{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-5-codex","slug":"openai-gpt-5-codex","name":"OpenAI: GPT-5 Codex","type":"model","url":"https://openrouter.ai/models/openai~gpt-5-codex","page_url":"https://unfragile.ai/openai-gpt-5-codex","categories":["code-editors"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.25e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-5-codex__cap_0","uri":"capability://code.generation.editing.codebase.aware.code.generation.with.extended.context.windows","name":"codebase-aware code generation with extended context windows","description":"Generates production-ready code by leveraging GPT-5's extended context window to ingest entire codebases, project structures, and multi-file dependencies. Uses transformer-based semantic understanding to maintain consistency across generated code segments while respecting existing architectural patterns, naming conventions, and module boundaries without requiring explicit prompt engineering for each file.","intents":["Generate new features that integrate seamlessly with existing codebase architecture","Scaffold boilerplate code that matches project conventions and style","Implement complex multi-file refactorings while maintaining cross-module consistency","Generate code that respects existing dependency graphs and import structures"],"best_for":["Full-stack engineering teams building large monorepos or microservices","Solo developers maintaining legacy codebases requiring context-aware generation","Teams migrating between frameworks or languages with complex interdependencies"],"limitations":["Context window, while extended, has finite limits — very large monorepos (>10M tokens) may require chunking strategies","Generated code quality degrades if codebase lacks consistent patterns or has highly fragmented architecture","No built-in verification that generated code actually compiles or passes existing test suites","Requires explicit codebase indexing/preparation — cannot auto-discover all relevant context from unstructured file systems"],"requires":["OpenAI API key with GPT-5-Codex model access","Codebase files provided as context (via prompt, file uploads, or API payload)","Supported language syntax understanding (Python, JavaScript, TypeScript, Go, Rust, Java, C++, etc.)"],"input_types":["source code (multiple files)","natural language specifications","code snippets with context","architectural diagrams or documentation as text"],"output_types":["source code (single or multiple files)","code diffs","structured code with inline documentation"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_1","uri":"capability://code.generation.editing.interactive.code.debugging.with.execution.trace.analysis","name":"interactive code debugging with execution trace analysis","description":"Analyzes runtime errors, stack traces, and execution logs by parsing structured error outputs and correlating them with source code context. Uses chain-of-thought reasoning to hypothesize root causes, suggest fixes, and generate test cases that isolate the bug — all without requiring manual code instrumentation or debugger attachment.","intents":["Diagnose runtime exceptions and suggest targeted fixes with code examples","Understand why tests are failing by correlating test output with source implementation","Trace execution flow through complex async/concurrent code to identify race conditions","Generate minimal reproducible examples (MREs) that isolate bugs for faster debugging"],"best_for":["Backend engineers debugging production issues in distributed systems","QA engineers analyzing test failures across large test suites","Junior developers learning to debug complex codebases","Teams using CI/CD pipelines that capture detailed execution logs"],"limitations":["Requires structured error output (stack traces, logs) — unstructured error messages reduce accuracy","Cannot execute code directly or attach to live debuggers — analysis is static/post-hoc only","May miss subtle bugs that require runtime state inspection (memory corruption, timing-dependent issues)","Suggestions are probabilistic — generated fixes may not address root cause without human verification"],"requires":["OpenAI API key with GPT-5-Codex access","Error logs, stack traces, or test output in text format","Source code context (relevant files or functions)"],"input_types":["stack traces","error logs","test output","source code snippets","execution traces"],"output_types":["debugging suggestions with code examples","root cause analysis","test cases to reproduce bugs","code patches"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_10","uri":"capability://code.generation.editing.sql.query.optimization.and.generation.with.execution.plan.analysis","name":"sql query optimization and generation with execution plan analysis","description":"Generates optimized SQL queries from natural language descriptions or existing queries, and analyzes execution plans to identify performance bottlenecks. Uses database schema understanding and query optimization patterns to suggest index creation, query rewrites, and join strategies — supporting multiple database systems (PostgreSQL, MySQL, SQL Server, etc.).","intents":["Generate SQL queries from natural language descriptions of data requirements","Optimize slow queries by analyzing execution plans and suggesting rewrites","Suggest index creation strategies to improve query performance","Migrate SQL queries between different database systems"],"best_for":["Data engineers optimizing slow database queries","Backend developers generating complex queries without SQL expertise","Database administrators identifying