{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-o3-mini-high","slug":"openai-o3-mini-high","name":"OpenAI: o3 Mini High","type":"model","url":"https://openrouter.ai/models/openai~o3-mini-high","page_url":"https://unfragile.ai/openai-o3-mini-high","categories":["model-training"],"tags":["openai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.10e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-o3-mini-high__cap_0","uri":"capability://planning.reasoning.extended.reasoning.stem.problem.solving","name":"extended-reasoning-stem-problem-solving","description":"Implements OpenAI's chain-of-thought reasoning architecture with high reasoning_effort setting, allocating extended computational budget to internal reasoning steps before generating responses. The model performs multi-step logical decomposition for STEM problems, explicitly working through intermediate reasoning states rather than direct answer generation. This is achieved through a configurable reasoning effort parameter that controls the depth and duration of the internal reasoning process.","intents":["solve complex mathematical proofs requiring multi-step logical derivation","debug scientific reasoning errors by examining intermediate computational steps","generate step-by-step solutions for physics and chemistry problems with full justification","verify correctness of STEM solutions through explicit reasoning chain inspection"],"best_for":["researchers and educators building STEM tutoring systems","teams developing automated scientific problem-solving pipelines","developers creating verification systems for mathematical correctness"],"limitations":["reasoning_effort=high increases latency significantly (typically 5-30 seconds per request vs <1 second for standard models)","extended reasoning incurs higher token consumption and API costs per request","reasoning chains are not directly exposed in API responses — only final answer is returned","performance gains are specific to STEM domains; general language tasks show minimal improvement over standard models"],"requires":["OpenAI API key with o3-mini model access","HTTP client supporting streaming or long-polling for extended response times","understanding that reasoning_effort parameter must be explicitly set to 'high' in API calls"],"input_types":["text (natural language problem statements)","mathematical notation (LaTeX, plain text formulas)","code snippets (for debugging and analysis)"],"output_types":["text (final answer with reasoning summary)","structured reasoning traces (when explicitly requested via API parameters)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini-high__cap_1","uri":"capability://text.generation.language.api.based.text.generation.with.streaming","name":"api-based-text-generation-with-streaming","description":"Provides REST API access to the o3-mini-high model through OpenAI's standard chat completion endpoint, supporting both streaming and non-streaming response modes. Requests are authenticated via API key and transmitted over HTTPS, with responses formatted as JSON containing token usage metadata, finish reasons, and generated text. The streaming variant uses server-sent events (SSE) to deliver tokens incrementally, enabling real-time response rendering in client applications.","intents":["integrate o3-mini-high into web applications with real-time token streaming UI","build backend services that call the model via standard REST patterns","implement token counting and cost estimation before making requests","handle rate limiting and retry logic for production reliability"],"best_for":["backend engineers building LLM-powered APIs and microservices","full-stack developers adding AI capabilities to web applications","teams already invested in OpenAI ecosystem wanting to upgrade reasoning capabilities"],"limitations":["API calls incur per-token costs; high reasoning_effort setting increases token consumption by 2-5x vs standard models","rate limits apply based on subscription tier (typically 3,500 requests/minute for paid accounts)","no local execution option — all requests route through OpenAI infrastructure, introducing network latency and dependency on service availability","streaming responses cannot be cached or replayed without client-side buffering"],"requires":["OpenAI API key with billing enabled","Python 3.8+ (for official SDK) or any language with HTTP client library","network connectivity to api.openai.com","understanding of OpenAI chat completion message format (system/user/assistant roles)"],"input_types":["text (natural language prompts)","structured messages (JSON with role and content fields)","conversation history (multi-turn message arrays)"],"output_types":["text (generated response)","JSON (with usage metadata: prompt_tokens, completion_tokens, total_tokens)","streaming events (SSE format with delta content objects)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini-high__cap_2","uri":"capability://planning.reasoning.cost.optimized.reasoning.for.stem.applications","name":"cost-optimized-reasoning-for-stem-applications","description":"Balances computational cost and reasoning capability through the o3-mini architecture, which uses fewer parameters and optimized inference than o3-full while maintaining extended reasoning for STEM tasks. The high reasoning_effort setting allocates extended computation specifically to STEM reasoning patterns rather than general language understanding, reducing wasted computation on non-STEM queries. Cost is further optimized through selective reasoning — developers can use lower reasoning_effort settings for simpler queries and reserve high effort for complex problems.","intents":["build cost-effective tutoring platforms that handle thousands of student queries monthly","create automated homework verification systems with predictable per-query costs","develop research tools that solve complex equations without enterprise-tier pricing","implement tiered reasoning strategies that use high effort only for difficult problems"],"best_for":["startups and small teams with limited AI budgets building STEM applications","educational institutions deploying AI tutors to large student populations","developers building cost-sensitive scientific research tools"],"limitations":["cost per request is still higher than GPT-4o or Claude 3.5 Sonnet (typically 3-5x more expensive per token)","reasoning_effort=high setting increases costs further; no cost-free tier or free trial available","cost optimization is specific to STEM domains — general language tasks don't benefit from the reasoning architecture","no built-in cost controls or spending caps in API — developers must implement their own rate limiting and budget tracking"],"requires":["OpenAI API key with active billing and sufficient account balance","cost tracking infrastructure (logging token usage per request)","decision logic to determine when high reasoning_effort is necessary vs lower settings"],"input_types":["text (STEM problem statements)","structured problem data (equations, datasets, code)"],"output_types":["text (solution with cost metadata)","usage statistics (tokens consumed, estimated cost)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini-high__cap_3","uri":"capability://text.generation.language.multi.turn.conversation.with.reasoning.context","name":"multi-turn-conversation-with-reasoning-context","description":"Supports multi-turn conversation history where each turn can leverage extended reasoning, maintaining conversation context across multiple exchanges. The model processes the full message history (system prompt + all previous user/assistant messages) before applying reasoning_effort to generate the next response. This enables interactive problem-solving sessions where users can ask follow-up questions, request clarifications, or build on previous reasoning steps without losing context.","intents":["build interactive tutoring chatbots that maintain problem-solving context across multiple exchanges","create debugging assistants that reason about code across multiple clarification rounds","develop scientific collaboration tools where users iteratively refine solutions with AI assistance","implement conversational problem-solving where each turn builds on previous reasoning"],"best_for":["developers building interactive AI tutoring or coaching applications","teams creating conversational debugging or code review tools","researchers developing collaborative scientific problem-solving interfaces"],"limitations":["token usage grows linearly with conversation length; long multi-turn sessions can exceed token limits (128K context window for o3-mini)","reasoning_effort=high is applied to entire conversation context, not just the new query, increasing latency and cost for each turn","no built-in conversation memory persistence — developers must implement their own session storage and history management","reasoning chains from previous turns are not directly accessible; only final answers are retained in conversation history"],"requires":["OpenAI API key with o3-mini model access","session management infrastructure to store and retrieve conversation history","understanding of message role semantics (system, user, assistant) for proper context construction"],"input_types":["text (user messages in multi-turn format)","conversation history (array of message objects with roles and content)"],"output_types":["text (assistant response)","conversation metadata (token usage for current turn, cumulative usage)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini-high__cap_4","uri":"capability://data.processing.analysis.structured.output.with.json.schema.validation","name":"structured-output-with-json-schema-validation","description":"Supports JSON mode and schema-based output constraints through OpenAI's structured output API, allowing developers to specify a JSON schema that the model must adhere to when generating responses. The model generates valid JSON that conforms to the provided schema, with built-in validation ensuring the output matches the specified structure, types, and constraints. This is particularly useful for STEM applications where structured data extraction (equations, solutions, step-by-step breakdowns) is required.","intents":["extract structured solutions from reasoning chains (e.g., step-by-step math solutions as JSON)","generate validated problem-solution pairs for dataset creation and benchmarking","build APIs that return structured STEM results for downstream processing","ensure consistent output format for automated grading and verification systems"],"best_for":["developers building automated grading systems that need structured solution data","teams creating STEM datasets with validated problem-solution pairs","engineers building APIs that require deterministic JSON output from reasoning models"],"limitations":["schema validation adds latency (typically 10-20% overhead) as the model must ensure output conformance","complex nested schemas may reduce reasoning quality if the schema is overly restrictive","schema definition requires careful design to balance structure with flexibility for diverse STEM problems","not all reasoning chains can be cleanly mapped to JSON structures — some multi-step reasoning may be lost in translation"],"requires":["OpenAI API key with structured output support enabled","JSON schema definition matching the desired output structure","understanding of JSON Schema specification (type constraints, required fields, etc.)"],"input_types":["text (STEM problem statements)","JSON schema (defining expected output structure)"],"output_types":["JSON (validated against provided schema)","structured data (equations, solutions, step arrays)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with o3-mini model access","HTTP client supporting streaming or long-polling for extended response times","understanding that reasoning_effort parameter must be explicitly set to 'high' in API calls","OpenAI API key with billing enabled","Python 3.8+ (for official SDK) or any language with HTTP client library","network connectivity to api.openai.com","understanding of OpenAI chat completion message format (system/user/assistant roles)","OpenAI API key with active billing and sufficient account balance","cost tracking infrastructure (logging token usage per request)","decision logic to determine when high reasoning_effort is necessary vs lower settings"],"failure_modes":["reasoning_effort=high increases latency significantly (typically 5-30 seconds per request vs <1 second for standard models)","extended reasoning incurs higher token consumption and API costs per request","reasoning chains are not directly exposed in API responses — only final answer is returned","performance gains are specific to STEM domains; general language tasks show minimal improvement over standard models","API calls incur per-token costs; high reasoning_effort setting increases token consumption by 2-5x vs standard models","rate limits apply based on subscription tier (typically 3,500 requests/minute for paid accounts)","no local execution option — all requests route through OpenAI infrastructure, introducing network latency and dependency on service availability","streaming responses cannot be cached or replayed without client-side buffering","cost per request is still higher than GPT-4o or Claude 3.5 Sonnet (typically 3-5x more expensive per token)","reasoning_effort=high setting increases costs further; no cost-free tier or free trial available","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-05-24T12:16:24.485Z","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-o3-mini-high","compare_url":"https://unfragile.ai/compare?artifact=openai-o3-mini-high"}},"signature":"PqkFB3pFqRhv8USt/CSSJZxWlt3FLioYCnrfULCxkaKn3uKmfzHPiEkSPG1nAUg1FBDXuNzC8ALvaiGsiDXnBA==","signedAt":"2026-06-20T08:35:03.562Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-o3-mini-high","artifact":"https://unfragile.ai/openai-o3-mini-high","verify":"https://unfragile.ai/api/v1/verify?slug=openai-o3-mini-high","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"}}