{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"o3","slug":"o3","name":"o3","type":"model","url":"https://openai.com/index/openai-o3/","page_url":"https://unfragile.ai/o3","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"o3__cap_0","uri":"capability://planning.reasoning.extended.chain.of.thought.reasoning.with.configurable.compute.allocation","name":"extended-chain-of-thought reasoning with configurable compute allocation","description":"Implements a variable-depth reasoning engine that allocates computational budget across problem-solving steps, allowing users to trade inference cost for solution quality through explicit compute parameters. The model internally expands reasoning chains dynamically, spending more tokens on harder subproblems while maintaining efficiency on simpler steps. This architecture enables breakthrough performance on tasks requiring 10+ logical steps without proportional cost increases for straightforward problems.","intents":["I need to solve a complex math proof but want to control how much I spend on computation","I'm working on a hard coding problem and want the model to think deeper without paying for unnecessary reasoning on easy parts","I need consistent high-quality answers on doctoral-level science questions where reasoning depth directly impacts correctness"],"best_for":["researchers solving competition-level mathematics and science problems","teams building AI systems for complex reasoning tasks where cost-quality tradeoffs matter","developers prototyping advanced code generation systems requiring multi-step logical inference"],"limitations":["Configurable compute allocation adds latency proportional to reasoning depth — no real-time response guarantees","Extended reasoning chains may exceed context windows for very long problem statements or multi-document reasoning","Compute budget allocation is opaque to users — no visibility into which subproblems consumed which budget portions"],"requires":["OpenAI API key with o3 model access","HTTP/REST client capable of handling streaming or polling for long-running inference","Understanding of compute budget parameters (low/medium/high or equivalent cost-quality knobs)"],"input_types":["text prompts","code snippets","mathematical problem statements","scientific questions with context"],"output_types":["text reasoning chains","code solutions","mathematical proofs","structured explanations"],"categories":["planning-reasoning","advanced-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_1","uri":"capability://code.generation.editing.advanced.code.generation.with.multi.step.logical.decomposition","name":"advanced code generation with multi-step logical decomposition","description":"Generates code solutions by internally decomposing problems into logical subcomponents and reasoning through implementation strategies before synthesis. The model applies extended reasoning to understand algorithm correctness, edge cases, and optimization tradeoffs before producing code, resulting in fewer bugs and better algorithmic choices. Supports generation across multiple programming languages with language-specific reasoning about idioms and performance characteristics.","intents":["I need to generate a complex algorithm implementation that handles edge cases correctly","I'm building a system where code quality and correctness matter more than speed, and I want the model to reason through the solution","I need to generate code that's not just syntactically correct but algorithmically optimal for the problem constraints"],"best_for":["teams building production systems where code correctness is critical","competitive programmers solving algorithmic challenges","developers working on security-sensitive or performance-critical code generation"],"limitations":["Extended reasoning for code generation increases latency significantly — not suitable for real-time code completion","Reasoning overhead may not be justified for simple boilerplate or straightforward implementations","Generated code still requires human review for production use; reasoning doesn't guarantee correctness in all edge cases"],"requires":["OpenAI API key with o3 model access","Ability to parse and validate generated code in target language","Sufficient API quota for potentially high token usage from extended reasoning"],"input_types":["natural language problem descriptions","code snippets with requirements","algorithm specifications","pseudocode or design documents"],"output_types":["executable code in target language","code with inline reasoning comments","multiple solution approaches with tradeoff analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_10","uri":"capability://planning.reasoning.system.architecture.design.and.validation","name":"system architecture design and validation","description":"Designs system architectures by reasoning about scalability, reliability, and operational constraints. The model can propose component structures, data flow patterns, and deployment topologies while reasoning about trade-offs between consistency, availability, and partition tolerance. Uses extended reasoning to validate architectural decisions against non-functional requirements.","intents":["I need to design a scalable system architecture for my use case","I want the model to reason about CAP theorem trade-offs and consistency models","I need validation that my architecture meets reliability and performance requirements"],"best_for":["architects designing large-scale systems","teams planning infrastructure migrations","engineers evaluating architectural patterns"],"limitations":["Architecture designs are conceptual — no simulation or empirical validation","Reasoning about very large systems (100+ components) may exceed reasoning budgets","Operational constraints and cost considerations may not be fully captured","No integration with infrastructure-as-code tools"],"requires":["OpenAI API key with o3 access","Clear specification of functional and non-functional requirements","Understanding