performance bottlenecks","Teams migrating between database systems"],"limitations":["Query optimization depends on actual data distribution and statistics — suggestions may not apply to all datasets","Cannot analyze execution plans without database access — recommendations are based on query structure alone","Generated queries may not reflect business logic or data integrity constraints","Index suggestions may conflict with write performance or storage constraints"],"requires":["OpenAI API key with GPT-5-Codex access","Database schema information (table definitions, relationships)","Target database system specified (PostgreSQL, MySQL, SQL Server, etc.)","Optional: execution plans or performance metrics"],"input_types":["natural language data requirements","existing SQL queries","database schemas","execution plans"],"output_types":["optimized SQL queries","index creation recommendations","query rewrite suggestions","execution plan analysis"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_11","uri":"capability://safety.moderation.dependency.analysis.and.vulnerability.scanning.with.remediation","name":"dependency analysis and vulnerability scanning with remediation","description":"Scans code dependencies for known vulnerabilities using vulnerability databases, and generates remediation code (version updates, API migrations, security patches). Uses semantic analysis to understand how vulnerable dependencies are used in code and generates targeted fixes that maintain compatibility while addressing security issues.","intents":["Identify vulnerable dependencies in package.json, requirements.txt, or other dependency files","Generate version update recommendations with compatibility analysis","Create migration code for deprecated APIs in vulnerable dependencies","Automate security patch application across large codebases"],"best_for":["Security teams automating vulnerability remediation","DevOps engineers managing dependency updates at scale","Open-source maintainers addressing security issues in dependencies","Teams enforcing security policies in CI/CD pipelines"],"limitations":["Vulnerability detection depends on database accuracy — zero-day vulnerabilities may be missed","Remediation code generation may break compatibility — requires testing before deployment","Cannot assess impact of vulnerabilities without understanding application context","Transitive dependency vulnerabilities may require complex resolution strategies"],"requires":["OpenAI API key with GPT-5-Codex access","Dependency files (package.json, requirements.txt, Gemfile, etc.)","Source code using vulnerable dependencies","Access to vulnerability databases (NVD, GitHub Security Advisory, etc.)"],"input_types":["dependency files","source code","vulnerability reports"],"output_types":["vulnerability reports with severity levels","version update recommendations","migration code for API changes","patch files"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_2","uri":"capability://code.generation.editing.natural.language.to.code.translation.with.type.safety.inference","name":"natural language to code translation with type safety inference","description":"Converts natural language specifications into type-safe, production-ready code by inferring data structures, function signatures, and error handling patterns from context. Uses semantic parsing to extract intent from ambiguous requirements and generates code with explicit type annotations, validation, and error boundaries appropriate to the target language's type system.","intents":["Translate product requirements or user stories directly into working code","Generate strongly-typed APIs from natural language specifications","Create data models and schemas from textual descriptions of domain entities","Implement business logic from written specifications with proper error handling"],"best_for":["Product teams translating requirements into code without intermediate design documents","Startups prototyping features rapidly from written specifications","Teams building domain-specific languages or internal tools from natural language specs","Non-technical stakeholders who can describe features but cannot write code"],"limitations":["Ambiguous or incomplete specifications produce lower-quality code — requires iterative refinement","Cannot infer non-functional requirements (performance, scalability, security) from text alone","Generated code may not match existing architectural patterns if specifications don't reference them","Type inference is probabilistic — generated types may not align with actual runtime data shapes"],"requires":["OpenAI API key with GPT-5-Codex access","Natural language specification (written description of desired behavior)","Target programming language specified (Python, TypeScript, Go, etc.)","Optional: existing codebase context for consistency"],"input_types":["natural language specifications","user stories","requirements documents","API descriptions","data model descriptions"],"output_types":["source code with type annotations","API schemas (OpenAPI, GraphQL, etc.)","data models and migrations","test cases"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_3","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 intent and idiomatic patterns specific to each target language. Uses language-specific AST understanding and idiom libraries to generate code that follows target language conventions (e.g., Pythonic patterns for Python, Rust ownership semantics for Rust) rather than mechanical line-by-line translation.","intents":["Migrate codebases from one language to another while maintaining readability and performance","Port algorithms