{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-o3","slug":"openai-o3","name":"OpenAI: o3","type":"model","url":"https://openrouter.ai/models/openai~o3","page_url":"https://unfragile.ai/openai-o3","categories":["model-training"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-o3__cap_0","uri":"capability://planning.reasoning.extended.reasoning.chain.of.thought.generation","name":"extended-reasoning-chain-of-thought-generation","description":"Generates multi-step reasoning chains with extended thinking capabilities, allowing the model to work through complex problems by breaking them into intermediate reasoning steps before producing final answers. 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The model applies domain-specific heuristics for calculus, linear algebra, physics, chemistry, and formal logic, with the ability to verify solutions through symbolic computation and dimensional analysis.","intents":["I need to solve a differential equation or complex integral symbolically","I want to verify that a physics or chemistry solution is dimensionally correct","I need to generate a formal proof for a mathematical theorem"],"best_for":["STEM students and educators using AI for homework verification","researchers prototyping mathematical models before implementation","engineers validating calculations for technical designs"],"limitations":["Solutions are not guaranteed to be correct — the model may produce plausible-sounding but mathematically invalid answers, especially for novel or edge-case problems","Cannot perform symbolic computation beyond what was in training data — integration with SymPy or Mathematica requires separate tool calling","Struggles with problems requiring multiple domain-specific knowledge areas (e.g., quantum chemistry + statistical mechanics)"],"requires":["OpenAI API key with o3 model access","Extended thinking enabled for complex problems (recommended)","Optional: integration with symbolic math libraries (SymPy, Mathematica) for verification"],"input_types":["mathematical problem statements","equations in LaTeX or plain text","scientific scenarios and parameters","formal logic statements"],"output_types":["step-by-step solutions","mathematical proofs","numerical answers with units","symbolic expressions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3__cap_3","uri":"capability://text.generation.language.technical.documentation.and.instruction.generation","name":"technical-documentation-and-instruction-generation","description":"Generates precise technical documentation, API specifications, and instruction manuals with high fidelity to domain conventions and standards. 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The model processes visual information through a vision transformer backbone integrated with the language model, enabling it to describe visual content, answer questions about images, and reason about spatial relationships and visual patterns.","intents":["I need to extract data from a chart or graph in an image","I want the model to analyze a screenshot and identify UI elements or issues","I need to understand what's happening in a complex diagram or technical drawing"],"best_for":["data analysts extracting information from visual reports","QA engineers analyzing screenshots for UI bugs","researchers analyzing scientific diagrams and experimental results"],"limitations":["Visual understanding is limited to 2D static images — cannot process video, animations, or 3D models","Accuracy degrades with low-resolution images, heavy compression, or unusual visual styles not well-represented in training data","Cannot perform precise measurements or pixel-level analysis — suitable for semantic understanding but not for CAD or technical drawing analysis requiring exact dimensions"],"requires":["OpenAI API key with vision capability enabled","Image input in JPEG, PNG, WebP, or GIF format","Maximum image size of 20MB per request"],"input_types":["photographs","screenshots","diagrams and charts","graphs and plots","technical drawings","documents with visual elements"],"output_types":["text descriptions","extracted data","answers to visual questions","analysis and insights","structured data from visual inputs"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3__cap_5","uri":"capability://text.generation.language.instruction.following.with.nuanced.constraints","name":"instruction-following-with-nuanced-constraints","description":"Follows complex, multi-part instructions with high fidelity, including nuanced constraints, edge cases, and conditional requirements. The model parses instruction hierarchies, maintains context across long instruction sets, and applies constraints consistently throughout generation, enabling it to handle instructions that require careful attention to detail and conditional logic.","intents":["I need the model to follow a complex set of formatting rules and constraints in its output","I want to specify conditional behavior (e.g., 'if the input contains X, do Y, otherwise do Z')","I need the model to maintain consistency with specific naming conventions or style guidelines throughout a long output"],"best_for":["teams using AI