{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-5.3-codex","slug":"openai-gpt-5.3-codex","name":"OpenAI: GPT-5.3-Codex","type":"model","url":"https://openrouter.ai/models/openai~gpt-5.3-codex","page_url":"https://unfragile.ai/openai-gpt-5.3-codex","categories":["model-training"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.75e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-5.3-codex__cap_0","uri":"capability://code.generation.editing.agentic.code.generation.with.reasoning","name":"agentic-code-generation-with-reasoning","description":"Generates production-grade code by combining GPT-5.2-Codex's specialized software engineering patterns with GPT-5.2's frontier reasoning capabilities. The model uses chain-of-thought decomposition to break complex coding tasks into sub-problems, reasoning through architectural decisions before generating implementation, enabling multi-step refactoring and cross-file dependency resolution in a single agentic loop.","intents":["Generate complete feature implementations from natural language specifications with architectural reasoning","Refactor large codebases while maintaining consistency across multiple files and modules","Debug complex issues by reasoning through execution flow and state management","Design and implement system components with consideration for scalability and maintainability"],"best_for":["Enterprise engineering teams building complex software systems requiring architectural coherence","Solo developers and startups needing to accelerate feature development without sacrificing code quality","Teams migrating legacy systems where reasoning about dependencies and refactoring patterns is critical"],"limitations":["Context window constraints limit the size of codebases that can be reasoned about in a single request","Reasoning overhead adds latency compared to direct code generation models — typical response time 5-15 seconds for complex tasks","No built-in version control integration — requires external tooling to track generated code changes","Training data cutoff means knowledge of very recent frameworks and libraries may be incomplete"],"requires":["OpenAI API key with GPT-5.3-Codex model access","HTTP client capable of handling streaming responses","Sufficient API quota for agentic multi-turn interactions","Code context provided as text (no automatic repository indexing)"],"input_types":["natural language specifications","existing code snippets","error messages and stack traces","architectural requirements and constraints"],"output_types":["executable code in 50+ programming languages","refactored code with explanations","architectural diagrams and design rationales","test cases and validation logic"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_1","uri":"capability://code.generation.editing.multi.language.code.completion.with.context.awareness","name":"multi-language-code-completion-with-context-awareness","description":"Provides intelligent code completion across 50+ programming languages by leveraging GPT-5.2-Codex's specialized training on diverse codebases. The model maintains awareness of surrounding code context, imported modules, and type signatures to predict the most contextually appropriate next tokens, supporting both line-level and block-level completions with semantic understanding of language-specific idioms.","intents":["Complete function implementations based on signature and docstring context","Auto-fill boilerplate code patterns specific to the current language and framework","Suggest next logical code blocks based on control flow and variable usage","Provide completions that respect existing code style and naming conventions"],"best_for":["Polyglot development teams working across multiple languages and frameworks","Individual developers seeking IDE-like completion without language-specific plugins","Teams standardizing on API-first code generation for CI/CD pipelines"],"limitations":["Completion quality degrades for domain-specific or proprietary languages with limited training data","No real-time streaming completion in all client implementations — some integrations batch requests","Requires sufficient context (typically 50+ lines) for high-quality predictions; short snippets may produce generic completions","No built-in linting or syntax validation — generated completions may require post-processing"],"requires":["OpenAI API key with GPT-5.3-Codex access","Code context provided as text or via IDE integration","Language identifier or file extension for context optimization","HTTP client supporting streaming for real-time completion"],"input_types":["partial code with cursor position","function signatures and type hints","docstrings and comments","surrounding code context (preceding and following lines)"],"output_types":["code tokens and completions","multiple completion suggestions ranked by confidence","completion metadata (confidence score, alternative suggestions)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_10","uri":"capability://code.generation.editing.performance.optimization.and.refactoring.suggestions","name":"performance-optimization-and-refactoring-suggestions","description":"Analyzes code for performance bottlenecks and suggests optimizations by reasoning about algorithmic complexity, memory usage, and execution patterns. The model identifies inefficient patterns, suggests algorithmic improvements, and generates