy applications with cost constraints","developers needing multimodal analysis with inference speed under 10 seconds","enterprises migrating from o1 to reduce per-request costs while maintaining reasoning quality"],"limitations":["Reasoning process is not exposed to users — cannot inspect intermediate reasoning steps","Extended thinking adds latency (typically 5-15 seconds per request) compared to standard models","Image resolution and complexity may impact reasoning quality; very large images may be downsampled","Reasoning depth is automatically determined by the model; no user control over reasoning budget"],"requires":["OpenAI API key with o4-mini model access","HTTP/2 capable client for streaming support","Timeout configuration of at least 30 seconds for reasoning completion"],"input_types":["text (unlimited context within model limits)","image (JPEG, PNG, WebP, GIF; multiple images per request supported)"],"output_types":["text (natural language response)","structured JSON (when requested via system prompt)","code snippets (Python, JavaScript, etc.)"],"categories":["planning-reasoning","multimodal-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini__cap_1","uri":"capability://tool.use.integration.tool.use.and.function.calling.with.structured.schema.binding","name":"tool-use and function calling with structured schema binding","description":"Supports function calling through OpenAI's native tool-use API, accepting JSON schema definitions and returning structured tool calls with arguments. The model can invoke multiple tools in sequence, handle tool results, and adapt behavior based on tool outputs, enabling agentic workflows without requiring prompt engineering for tool invocation.","intents":["I want to build an agent that can call APIs, databases, or custom functions based on reasoning","I need the model to decide when and how to use tools without manual prompt engineering","I want to integrate the model into a workflow where it orchestrates multiple external systems"],"best_for":["developers building autonomous agents with tool orchestration","teams implementing retrieval-augmented generation (RAG) with tool-based document access","applications requiring real-time data integration (weather, stock prices, database queries)"],"limitations":["Tool schema must be valid JSON Schema; complex nested schemas may reduce model accuracy","No built-in tool result caching — repeated tool calls with identical inputs are not deduplicated","Tool execution is synchronous; parallel tool calls must be simulated through sequential invocation","Maximum of ~100 tool definitions per request; very large tool registries require filtering"],"requires":["OpenAI API key with function calling support","JSON Schema definitions for each tool (OpenAI format)","Client-side tool execution logic (model only generates calls, does not execute)"],"input_types":["text (tool descriptions and user queries)","JSON schema (tool definitions)"],"output_types":["tool_call objects (function name + JSON arguments)","text (fallback response if no tools are invoked)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini__cap_2","uri":"capability://image.visual.image.understanding.and.visual.reasoning","name":"image understanding and visual reasoning","description":"Analyzes images through multimodal encoding that processes visual features alongside text, enabling the model to answer questions about image content, describe visual elements, detect objects, read text in images, and reason about spatial relationships. The model applies its reasoning capability to visual analysis, allowing it to draw inferences about what is shown rather than just describing surface-level content.","intents":["I need to extract information from images (text, objects, layouts) with reasoning about context","I want to analyze diagrams, charts, or screenshots and explain what they mean","I need to detect anomalies or issues in visual content based on reasoning about expected patterns"],"best_for":["document processing pipelines that need to understand scanned documents or PDFs","quality assurance teams analyzing screenshots or product images","accessibility applications that need to describe images with reasoning about context"],"limitations":["Image resolution is limited; very high-resolution images are downsampled to ~1024x1024 effective resolution","OCR accuracy depends on image quality; handwritten text recognition is less reliable than printed text","Cannot process video directly; only static image frames are supported","Reasoning about images adds latency; image analysis requests typically take 8-15 seconds"],"requires":["OpenAI API key with vision capability enabled","Images in JPEG, PNG, WebP, or GIF format","Base64 encoding or URL-accessible image URLs"],"input_types":["image (JPEG, PNG, WebP, GIF; up to 20MB per image)","text (questions or prompts about the image)"],"output_types":["text (descriptions, answers, analysis)","structured JSON (when prompted for specific fields)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini__cap_3","uri":"capability://planning.reasoning.cost.optimized.inference.with.dynamic.reasoning.depth","name":"cost-optimized inference with dynamic reasoning depth","description":"Automatically adjusts the depth of reasoning computation based on query complexity, using lighter reasoning for straightforward questions and deeper reasoning for complex problems. This dynamic approach reduces token consumption and latency for simple queries while maintaining reasoning capability for difficult tasks, implemented through internal heuristics that estimate problem difficulty without exposing reasoning tokens.","intents":["I want to use a reasoning model but need predictable costs for a high-volume application","I need fast responses for simple queries but deep reasoning for complex ones, without manual configuration","I want to reduce per-request costs compared to o1 while keeping reasoning capability"],"best_for":["SaaS applications with variable query complexity and cost-sensitive pricing models","teams running high-volume inference pipelines with mixed difficulty queries","startups needing reasoning capability without o1-level costs"],"limitations":["Reasoning depth is not user-controllable; cannot force deep reasoning on simple queries or shallow reasoning on complex ones","Cost savings are unpredictable per-request; only visible in aggregate across many requests","No visibility into reasoning depth used; cannot optimize prompts based on actual reasoning behavior","Dynamic depth may occasionally produce suboptimal results if complexity heuristics misclassify a query"],"requires":["OpenAI API key with o4-mini access","Standard OpenAI pricing model (pay-per-token)"],"input_types":["text (any query)","image (optional, for multimodal queries)"],"output_types":["text (response with variable reasoning depth applied internally)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini__cap_4","uri":"capability://code.generation.editing.context.aware.code.generation.and.analysis","name":"context-aware code generation and analysis","description":"Generates, debugs, and analyzes code across multiple programming