for content generation with strict brand guidelines","developers building AI systems that require precise output formatting","organizations automating workflows with detailed procedural requirements"],"limitations":["Instruction following degrades with instruction sets longer than ~5000 tokens — very long instructions may be partially ignored or misinterpreted","Conflicting or ambiguous instructions may cause the model to prioritize early instructions over later ones, leading to inconsistent behavior","Complex conditional logic in instructions may not be fully understood — the model may miss edge cases or apply conditions inconsistently"],"requires":["OpenAI API key with o3 model access","Clear, well-structured instructions","Optional: examples demonstrating expected behavior"],"input_types":["text instructions","constraint specifications","formatting requirements","conditional logic statements"],"output_types":["text following specified constraints","formatted content","structured outputs matching requirements"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3__cap_6","uri":"capability://code.generation.editing.code.debugging.and.error.analysis","name":"code-debugging-and-error-analysis","description":"Analyzes buggy code, identifies root causes of errors, and generates fixes with explanations of what went wrong and why. The model uses static analysis patterns, common bug signatures, and reasoning about code execution flow to pinpoint issues, then generates corrected code with comments explaining the fix. Supports debugging across multiple languages and frameworks.","intents":["I have a runtime error and need help understanding what's causing it","I want the model to review my code and identify potential bugs before they cause problems","I need to understand why my code is producing incorrect output"],"best_for":["developers debugging complex codebases","teams conducting code reviews with AI assistance","junior developers learning debugging techniques"],"limitations":["Debugging accuracy depends on error messages and context — without clear error messages, the model may suggest incorrect fixes","Cannot debug issues that require runtime state inspection or debugger breakpoints — limited to static analysis and logical reasoning","May miss subtle bugs related to concurrency, memory management, or platform-specific behavior"],"requires":["OpenAI API key with o3 model access","Source code snippet or file","Error message or description of unexpected behavior","Optional: stack trace or reproduction steps"],"input_types":["source code","error messages","stack traces","descriptions of unexpected behavior"],"output_types":["corrected code","explanations of bugs","debugging suggestions","fixed code with comments"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.text","name":"structured-data-extraction-from-unstructured-text","description":"Extracts structured information from unstructured text inputs (documents, emails, articles, etc.) and outputs data in specified formats (JSON, CSV, tables, etc.). The model parses natural language, identifies relevant information, handles missing or ambiguous data, and formats output according to schema specifications provided in prompts.","intents":["I need to extract key information from a document and convert it to JSON","I want to parse a list of items from text and create a structured table","I need to extract entities (names, dates, amounts) from unstructured text"],"best_for":["data teams processing documents and converting to structured formats","business analysts extracting information from reports and emails","teams building data pipelines that require text-to-structured-data conversion"],"limitations":["Extraction accuracy depends on clarity of input text — ambiguous or poorly formatted text may result in missing or incorrect extractions","Cannot extract information that is not explicitly stated in the text — requires inference or external knowledge","Large documents may exceed context window limits — requires chunking or summarization before extraction"],"requires":["OpenAI API key with o3 model access","Unstructured text input","Optional: JSON schema or format specification for output"],"input_types":["unstructured text","documents","emails","articles","transcripts"],"output_types":["JSON","CSV","structured tables","key-value pairs","formatted lists"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3__cap_8","uri":"capability://code.generation.editing.multi.language.code.generation.and.translation","name":"multi-language-code-generation-and-translation","description":"Generates and translates code across 40+ programming languages, including modern languages (Python, JavaScript, Rust) and legacy languages (COBOL, Fortran). The model understands language-specific idioms, standard libraries, and best practices for each language, enabling it to generate idiomatic code rather than direct translations that would be non-functional or inefficient.","intents":["I need to convert code from Python to JavaScript while maintaining functionality","I want to generate the same algorithm in multiple languages for comparison","I need to write code in a language I'm not