between languages while respecting each language's idioms and best practices","Generate language-specific implementations from a reference implementation in another language","Create polyglot systems where the same logic is implemented in multiple languages"],"best_for":["Teams migrating from legacy languages (Java, C++) to modern languages (Rust, Go, TypeScript)","Polyglot organizations maintaining the same logic across multiple language ecosystems","Library maintainers providing language bindings or multi-language implementations","Performance-critical teams porting algorithms to systems languages while preserving correctness"],"limitations":["Idiomatic translation requires deep knowledge of both source and target languages — quality varies by language pair","Some language features have no direct equivalent (e.g., Rust ownership to Python, Go goroutines to JavaScript) — translation may require architectural changes","Performance characteristics may differ significantly between translations — generated code may not match source performance","Library ecosystems differ — direct API translations may not exist, requiring manual adaptation"],"requires":["OpenAI API key with GPT-5-Codex access","Source code in supported language","Target language specified","Optional: performance or idiom preferences for target language"],"input_types":["source code (any supported language)","code snippets","algorithms with explanatory comments"],"output_types":["translated source code","idiom-specific implementations","migration guides with manual steps"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_4","uri":"capability://code.generation.editing.code.review.and.architectural.analysis.with.pattern.detection","name":"code review and architectural analysis with pattern detection","description":"Analyzes code for architectural issues, design pattern violations, performance anti-patterns, and security vulnerabilities by applying semantic code analysis and pattern matching against known best practices. Generates detailed review comments with specific line references, severity levels, and actionable remediation suggestions backed by architectural reasoning.","intents":["Identify architectural violations and design pattern mismatches in pull requests","Detect performance anti-patterns (N+1 queries, unnecessary allocations, etc.) with specific optimization suggestions","Flag security vulnerabilities (injection, authentication flaws, etc.) with remediation code","Enforce coding standards and architectural consistency across teams"],"best_for":["Engineering teams enforcing architectural standards across large codebases","Security-focused teams automating vulnerability detection in code reviews","Performance-critical teams identifying optimization opportunities at code review time","Distributed teams standardizing code quality without heavyweight code review processes"],"limitations":["Pattern detection is probabilistic — may produce false positives for unconventional but valid patterns","Cannot analyze runtime behavior or performance characteristics without execution context","Requires sufficient code context to understand architectural intent — isolated code snippets produce less accurate analysis","Security analysis is pattern-based — novel or sophisticated vulnerabilities may be missed"],"requires":["OpenAI API key with GPT-5-Codex access","Source code to review (single file or multiple files for context)","Optional: architectural guidelines or coding standards as reference"],"input_types":["source code","pull request diffs","code snippets with context","architectural documentation"],"output_types":["structured code review comments","severity-ranked issue lists","remediation code suggestions","architectural analysis reports"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_5","uri":"capability://code.generation.editing.test.case.generation.with.coverage.driven.synthesis","name":"test case generation with coverage-driven synthesis","description":"Generates comprehensive test suites by analyzing source code to identify code paths, edge cases, and boundary conditions. Uses symbolic execution concepts and coverage metrics to synthesize test cases that exercise uncovered branches, error paths, and integration points — producing both unit tests and integration tests with assertions and setup/teardown logic.","intents":["Generate unit tests for existing functions to improve code coverage","Create edge case and boundary condition tests automatically","Synthesize integration tests that exercise multi-component interactions","Generate tests for error paths and exception handling"],"best_for":["Teams improving test coverage on legacy codebases without existing tests","QA engineers automating test generation for rapid iteration cycles","Developers writing tests for complex functions with many edge cases","Teams enforcing minimum coverage thresholds in CI/CD pipelines"],"limitations":["Generated tests may not reflect actual business requirements — require human review and adjustment","Cannot generate meaningful assertions without understanding expected behavior — may produce tautological tests","Coverage-driven generation may miss semantic bugs that don't manifest as uncovered code paths","Test data generation is probabilistic — may not produce realistic or representative test inputs"],"requires":["OpenAI API key with GPT-5-Codex access","Source code to test","Test framework specified (Jest, pytest, JUnit, etc.)","Optional: existing tests as examples for style/pattern matching"],"input_types":["source code (functions, classes, modules)","function signatures with type annotations","existing test