refactored code with performance analysis showing expected improvements in time and space complexity.","intents":["Identify algorithmic inefficiencies and suggest more efficient algorithms","Optimize hot paths and critical sections for performance","Suggest caching, memoization, or other optimization strategies","Refactor code for better memory usage and reduced allocations"],"best_for":["Performance-critical systems where optimization significantly impacts user experience","Teams with performance regressions seeking to identify and fix bottlenecks","Developers learning optimization techniques and best practices","Systems approaching resource limits where efficiency improvements are necessary"],"limitations":["Optimization suggestions depend on actual workload patterns and data characteristics — generic suggestions may not apply","Premature optimization can reduce code readability — requires judgment about when optimization is justified","Profiling data is needed for accurate identification of bottlenecks — code analysis alone may miss runtime issues","Optimization trade-offs (speed vs memory, readability vs performance) require domain knowledge to evaluate"],"requires":["OpenAI API key with GPT-5.3-Codex access","Source code to be optimized","Optional: profiling data or performance metrics","Optional: performance requirements and constraints"],"input_types":["source code functions and algorithms","profiling data and performance metrics","workload characteristics and data patterns","performance requirements and constraints"],"output_types":["optimized code with complexity analysis","algorithmic improvements and explanations","caching and memoization strategies","performance comparison and expected improvements"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_2","uri":"capability://code.generation.editing.code.review.and.quality.analysis.with.reasoning","name":"code-review-and-quality-analysis-with-reasoning","description":"Analyzes code for bugs, performance issues, security vulnerabilities, and style violations by applying reasoning-based inspection patterns. The model examines code structure, data flow, and execution paths to identify subtle issues that regex-based linters miss, providing explanations for each finding and suggesting specific fixes with architectural context.","intents":["Identify security vulnerabilities and potential exploits in code before deployment","Detect performance bottlenecks and suggest optimization strategies with complexity analysis","Review code for adherence to team standards and architectural patterns","Explain why specific code patterns are problematic and suggest idiomatic alternatives"],"best_for":["Security-conscious teams requiring deep code analysis beyond static linters","Performance-critical systems where algorithmic complexity and resource usage matter","Teams standardizing code quality across polyglot codebases with varying conventions","Startups lacking dedicated security and code review expertise"],"limitations":["Analysis latency (5-15 seconds) makes real-time IDE integration impractical — best suited for pre-commit or CI/CD stages","May produce false positives for domain-specific patterns or intentional design choices not obvious from code alone","Cannot detect runtime issues that depend on external state or network conditions","Requires sufficient code context (typically 200+ lines) for meaningful analysis; isolated snippets may miss systemic issues"],"requires":["OpenAI API key with GPT-5.3-Codex access","Complete code file or module (not just snippets)","Optional: architectural documentation or style guide for context","HTTP client for API communication"],"input_types":["source code in any supported language","error logs and stack traces","performance metrics or profiling data","architectural constraints or requirements"],"output_types":["structured list of issues with severity levels","code snippets highlighting problematic patterns","suggested fixes with explanations","performance analysis and complexity estimates"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_3","uri":"capability://code.generation.editing.test.generation.and.coverage.optimization","name":"test-generation-and-coverage-optimization","description":"Generates comprehensive test suites by analyzing code structure, control flow, and edge cases using reasoning-based test design patterns. The model identifies critical paths, boundary conditions, and error scenarios, then generates unit tests, integration tests, and property-based tests with appropriate assertions and setup/teardown logic for the target testing framework.","intents":["Generate unit tests that cover critical paths and edge cases in existing code","Create integration tests that validate interactions between multiple modules","Design property-based tests for functions with complex input domains","Identify untested code paths and suggest test cases to improve coverage"],"best_for":["Teams with low test coverage seeking to improve reliability without manual test writing","Projects migrating to new testing frameworks and needing test suite refactoring","Quality-focused teams requiring comprehensive test documentation and coverage analysis","Startups needing rapid test generation to meet release deadlines"],"limitations":["Generated tests may not capture domain-specific business logic or acceptance criteria not evident from code","Mock and stub generation