familiar with"],"best_for":["polyglot developers working across multiple languages","teams maintaining codebases in multiple languages","developers learning new programming languages"],"limitations":["Code translation may not preserve performance characteristics — code that is efficient in one language may be inefficient when translated to another","Language-specific features and idioms may not have direct equivalents — translations may require manual refactoring to be idiomatic","Generated code may not follow language-specific conventions or style guides without explicit instruction"],"requires":["OpenAI API key with o3 model access","Source code in supported language","Optional: target language specification and style guide"],"input_types":["source code in any supported language","algorithm descriptions","pseudocode"],"output_types":["source code in target language","multi-language code samples","language-specific implementations"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3__cap_9","uri":"capability://code.generation.editing.context.aware.code.completion.with.codebase.understanding","name":"context-aware-code-completion-with-codebase-understanding","description":"Provides code completions that understand the broader codebase context, including imported modules, class hierarchies, function signatures, and project-specific conventions. The model analyzes the current file, related files, and project structure to generate completions that are consistent with existing code patterns and architectural decisions, rather than generic completions.","intents":["I want code completions that match my project's coding style and patterns","I need the model to understand my custom classes and functions when suggesting completions","I want completions that are aware of my project's dependencies and imports"],"best_for":["developers working in large, complex codebases","teams with strong code style conventions","projects with custom frameworks or architectural patterns"],"limitations":["Requires access to codebase context — works best when integrated with IDE or code editor, less effective in isolated code snippets","Context window limitations mean very large codebases may not be fully analyzed — the model may miss relevant context from distant files","Completions are probabilistic — the model may suggest valid but unintended completions if context is ambiguous"],"requires":["OpenAI API key with o3 model access","Integration with IDE or code editor (VS Code, JetBrains, etc.)","Access to codebase files for context analysis"],"input_types":["current code file","cursor position","related codebase files","project configuration"],"output_types":["code completions","function suggestions","import statements","code snippets"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with o3 model access","Support for extended_thinking parameter in API calls","Tolerance for variable latency (10-60 second response times)","OpenAI API key with vision capability enabled","Image input in JPEG, PNG, WebP, or GIF format","Maximum image size of 20MB per request","Support for multimodal API endpoints","Extended thinking enabled for complex problems (recommended)","Optional: integration with symbolic math libraries (SymPy, Mathematica) for verification","Source code or specifications to document"],"failure_modes":["Extended reasoning increases latency significantly — queries may take 10-60 seconds vs 1-5 seconds for standard inference","Reasoning tokens are billed separately and at higher rates than standard completion tokens, increasing cost per query by 3-10x","Reasoning output is not always human-readable or structured — internal reasoning may contain model-specific notation","Visual understanding is limited to 2D layouts — 3D rendering, animation timing, and complex interactions may not be accurately inferred from static images","Generated code from visual input requires manual review for accessibility, performance, and security — the model may miss non-visual requirements","Image input adds ~500-1500ms latency compared to text-only code generation","Solutions are not guaranteed to be correct — the model may produce plausible-sounding but mathematically invalid answers, especially for novel or edge-case problems","Cannot perform symbolic computation beyond what was in training data — integration with SymPy or Mathematica requires separate tool calling","Struggles with problems requiring multiple domain-specific knowledge areas (e.g., quantum chemistry + statistical mechanics)","Generated documentation may contain outdated references if the model's training data is older than the codebase being documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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-o3","compare_url":"https://unfragile.ai/compare?artifact=openai-o3"}},"signature":"KTHjR7t3SgOTEooy9Xvl7yz/OhDy5Rc/5D6q3XXgeYTicVIpw9OS/HiHw7jOyjGhKtoPV96iP+h6uaHTe1dGBQ==","signedAt":"2026-06-23T02:16:00.957Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-o3","artifact":"https://unfragile.ai/openai-o3","verify":"https://unfragile.ai/api/v1/verify?slug=openai-o3","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"}}