of distributed systems concepts"],"input_types":["system requirements","scale specifications","reliability requirements","operational constraints"],"output_types":["architecture diagrams (text descriptions)","component specifications","data flow descriptions","deployment topology recommendations"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_2","uri":"capability://planning.reasoning.mathematical.proof.generation.and.verification.reasoning","name":"mathematical proof generation and verification reasoning","description":"Generates formal and informal mathematical proofs by reasoning through logical steps, constraint satisfaction, and proof strategies. The model internally explores proof paths, backtracks on dead ends, and applies domain-specific reasoning about mathematical structures before committing to a proof outline. Supports competitive mathematics problems, theorem proving, and rigorous derivations with explicit step-by-step reasoning chains.","intents":["I need to generate a rigorous proof for a mathematical theorem and want the model to reason through multiple approaches","I'm solving competition math problems and need solutions with complete logical justification","I want to verify whether a mathematical claim is correct by having the model reason through the proof systematically"],"best_for":["mathematicians and researchers working on proof verification","competitive mathematics teams preparing for olympiads or contests","educators building AI tutoring systems for advanced mathematics"],"limitations":["Mathematical reasoning is domain-specific and may fail on novel or cutting-edge mathematics not well-represented in training data","Proofs generated may be correct but not in the most elegant or insightful form","Extended reasoning for complex proofs can exceed practical latency budgets for interactive use"],"requires":["OpenAI API key with o3 model access","Mathematical notation support (LaTeX or equivalent) in client application","Understanding of proof verification to validate generated proofs"],"input_types":["mathematical problem statements","theorem specifications","partial proofs requiring completion","mathematical notation and formulas"],"output_types":["complete formal proofs","step-by-step derivations","proof outlines with reasoning","counterexamples or impossibility arguments"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_3","uri":"capability://planning.reasoning.doctoral.level.scientific.reasoning.and.analysis","name":"doctoral-level scientific reasoning and analysis","description":"Reasons through complex scientific problems requiring domain knowledge integration, hypothesis formation, and multi-step experimental or theoretical analysis. The model applies extended reasoning to synthesize information across scientific domains, evaluate competing explanations, and construct rigorous arguments about scientific phenomena. Supports physics, chemistry, biology, and interdisciplinary problems with reasoning that mirrors expert scientific thinking.","intents":["I need to analyze a complex scientific problem that requires reasoning across multiple domains","I'm working on research and need the model to think through competing hypotheses and their implications","I want to generate scientifically rigorous explanations for phenomena that require deep domain reasoning"],"best_for":["PhD students and researchers working on complex scientific problems","science educators building AI tutoring systems for advanced topics","teams building scientific discovery or analysis tools"],"limitations":["Scientific reasoning is constrained by training data cutoff — may not incorporate very recent discoveries or emerging theories","Extended reasoning doesn't guarantee novel scientific insights; model is bounded by existing knowledge","Domain-specific terminology and notation may require careful prompt engineering to ensure accurate interpretation"],"requires":["OpenAI API key with o3 model access","Scientific notation and formula support in client application","Domain expertise to validate scientific reasoning outputs"],"input_types":["scientific problem statements","research questions","experimental data or observations","scientific literature excerpts"],"output_types":["scientific explanations with reasoning","hypothesis evaluation and comparison","experimental design suggestions","theoretical analysis and derivations"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_4","uri":"capability://planning.reasoning.arc.agi.benchmark.reasoning.and.abstract.problem.solving","name":"arc-agi benchmark reasoning and abstract problem-solving","description":"Solves abstract reasoning and pattern recognition problems from the ARC-AGI benchmark through extended reasoning about visual patterns, logical rules, and transformation operations. The model reasons about grid transformations, object relationships, and implicit rules by exploring hypotheses about pattern structure before predicting outputs. Achieves 87.5% accuracy on ARC-AGI through reasoning that mimics human visual-logical problem-solving.","intents":["I need to solve abstract reasoning problems that require understanding implicit visual and logical patterns","I'm building a system that needs to reason about pattern transformations and rule inference","I want to test whether an AI model can perform human-like abstract reasoning on novel visual-logical problems"],"best_for":["AI researchers evaluating reasoning capabilities on benchmark tasks","teams building pattern recognition and rule inference systems","developers working on abstract problem-solving AI applications"],"limitations":["ARC-AGI reasoning is specialized to grid-based visual-logical problems and may not transfer to other abstract reasoning domains","Extended reasoning for each problem adds significant latency unsuitable for real-time applications","Reasoning process is not fully interpretable — users cannot easily understand which hypotheses the model explored"],"requires":["OpenAI API key with o3 model