{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-o3-mini","slug":"openai-o3-mini","name":"OpenAI: o3 Mini","type":"model","url":"https://openrouter.ai/models/openai~o3-mini","page_url":"https://unfragile.ai/openai-o3-mini","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__cap_0","uri":"capability://planning.reasoning.stem.optimized.reasoning.with.configurable.computational.budget","name":"stem-optimized reasoning with configurable computational budget","description":"Implements a reasoning architecture that allocates variable computational resources to problem-solving based on the `reasoning_effort` parameter (low/medium/high), enabling the model to spend more inference-time tokens on complex mathematical, scientific, and coding problems. The model uses an internal chain-of-thought mechanism that scales with effort level, allowing developers to trade latency and cost for solution quality on domain-specific tasks.","intents":["I need to solve complex math problems programmatically with variable accuracy-vs-cost tradeoffs","I want to generate correct code solutions for algorithmic challenges without paying for full o1-level reasoning","I need to verify scientific calculations and derivations with tunable confidence levels","I'm building a tutoring system that explains STEM concepts with varying depth based on user needs"],"best_for":["developers building educational platforms for STEM subjects","teams implementing competitive programming solution generators","researchers prototyping scientific computing workflows with cost constraints","startups building homework-help or tutoring applications"],"limitations":["Reasoning effort parameter only optimizes for STEM domains; general language tasks see minimal benefit from higher effort levels","Higher reasoning_effort settings increase latency significantly (estimated 5-15x slower than standard inference) and token consumption proportionally","No guarantee of correctness even at maximum effort; still subject to hallucination on novel or adversarial problems","Reasoning tokens are billed at premium rates; cost per request can exceed standard models by 10-50x depending on effort level"],"requires":["OpenAI API key with o3-mini model access","HTTP client capable of handling streaming or long-polling responses (reasoning can take 30+ seconds)","Understanding of reasoning_effort parameter semantics (low/medium/high) for your use case"],"input_types":["text (natural language problem statements)","code (snippets for debugging or optimization)","mathematical notation (LaTeX or plain text formulas)","scientific problem descriptions"],"output_types":["text (reasoning steps and final answer)","code (generated solutions with explanations)","structured reasoning traces (if parsed from response)","mathematical derivations"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini__cap_1","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing.support","name":"api-based inference with streaming and batch processing support","description":"Provides access to o3-mini through OpenAI's REST API endpoints, supporting both real-time streaming responses (Server-Sent Events) and batch processing via OpenAI's Batch API. The model integrates with OpenRouter's proxy layer, which abstracts authentication, rate limiting, and multi-provider fallback logic, allowing developers to call o3-mini through a unified interface without managing OpenAI credentials directly.","intents":["I want to integrate o3-mini into my application without managing OpenAI API keys myself","I need to stream reasoning steps to users in real-time as the model thinks through problems","I'm processing thousands of STEM problems overnight and need cost-optimized batch inference","I want to switch between o3-mini and other reasoning models (o1, Claude) with minimal code changes"],"best_for":["SaaS platforms that want to offer reasoning capabilities without exposing API keys to users","teams building interactive tutoring or coding interview prep tools requiring real-time feedback","batch processing pipelines for scientific data analysis or homework verification at scale","multi-model applications leveraging OpenRouter's unified API abstraction"],"limitations":["OpenRouter adds ~50-200ms latency per request due to proxy overhead and request routing","Streaming responses may have higher latency variance than direct OpenAI API calls due to intermediate routing","Batch API has 24-hour processing SLA; not suitable for real-time applications requiring sub-second responses","Rate limits depend on OpenRouter's tier; may be more restrictive than direct OpenAI API access for high-volume users"],"requires":["OpenRouter API key (free tier available with limited usage)","HTTP client library supporting streaming (e.g., fetch, requests, httpx)","Understanding of OpenAI API request/response format (messages, model parameter, etc.)","Network connectivity to OpenRouter endpoints"],"input_types":["JSON (OpenAI-compatible message format with system/user/assistant roles)","text (raw problem statements converted to messages)"],"output_types":["JSON (streaming chunks with delta content and finish_reason)","text (complete response after streaming completes)","structured batch results (for batch API submissions)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini__cap_2","uri":"capability://planning.reasoning.cost.optimized.stem.problem.solving.with.variable.quality.tiers","name":"cost-optimized stem problem solving with variable quality tiers","description":"Implements a tiered inference strategy where the `reasoning_effort` parameter maps to different computational budgets, allowing developers to solve STEM problems at three distinct cost-quality points: low effort (minimal reasoning, lowest cost), medium effort (balanced reasoning), and high effort (maximum reasoning, highest cost). The model internally allocates more inference-time tokens at higher effort levels, enabling fine-grained cost