requires manual refinement for complex external dependencies","Test quality depends heavily on code clarity — poorly documented code produces weak tests","Cannot generate tests for non-deterministic or time-dependent code without additional context"],"requires":["OpenAI API key with GPT-5.3-Codex access","Source code to be tested","Target testing framework specification (Jest, pytest, JUnit, etc.)","Optional: existing test examples for style matching"],"input_types":["source code functions and classes","function signatures and type definitions","docstrings and comments describing intended behavior","existing test examples for pattern matching"],"output_types":["executable test code in target framework","test case descriptions and coverage analysis","mock and stub definitions","coverage reports and gap analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_4","uri":"capability://code.generation.editing.natural.language.to.code.translation.with.specification.inference","name":"natural-language-to-code-translation-with-specification-inference","description":"Translates natural language requirements and specifications into executable code by inferring architectural decisions, design patterns, and implementation details from context. The model uses reasoning to decompose requirements into components, validate feasibility, and generate code that balances correctness with maintainability, supporting iterative refinement through follow-up clarifications.","intents":["Convert user stories and requirements into working code implementations","Generate boilerplate and scaffolding for new projects based on high-level descriptions","Implement algorithms from mathematical or algorithmic descriptions","Create API endpoints and database schemas from business requirements"],"best_for":["Non-technical product managers and business analysts needing to prototype ideas","Rapid prototyping and MVP development where speed matters more than optimization","Teams documenting requirements in natural language and needing code generation","Educational contexts where students learn by seeing code generated from specifications"],"limitations":["Ambiguous requirements produce unpredictable code — specifications must be detailed enough to constrain implementation","Generated code may not match team conventions or architectural patterns without explicit guidance","Performance and scalability are not optimized unless explicitly mentioned in requirements","Requires multiple iterations to refine generated code to production quality"],"requires":["OpenAI API key with GPT-5.3-Codex access","Clear, detailed natural language specification","Target programming language and framework specification","Optional: existing codebase examples for style and pattern matching"],"input_types":["natural language requirements and user stories","acceptance criteria and test cases","architectural constraints and technology choices","example code or design patterns to follow"],"output_types":["executable code in specified language","generated documentation and comments","implementation notes and design decisions","suggested test cases and validation logic"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_5","uri":"capability://code.generation.editing.cross.language.code.translation.with.idiom.preservation","name":"cross-language-code-translation-with-idiom-preservation","description":"Translates code between programming languages while preserving semantic meaning and adapting to target language idioms and best practices. The model understands language-specific patterns, standard libraries, and performance characteristics, generating idiomatic code rather than mechanical translations that would be inefficient or unreadable in the target language.","intents":["Port codebases from one language to another while maintaining functionality","Migrate legacy code to modern languages with improved performance and maintainability","Generate language-specific implementations from a reference implementation","Understand code written in unfamiliar languages by translating to a known language"],"best_for":["Teams migrating from legacy languages (COBOL, Fortran) to modern stacks","Polyglot organizations needing to maintain code in multiple languages","Open source projects seeking to provide implementations in multiple languages","Performance-critical systems where language choice significantly impacts efficiency"],"limitations":["Language-specific libraries and APIs may not have direct equivalents — requires manual mapping","Performance characteristics vary significantly across languages; translated code may not meet performance requirements","Idioms and conventions differ substantially; translated code may be readable but not idiomatic","Type systems and memory models differ; translations may introduce subtle bugs if not carefully reviewed"],"requires":["OpenAI API key with GPT-5.3-Codex access","Source code in supported language","Target language specification","Optional: performance requirements and library constraints"],"input_types":["source code in any supported language","library and dependency specifications","performance requirements and constraints","target language version and available libraries"],"output_types":["translated code in target language","mapping of source to target library equivalents","performance analysis and optimization suggestions","migration notes and compatibility