access","Ability to encode ARC-AGI problems as text or structured format","Grid visualization or parsing capability for input/output validation"],"input_types":["ARC-AGI problem grids (input-output examples)","structured problem descriptions","visual pattern specifications"],"output_types":["predicted output grids","pattern descriptions and rules","reasoning chains explaining inferred rules"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_5","uri":"capability://planning.reasoning.multi.step.task.decomposition.and.planning","name":"multi-step task decomposition and planning","description":"Decomposes complex multi-step tasks into logical subtasks and reasons through execution strategies, dependencies, and resource allocation. The model internally explores task decomposition alternatives, identifies critical path items, and reasons about optimal execution order before providing a plan. Supports tasks spanning code generation, research, analysis, and problem-solving with explicit reasoning about task structure.","intents":["I have a complex project and need the model to break it down into logical steps with reasoning about dependencies","I want to understand not just what steps are needed but why they're ordered that way and what could go wrong","I'm building a system that needs to plan multi-step workflows and I want reasoning about execution strategy"],"best_for":["project managers and team leads using AI for task planning","developers building AI agents that need to decompose complex goals","researchers working on complex multi-phase projects"],"limitations":["Task decomposition reasoning is constrained by model's understanding of domain — may miss domain-specific dependencies","Extended reasoning for planning adds latency unsuitable for real-time task management","Plans generated are suggestions requiring human validation; model cannot execute tasks or adapt plans dynamically"],"requires":["OpenAI API key with o3 model access","Ability to parse and represent task decomposition outputs","Domain knowledge to validate task plans and dependencies"],"input_types":["high-level goal descriptions","project specifications","problem statements","constraint and requirement lists"],"output_types":["task decomposition with dependencies","execution plans with ordering","risk analysis and mitigation strategies","resource allocation recommendations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_6","uri":"capability://planning.reasoning.complex.problem.solving.with.edge.case.reasoning","name":"complex problem-solving with edge case reasoning","description":"Solves complex problems by reasoning through edge cases, boundary conditions, and exceptional scenarios before providing solutions. The model internally explores potential failure modes, validates assumptions, and reasons about robustness before committing to answers. Applies to code generation, mathematical problems, and logical reasoning where edge cases significantly impact correctness.","intents":["I need a solution that handles edge cases correctly and I want the model to reason through potential failure modes","I'm building a system where robustness matters and I need solutions that anticipate boundary conditions","I want to understand not just the main solution but also what could go wrong and how to handle it"],"best_for":["teams building production systems where edge case handling is critical","security-focused development where threat modeling is essential","competitive programming and algorithm design where edge cases determine correctness"],"limitations":["Edge case reasoning is constrained by model's ability to anticipate failure modes — some edge cases may be missed","Extended reasoning for edge case analysis increases latency significantly","Reasoning about edge cases doesn't guarantee all edge cases are covered; human review is still necessary"],"requires":["OpenAI API key with o3 model access","Ability to test and validate edge case handling in generated solutions","Domain expertise to identify critical edge cases"],"input_types":["problem specifications with constraints","code requirements with edge case descriptions","algorithm specifications","system design requirements"],"output_types":["solutions with edge case handling","edge case analysis and test cases","robustness validation reasoning","failure mode descriptions"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_7","uri":"capability://tool.use.integration.api.based.inference.with.configurable.reasoning.budget","name":"api-based inference with configurable reasoning budget","description":"Exposes o3 reasoning capabilities through OpenAI's REST API with parameters allowing users to specify reasoning intensity (low/medium/high or equivalent cost-quality knobs). The API abstracts internal reasoning allocation, handling variable-depth computation transparently while providing consistent response formats. Supports both synchronous and asynchronous inference patterns with streaming or polling for long-running reasoning tasks.","intents":["I want to integrate o3's reasoning capabilities into my application without managing reasoning complexity","I need to control cost-quality tradeoffs at the API level for different problem types","I'm building a system that needs variable reasoning depth for different problem difficulties"],"best_for":["application developers integrating advanced reasoning into products","teams building AI-powered tools that need configurable reasoning depth","researchers evaluating o3 reasoning capabilities at scale"],"limitations":["API latency scales with reasoning budget — high reasoning intensity may exceed practical response time budgets","Reasoning budget parameters are opaque — no visibility into internal reasoning allocation","API rate limits and quota management become critical for high-volume reasoning workloads"],"requires":["OpenAI API key with o3 model access","HTTP/REST client library","Understanding of reasoning budget parameters and cost implications","Handling for long-running