control without requiring multiple model calls or manual prompt engineering.","intents":["I want to offer multiple solution quality tiers to users (quick answer vs detailed explanation) with corresponding pricing","I need to optimize costs for a high-volume STEM tutoring platform by using low effort for simple problems and high effort for complex ones","I'm building a system that automatically selects reasoning effort based on problem difficulty to minimize cost","I want to A/B test different reasoning budgets to find the optimal cost-quality tradeoff for my use case"],"best_for":["SaaS platforms monetizing STEM solutions with tiered pricing models","educational platforms optimizing cost per student interaction","research teams with fixed budgets exploring reasoning model capabilities","developers building adaptive systems that adjust reasoning depth based on problem complexity"],"limitations":["No programmatic way to determine optimal effort level for a given problem; requires manual testing or heuristics","Effort levels are discrete (low/medium/high), not continuous; fine-grained cost control requires external logic","Higher effort does not guarantee correctness; may waste budget on inherently unsolvable or ambiguous problems","No built-in cost estimation; developers must track token usage separately to predict expenses"],"requires":["OpenAI API key or OpenRouter API key with o3-mini access","Ability to parse and track token usage from API responses (usage.prompt_tokens, usage.completion_tokens)","Cost calculation logic: (prompt_tokens * prompt_rate + completion_tokens * completion_rate) per effort level","Monitoring infrastructure to track actual vs budgeted costs across effort levels"],"input_types":["text (STEM problem statements)","code (programming challenges)","mathematical expressions"],"output_types":["text (solutions with reasoning traces)","code (generated implementations)","metadata (token counts, effort level used, cost incurred)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini__cap_3","uri":"capability://text.generation.language.multi.domain.language.understanding.with.stem.specialization","name":"multi-domain language understanding with stem specialization","description":"Implements a transformer-based architecture trained on diverse text corpora with specialized fine-tuning for STEM domains (mathematics, physics, chemistry, computer science), enabling the model to handle general language tasks while excelling at technical reasoning. The model maintains general-purpose capabilities (summarization, translation, creative writing) while applying domain-specific optimizations during inference for STEM problems, allowing developers to use a single model for mixed workloads without domain-specific routing.","intents":["I need a single model that can handle both general chat and technical problem-solving without switching models","I want to build a homework helper that explains concepts in plain English and solves problems accurately","I'm creating a documentation generator that can write prose and generate correct code examples","I need a model that understands scientific papers and can answer questions about them"],"best_for":["general-purpose applications that occasionally need STEM reasoning","educational platforms covering multiple subjects with varying technical depth","documentation and content generation tools that mix prose and code","research assistants that need to understand and summarize scientific literature"],"limitations":["STEM specialization may reduce performance on non-technical domains compared to models optimized purely for general language (e.g., GPT-4 Turbo for creative writing)","No explicit domain detection; developers must manually specify reasoning_effort or rely on heuristics to route problems appropriately","Performance on non-English STEM content is not documented; likely weaker than English due to training data distribution","General language tasks don't benefit from reasoning_effort parameter; setting high effort on non-STEM queries wastes tokens"],"requires":["OpenAI API key or OpenRouter access","Understanding of when to apply reasoning_effort (STEM tasks) vs standard inference (general language)","Ability to parse and validate responses for correctness in technical domains"],"input_types":["text (natural language questions, prompts, documents)","code (snippets for explanation or debugging)","mathematical notation","scientific problem descriptions"],"output_types":["text (explanations, summaries, translations, creative content)","code (generated solutions, refactored implementations)","structured reasoning (for STEM tasks)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini__cap_4","uri":"capability://planning.reasoning.inference.time.token.scaling.for.adaptive.reasoning.depth","name":"inference-time token scaling for adaptive reasoning depth","description":"Implements a mechanism where the `reasoning_effort` parameter controls the number of internal reasoning tokens (chain-of-thought steps) allocated during inference, without requiring changes to the prompt or model weights. At low effort, the model generates fewer intermediate reasoning steps and reaches conclusions faster; at high effort, it explores more solution paths and validates answers more thoroughly. This is implemented as a runtime parameter that scales the model's internal computation budget, not as a prompt engineering technique.","intents":["I want to solve simple problems quickly and complex problems thoroughly without changing my prompt","I need to understand how much reasoning a model is doing internally for a given problem","I'm optimizing latency for a real-time application and want to reduce reasoning depth for fast responses","I want to debug why a model is failing on a problem by increasing reasoning effort to see if more computation helps"],"best_for":["developers building latency-sensitive applications that can trade accuracy for speed","teams