warnings"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_6","uri":"capability://code.generation.editing.debugging.and.error.diagnosis.with.execution.reasoning","name":"debugging-and-error-diagnosis-with-execution-reasoning","description":"Diagnoses bugs and errors by reasoning about code execution flow, state changes, and data flow to identify root causes rather than just symptoms. The model analyzes error messages, stack traces, and code context to trace execution paths, identify invariant violations, and suggest specific fixes with explanations of why the bug occurred and how to prevent similar issues.","intents":["Identify root causes of runtime errors and exceptions from stack traces","Debug logic errors by tracing execution flow and variable state changes","Diagnose performance issues and bottlenecks from profiling data","Suggest fixes for bugs with explanations of the underlying cause"],"best_for":["Developers debugging complex issues in unfamiliar codebases","Teams with limited debugging expertise seeking guidance on error diagnosis","Performance-critical systems where bottleneck identification is crucial","Rapid development environments where debugging speed impacts productivity"],"limitations":["Requires sufficient context (error message, stack trace, relevant code) — incomplete information produces inaccurate diagnoses","Cannot debug issues that depend on external state, network conditions, or timing — requires reproduction steps","May suggest fixes that address symptoms rather than root causes if context is incomplete","Debugging latency (5-15 seconds) makes real-time debugging impractical"],"requires":["OpenAI API key with GPT-5.3-Codex access","Error message or stack trace","Relevant source code context","Optional: reproduction steps, profiling data, or logs"],"input_types":["error messages and stack traces","source code context","profiling data and performance metrics","reproduction steps and test cases","logs and debug output"],"output_types":["root cause analysis and explanation","suggested fixes with code changes","prevention strategies and best practices","related issues and edge cases to consider"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_7","uri":"capability://code.generation.editing.documentation.generation.and.code.explanation","name":"documentation-generation-and-code-explanation","description":"Generates comprehensive documentation by analyzing code structure, function signatures, and implementation details to create accurate docstrings, API documentation, and architectural guides. The model understands code intent from context and generates explanations at multiple levels of detail, from inline comments to full architectural documentation with examples and usage patterns.","intents":["Generate docstrings and inline comments for undocumented code","Create API documentation with examples and usage patterns","Generate architectural documentation explaining system design and component interactions","Explain complex code sections to developers unfamiliar with the codebase"],"best_for":["Teams with legacy codebases lacking documentation seeking to improve maintainability","Open source projects needing comprehensive documentation for users and contributors","Organizations standardizing documentation across polyglot codebases","Rapid development teams where documentation lags behind code changes"],"limitations":["Generated documentation may be inaccurate if code is poorly written or has unclear intent","Cannot document business logic or domain-specific context not evident from code","Generated examples may not cover all use cases or edge cases","Requires manual review and refinement to ensure accuracy and completeness"],"requires":["OpenAI API key with GPT-5.3-Codex access","Source code to be documented","Optional: existing documentation examples for style matching","Optional: architectural diagrams or design documents for context"],"input_types":["source code functions, classes, and modules","function signatures and type definitions","existing comments and docstrings","architectural context and design documents"],"output_types":["docstrings in target format (JSDoc, Sphinx, etc.)","API documentation with examples","architectural documentation and diagrams","usage guides and best practices"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_8","uri":"capability://code.generation.editing.schema.aware.api.and.database.generation","name":"schema-aware-api-and-database-generation","description":"Generates API endpoints, database schemas, and ORM models from natural language specifications or existing data structures using reasoning about data relationships and access patterns. The model understands normalization principles, indexing strategies, and API design patterns to generate schemas that are both correct and performant, with support for migrations and version management.","intents":["Generate REST or GraphQL API definitions from business requirements","Create database schemas with appropriate normalization and indexing","Generate ORM models and migrations for database changes","Design API versioning and backward compatibility strategies"],"best_for":["Backend teams rapidly prototyping APIs and database designs","Startups needing to generate boilerplate for new services","Teams standardizing API design across microservices","Data-intensive applications where schema design significantly impacts