languages using reasoning to understand code structure, dependencies, and logic flow. The model can generate complete functions or modules, suggest refactorings, identify bugs, and explain code behavior by reasoning through execution paths rather than pattern matching.","intents":["I need to generate code that handles complex logic or edge cases correctly","I want to debug code by reasoning about what the code does and why it might fail","I need to refactor code while maintaining correctness and understanding dependencies"],"best_for":["developers building complex algorithms or systems that need correctness verification","code review workflows that need reasoning about logic and potential bugs","teams migrating or refactoring large codebases with complex interdependencies"],"limitations":["Code generation quality depends on prompt clarity; ambiguous requirements may produce incorrect implementations","Cannot execute code directly; generated code must be tested by the user","Reasoning latency (5-15 seconds) makes real-time code completion impractical","Large codebases (>50KB context) may exceed token limits; requires code selection/summarization"],"requires":["OpenAI API key with code generation capability","Programming language specifications or examples in the prompt"],"input_types":["text (code snippets, requirements, questions)","code (existing code for analysis or refactoring)"],"output_types":["code (generated or refactored code)","text (explanations, bug reports, refactoring suggestions)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini__cap_5","uri":"capability://text.generation.language.streaming.response.generation.with.partial.output","name":"streaming response generation with partial output","description":"Supports server-sent events (SSE) streaming to deliver model outputs incrementally as they are generated, enabling real-time display of responses without waiting for full completion. Streaming works with reasoning models by delivering the final response tokens as they are produced, while internal reasoning steps remain hidden.","intents":["I want to show users responses in real-time as they are generated, not wait for full completion","I need to reduce perceived latency in interactive applications by streaming partial results","I want to cancel long-running requests mid-generation if the user stops waiting"],"best_for":["web applications and chat interfaces requiring real-time response display","interactive tools where users expect immediate feedback","applications with strict latency requirements (e.g., real-time chat)"],"limitations":["Reasoning process is not streamed; users see no output until reasoning completes, then final response streams","Streaming adds complexity to error handling; errors may occur mid-stream after partial output is sent","Token counting is less accurate with streaming; final token usage is only known after completion","Some client libraries have poor streaming support; requires careful implementation"],"requires":["OpenAI API key with streaming support","HTTP client with SSE support (most modern HTTP libraries)","stream=true parameter in API request"],"input_types":["text (any query)","image (optional, for multimodal queries)"],"output_types":["stream of text chunks (SSE format, delivered incrementally)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini__cap_6","uri":"capability://automation.workflow.batch.processing.for.cost.reduction.and.throughput.optimization","name":"batch processing for cost reduction and throughput optimization","description":"Supports batch API processing where multiple requests are submitted together and processed asynchronously, typically at 50% lower cost than real-time API calls. Batch processing is optimized for non-urgent inference workloads and can process thousands of requests efficiently by optimizing token utilization across the batch.","intents":["I need to process large volumes of requests (100s-1000s) at lower cost","I can tolerate 24-hour latency for processing in exchange for significant cost savings","I want to optimize token utilization across multiple requests in a single batch"],"best_for":["data processing pipelines that can tolerate 24-hour latency","analytics and reporting workflows processing historical data","cost-sensitive applications with non-real-time requirements"],"limitations":["Batch processing has 24-hour latency; not suitable for real-time applications","Batch API requires JSONL format; complex request structures must be serialized carefully","No streaming support in batch mode; full responses are returned after processing","Batch requests cannot be cancelled once submitted; must wait for completion"],"requires":["OpenAI API key with batch processing enabled","JSONL file format for batch requests","Ability to poll for batch completion status"],"input_types":["JSONL (newline-delimited JSON with request objects)"],"output_types":["JSONL (newline-delimited JSON with response objects)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with o4-mini model access","HTTP/2 capable client for streaming support","Timeout configuration of at least 30 seconds for reasoning completion","OpenAI API key with function calling support","JSON Schema definitions for each tool (OpenAI format)","Client-side tool execution logic (model only generates calls, does not execute)","OpenAI API key with vision capability enabled","Images in JPEG, PNG, WebP, or GIF format","Base64 encoding or URL-accessible image URLs","OpenAI API key with o4-mini access"],"failure_modes":["Reasoning process is not exposed to users — cannot inspect intermediate reasoning steps","Extended thinking adds latency (typically 5-15 seconds per request) compared to standard models","Image resolution and complexity may impact reasoning quality; very large images may be downsampled","Reasoning depth is automatically determined by the model; no user control over reasoning budget","Tool schema must be valid JSON Schema; complex nested schemas may reduce model accuracy","No built-in tool result caching — repeated tool calls with identical inputs are not deduplicated","Tool execution is synchronous; parallel tool calls must be simulated through sequential invocation","Maximum of ~100 tool definitions per request; very large tool registries require filtering","Image resolution is limited; very high-resolution images are downsampled to ~1024x1024 effective resolution","OCR accuracy depends on image quality; handwritten text recognition is less reliable than printed text","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"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":"2026-05-24T12:16:24.485Z","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=openai-o4-mini","compare_url":"https://unfragile.ai/compare?artifact=openai-o4-mini"}},"signature":"CK0FPh4pwnPIHoihkSh2oxHnlKF2rdA7fZGbLW9rKyhZx5ncK1QTQkzHA1ELGebQGbHjcDUDXqgo3Pe3aW5MCQ==","signedAt":"2026-06-20T10:42:11.884Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-o4-mini","artifact":"https://unfragile.ai/openai-o4-mini","verify":"https://unfragile.ai/api/v1/verify?slug=openai-o4-mini","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"}}