debugging model failures by systematically increasing reasoning budget","applications with variable SLAs (some requests need fast responses, others can wait)","researchers studying the relationship between inference-time computation and reasoning quality"],"limitations":["Reasoning tokens are not directly observable; developers cannot inspect the internal chain-of-thought steps, only the final output","Effort parameter is discrete (low/medium/high), not continuous; no fine-grained control over exact token budgets","Latency scales non-linearly with effort; high effort may take 10-20x longer than low effort, making real-time applications infeasible","No guarantee that increased effort improves correctness; some problems may be fundamentally unsolvable regardless of reasoning budget"],"requires":["OpenAI API key or OpenRouter access","HTTP client capable of handling long-running requests (30+ second timeouts for high effort)","Monitoring infrastructure to track latency and token usage per effort level","Understanding that reasoning_effort is a runtime parameter, not a prompt engineering technique"],"input_types":["text (problem statements)","code (debugging tasks)","mathematical expressions"],"output_types":["text (final answer with reasoning trace)","metadata (total tokens used, latency, effort level applied)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini__cap_5","uri":"capability://data.processing.analysis.structured.output.generation.for.stem.solutions","name":"structured output generation for stem solutions","description":"Enables the model to generate responses in structured formats (JSON, XML, or markdown with specific schemas) for STEM problems, allowing developers to parse solutions programmatically and extract components like intermediate steps, final answers, confidence scores, and explanations. The model uses constrained decoding or output formatting instructions to ensure responses conform to expected schemas, enabling downstream processing without manual parsing.","intents":["I need to extract the final answer from a math problem solution and validate it against expected output","I want to build a system that grades homework by comparing generated solutions to answer keys","I need to log reasoning steps separately from final answers for analytics and debugging","I'm building an API that returns solutions in a specific JSON format for client applications"],"best_for":["automated grading systems that need to parse and validate solutions","APIs that expose o3-mini capabilities with structured response contracts","analytics platforms tracking reasoning quality and solution correctness","applications that need to extract specific components (steps, answers, confidence) from solutions"],"limitations":["Structured output is not enforced by the model; developers must validate responses and handle malformed outputs","No built-in schema validation; requires external JSON schema validators or custom parsing logic","Structured output may increase latency slightly due to formatting constraints during generation","Complex schemas may reduce model performance; simpler structures are more reliable"],"requires":["OpenAI API key or OpenRouter access","JSON schema or output format specification (e.g., 'respond in JSON with keys: steps, answer, confidence')","JSON parsing library (json, pydantic, zod, etc.) for response validation","Error handling for malformed responses (fallback to text parsing or retry logic)"],"input_types":["text (problem statements with format instructions)","JSON schema (optional, for specifying expected output structure)"],"output_types":["JSON (structured solutions with steps, answers, metadata)","XML (alternative structured format)","markdown (formatted text with specific structure)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini__cap_6","uri":"capability://memory.knowledge.context.aware.problem.solving.with.multi.turn.conversations","name":"context-aware problem solving with multi-turn conversations","description":"Maintains conversation history across multiple turns, allowing developers to build interactive problem-solving sessions where the model can reference previous problems, solutions, and clarifications. The model uses the message history to build context about the user's learning level, problem domain, and preferred explanation style, enabling more personalized and coherent responses across multiple interactions without requiring explicit context injection.","intents":["I want to build a tutoring chatbot that remembers previous problems and adapts explanations based on student progress","I need to implement a debugging session where the model can reference earlier code snippets and error messages","I'm creating a homework helper that tracks which topics a student struggles with and provides targeted help","I want to enable users to ask follow-up questions about solutions without re-explaining the entire problem"],"best_for":["interactive tutoring and homework help applications","debugging assistants that need to maintain context across multiple code iterations","educational platforms tracking student progress and adapting difficulty","conversational AI systems for STEM education"],"limitations":["Conversation history increases token usage linearly; long conversations become expensive and slow","Model may lose context or become confused with very long conversation histories (100+ turns); no built-in summarization","No persistent memory between sessions; conversation history must be stored externally and reloaded","Context window is limited (likely 128k tokens for o3-mini); very long conversations may exceed limits"],"requires":["OpenAI API key or OpenRouter access","Conversation history storage (in-memory, database, or file system)","Message formatting following OpenAI's role-based structure (system, user, assistant)","Token counting logic to monitor conversation size and manage context window"],"input_types":["text (user