performance"],"limitations":["Generated schemas may not account for complex business logic or access patterns not specified","Performance optimization requires knowledge of actual query patterns and data volumes","API design decisions (REST vs GraphQL, versioning strategy) require explicit specification","Migration generation assumes no existing data — manual refinement needed for existing systems"],"requires":["OpenAI API key with GPT-5.3-Codex access","Data model specification or existing schema","API framework and database technology specification","Optional: performance requirements and query patterns"],"input_types":["natural language data model descriptions","existing database schemas or data structures","API requirements and endpoint specifications","performance requirements and access patterns"],"output_types":["database schema definitions (SQL, MongoDB, etc.)","ORM model definitions","API endpoint definitions (OpenAPI, GraphQL schema)","migration scripts and version management"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.3-codex__cap_9","uri":"capability://code.generation.editing.infrastructure.and.devops.code.generation","name":"infrastructure-and-devops-code-generation","description":"Generates infrastructure-as-code (Terraform, CloudFormation, Kubernetes manifests) and DevOps configurations from high-level specifications using reasoning about cloud architecture patterns, security best practices, and operational requirements. The model understands infrastructure trade-offs and generates configurations that balance cost, performance, and reliability.","intents":["Generate Terraform or CloudFormation templates for cloud infrastructure","Create Kubernetes manifests and Helm charts for containerized applications","Generate CI/CD pipeline configurations (GitHub Actions, GitLab CI, Jenkins)","Design infrastructure for scalability, high availability, and disaster recovery"],"best_for":["DevOps teams rapidly provisioning infrastructure for new services","Startups lacking dedicated infrastructure expertise seeking to standardize deployments","Teams migrating between cloud providers or on-premises to cloud","Organizations standardizing infrastructure patterns across teams"],"limitations":["Generated configurations may not account for organization-specific policies or constraints","Cost optimization requires knowledge of actual workload patterns and usage","Security configurations depend on threat model and compliance requirements not always evident","Requires manual review and testing before deployment to production"],"requires":["OpenAI API key with GPT-5.3-Codex access","Cloud provider and service specifications","Application requirements (compute, storage, networking)","Optional: existing infrastructure examples for pattern matching"],"input_types":["application requirements and specifications","cloud provider and service choices","scalability and availability requirements","security and compliance constraints"],"output_types":["infrastructure-as-code templates (Terraform, CloudFormation)","container orchestration manifests (Kubernetes, Docker Compose)","CI/CD pipeline configurations","monitoring and alerting configurations"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-5.3-Codex model access","HTTP client capable of handling streaming responses","Sufficient API quota for agentic multi-turn interactions","Code context provided as text (no automatic repository indexing)","OpenAI API key with GPT-5.3-Codex access","Code context provided as text or via IDE integration","Language identifier or file extension for context optimization","HTTP client supporting streaming for real-time completion","Source code to be optimized","Optional: profiling data or performance metrics"],"failure_modes":["Context window constraints limit the size of codebases that can be reasoned about in a single request","Reasoning overhead adds latency compared to direct code generation models — typical response time 5-15 seconds for complex tasks","No built-in version control integration — requires external tooling to track generated code changes","Training data cutoff means knowledge of very recent frameworks and libraries may be incomplete","Completion quality degrades for domain-specific or proprietary languages with limited training data","No real-time streaming completion in all client implementations — some integrations batch requests","Requires sufficient context (typically 50+ lines) for high-quality predictions; short snippets may produce generic completions","No built-in linting or syntax validation — generated completions may require post-processing","Optimization suggestions depend on actual workload patterns and data characteristics — generic suggestions may not apply","Premature optimization can reduce code readability — requires judgment about when optimization is justified","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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-gpt-5.3-codex","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-5.3-codex"}},"signature":"UKPq3A2HsZI9ZIEpj9c4+GPSnvlFQZVQzbfk7byeqeyG7pFxndtGJRdMeoLz+LT3QummD04nzvoVA7B8zyoVBg==","signedAt":"2026-06-19T19:59:48.408Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-5.3-codex","artifact":"https://unfragile.ai/openai-gpt-5.3-codex","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-5.3-codex","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"}}