inference (polling or streaming)"],"input_types":["text prompts","structured problem descriptions","code snippets","any input supported by o3 model"],"output_types":["text responses","code solutions","structured reasoning chains","streaming or batch results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_8","uri":"capability://planning.reasoning.context.aware.reasoning.with.problem.structure.understanding","name":"context-aware reasoning with problem structure understanding","description":"Reasons about problem structure and context to allocate reasoning resources effectively, spending more computation on genuinely difficult subproblems while maintaining efficiency on straightforward parts. The model internally analyzes problem complexity, identifies critical reasoning points, and adapts reasoning depth accordingly. This enables efficient reasoning that scales with problem difficulty rather than fixed token budgets.","intents":["I have problems of varying difficulty and want the model to spend reasoning effort proportionally","I need efficient reasoning that doesn't waste computation on easy parts of complex problems","I want the model to understand problem structure and allocate reasoning resources intelligently"],"best_for":["teams solving heterogeneous problem sets with varying difficulty","cost-conscious applications where reasoning efficiency matters","systems processing large volumes of problems with mixed complexity"],"limitations":["Problem structure understanding is constrained by model's ability to analyze complexity — some structures may be misunderstood","Adaptive reasoning allocation is not fully transparent — users cannot see which parts received more reasoning","Efficiency gains depend on problem structure; uniform difficulty problems may not benefit from adaptive allocation"],"requires":["OpenAI API key with o3 model access","Clear problem specifications enabling structure analysis","Monitoring of reasoning allocation and cost patterns"],"input_types":["problems with varying complexity","structured problem descriptions","problems with explicit difficulty indicators"],"output_types":["solutions with reasoning depth proportional to difficulty","efficiency metrics and reasoning allocation insights","cost-quality tradeoff analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__cap_9","uri":"capability://code.generation.editing.api.design.and.specification.generation.with.reasoning","name":"api design and specification generation with reasoning","description":"Generates API specifications, schemas, and interface designs by reasoning about use cases, consistency, and extensibility. The model can design RESTful APIs, GraphQL schemas, or gRPC services with consideration for versioning, backward compatibility, and performance. Uses extended reasoning to explore design alternatives and validate consistency across endpoints.","intents":["I need to design an API specification that's consistent and extensible","I want the model to reason about API design patterns and best practices","I need to generate OpenAPI/GraphQL schemas with proper structure"],"best_for":["backend teams designing service APIs","architects planning microservice interfaces","teams building platform SDKs"],"limitations":["Generated APIs are specifications only — no code generation or validation","Design reasoning may not account for performance characteristics of specific backends","Versioning and migration strategies require domain expertise to evaluate","No integration with API documentation tools"],"requires":["OpenAI API key with o3 access","Clear specification of API use cases and requirements","Understanding of API design patterns"],"input_types":["use case descriptions","data model specifications","functional requirements","performance constraints"],"output_types":["OpenAPI specifications","GraphQL schemas","API design documents","endpoint specifications"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3__headline","uri":"capability://planning.reasoning.advanced.reasoning.ai.model","name":"advanced reasoning ai model","description":"OpenAI's O3 is an advanced reasoning AI model designed for complex problem-solving, excelling in multi-step tasks like code generation and scientific analysis, making it ideal for high-level academic and technical challenges.","intents":["best advanced reasoning AI model","advanced reasoning AI model for complex problem solving","top AI model for scientific analysis","AI model for competitive mathematics","best model for multi-step reasoning tasks"],"best_for":["complex problem solving","advanced code generation","scientific analysis"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with o3 model access","HTTP/REST client capable of handling streaming or polling for long-running inference","Understanding of compute budget parameters (low/medium/high or equivalent cost-quality knobs)","Ability to parse and validate generated code in target language","Sufficient API quota for potentially high token usage from extended reasoning","OpenAI API key with o3 access","Clear specification of functional and non-functional requirements","Understanding of distributed systems concepts","Mathematical notation support (LaTeX or equivalent) in client application","Understanding of proof verification to validate generated proofs"],"failure_modes":["Configurable compute allocation adds latency proportional to reasoning depth — no real-time response guarantees","Extended reasoning chains may exceed context windows for very long problem statements or multi-document reasoning","Compute budget allocation is opaque to users — no visibility into which subproblems consumed which budget portions","Extended reasoning for code generation increases latency significantly — not suitable for real-time code completion","Reasoning overhead may not be justified for simple boilerplate or straightforward implementations","Generated code still requires human review for production use; 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