messages in multi-turn format)","conversation history (array of messages with roles and content)"],"output_types":["text (model responses in conversation context)","conversation state (updated history for next turn)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini__cap_7","uri":"capability://code.generation.editing.code.generation.and.debugging.with.stem.optimized.reasoning","name":"code generation and debugging with stem-optimized reasoning","description":"Generates, debugs, and optimizes code for algorithmic and scientific computing problems by applying the model's STEM reasoning capabilities to programming tasks. The model can generate correct implementations for competitive programming problems, debug runtime errors by reasoning about code execution, and suggest optimizations based on algorithmic analysis. The reasoning_effort parameter scales the depth of algorithmic analysis, enabling developers to trade off code quality for latency.","intents":["I want to generate correct solutions for competitive programming problems with explanations","I need to debug a complex algorithm and understand why it's producing incorrect results","I'm building a code review tool that analyzes algorithmic correctness and suggests optimizations","I want to generate efficient implementations for mathematical algorithms (sorting, graph traversal, dynamic programming)"],"best_for":["competitive programming platforms and interview prep tools","educational platforms teaching algorithms and data structures","code review and quality assurance tools for scientific computing","automated code generation for mathematical and scientific libraries"],"limitations":["Generated code may have subtle bugs or edge case failures; always requires testing and validation","Model may generate inefficient code even with high reasoning effort; algorithmic optimization is not guaranteed","Code generation quality varies significantly by language; Python and JavaScript are likely better supported than Rust or Go","No built-in code execution or testing; developers must run generated code and validate correctness"],"requires":["OpenAI API key or OpenRouter access","Code testing framework (pytest, Jest, etc.) to validate generated solutions","Understanding of the target programming language and problem domain","Ability to parse and extract code from model responses (may be embedded in markdown or text)"],"input_types":["text (problem statements in natural language)","code (existing implementations to debug or optimize)","pseudocode (algorithm descriptions to implement)"],"output_types":["code (generated implementations with comments)","text (explanations of algorithmic approach)","debugging traces (step-by-step execution analysis)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-mini__cap_8","uri":"capability://text.generation.language.mathematical.problem.solving.with.step.by.step.derivations","name":"mathematical problem solving with step-by-step derivations","description":"Solves mathematical problems (algebra, calculus, linear algebra, discrete math) by generating step-by-step derivations that show intermediate calculations and reasoning. The model uses symbolic reasoning to manipulate equations, apply mathematical rules, and validate solutions. The reasoning_effort parameter controls the depth of derivation detail, allowing developers to generate quick answers or detailed educational explanations.","intents":["I want to generate detailed solutions to math problems for homework help or tutoring","I need to verify mathematical derivations and check for errors in student work","I'm building an educational platform that explains math concepts with step-by-step solutions","I want to generate practice problems with worked solutions for students to learn from"],"best_for":["online tutoring and homework help platforms","educational content generation for math courses","automated grading systems that need to verify mathematical correctness","math learning applications with interactive problem solving"],"limitations":["Mathematical notation in responses may be plain text or LaTeX; requires parsing or rendering infrastructure","Model may make algebraic errors or take inefficient solution paths; solutions should be verified independently","Complex multi-step problems may exceed token limits or produce incomplete derivations","No symbolic math engine; cannot guarantee algebraic correctness without external validation (e.g., SymPy)"],"requires":["OpenAI API key or OpenRouter access","LaTeX or mathematical notation rendering (for displaying solutions to users)","Optional: SymPy or similar symbolic math library for validating solutions","Understanding of mathematical notation and problem types"],"input_types":["text (math problems in natural language or LaTeX)","mathematical expressions (equations, inequalities, systems)"],"output_types":["text (step-by-step derivations with explanations)","LaTeX (formatted mathematical notation)","structured solutions (steps as separate components)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with o3-mini model access","HTTP client capable of handling streaming or long-polling responses (reasoning can take 30+ seconds)","Understanding of reasoning_effort parameter semantics (low/medium/high) for your use case","OpenRouter API key (free tier available with limited usage)","HTTP client library supporting streaming (e.g., fetch, requests, httpx)","Understanding of OpenAI API request/response format (messages, model parameter, etc.)","Network connectivity to OpenRouter endpoints","OpenAI API key or OpenRouter API key with o3-mini access","Ability to parse and track token usage from API responses (usage.prompt_tokens, usage.completion_tokens)","Cost calculation logic: (prompt_tokens * prompt_rate + completion_tokens * completion_rate) per effort level"],"failure_modes":["Reasoning effort parameter only optimizes for STEM domains; 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