examples"],"output_types":["unit test code","integration test code","test data fixtures","coverage reports"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_6","uri":"capability://text.generation.language.documentation.generation.from.code.with.semantic.extraction","name":"documentation generation from code with semantic extraction","description":"Generates comprehensive documentation (API docs, architecture guides, usage examples) by extracting semantic intent from code structure, function signatures, type annotations, and existing comments. Uses code analysis to infer parameter meanings, return types, error conditions, and usage patterns — producing documentation that stays synchronized with code changes.","intents":["Generate API documentation from function signatures and type annotations","Create architecture guides from codebase structure and module relationships","Produce usage examples and tutorials from existing code patterns","Generate changelog entries from code diffs and commit messages"],"best_for":["Open-source projects maintaining documentation alongside rapidly evolving code","API teams generating client documentation from server implementations","Internal tool teams documenting complex systems for new team members","Teams automating documentation updates in CI/CD pipelines"],"limitations":["Generated documentation quality depends on code clarity — poorly structured code produces poor documentation","Cannot infer business context or user-facing intent from code alone — requires domain knowledge","Generated examples may not reflect real-world usage patterns or common pitfalls","Documentation may become outdated if code changes are not reflected in regeneration"],"requires":["OpenAI API key with GPT-5-Codex access","Source code with type annotations and comments","Documentation format specified (Markdown, HTML, Sphinx, etc.)"],"input_types":["source code","type annotations","existing comments and docstrings","commit messages"],"output_types":["API documentation (Markdown, HTML)","architecture guides","usage examples","changelog entries"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_7","uri":"capability://code.generation.editing.performance.optimization.with.bottleneck.identification","name":"performance optimization with bottleneck identification","description":"Identifies performance bottlenecks in code by analyzing algorithmic complexity, memory usage patterns, and I/O operations. Generates optimized code variants with explanations of complexity improvements (e.g., O(n²) to O(n log n)), caching strategies, parallelization opportunities, and data structure recommendations — all with before/after performance comparisons.","intents":["Optimize slow algorithms by suggesting better data structures or algorithms","Identify and eliminate N+1 query patterns in database access code","Suggest caching strategies and memoization opportunities","Parallelize sequential code where possible with thread-safe implementations"],"best_for":["Performance engineers optimizing hot paths in production systems","Backend teams reducing database query counts in API endpoints","Data processing teams improving throughput of batch jobs","Teams profiling code and seeking algorithmic improvements"],"limitations":["Optimization suggestions are based on code analysis — actual performance gains depend on runtime characteristics and data distribution","Cannot optimize without understanding data volumes and access patterns — recommendations may not apply to all use cases","Parallelization suggestions may introduce concurrency bugs if not carefully implemented","Memory optimization may trade off readability or maintainability — requires human judgment"],"requires":["OpenAI API key with GPT-5-Codex access","Source code to optimize","Optional: performance profiling data or execution traces","Optional: data volume and access pattern information"],"input_types":["source code","performance profiles","execution traces","data volume specifications"],"output_types":["optimized code variants","complexity analysis (Big O notation)","performance improvement estimates","implementation guides for optimizations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_8","uri":"capability://code.generation.editing.api.schema.generation.and.validation.with.multi.format.support","name":"api schema generation and validation with multi-format support","description":"Generates API schemas (OpenAPI, GraphQL, Protocol Buffers) from code or natural language specifications, and validates API implementations against schemas. Uses type inference and semantic analysis to extract API contracts from function signatures, request/response types, and error definitions — supporting multiple schema formats and generating client/server code from schemas.","intents":["Generate OpenAPI schemas from REST API implementations","Create GraphQL schemas from data models and resolver functions","Generate Protocol Buffer definitions from typed code structures","Validate API implementations against schemas and flag contract violations"],"best_for":["API teams documenting and versioning API contracts","Microservices teams generating client SDKs from server implementations","Teams enforcing API contract consistency across multiple implementations","API-first development teams generating server stubs from schemas"],"limitations":["Schema generation is only as accurate as type annotations in source code — untyped code produces incomplete schemas","Cannot infer non-functional requirements (rate limits, authentication, caching) from code alone","Schema validation is structural — cannot validate business logic or semantic constraints","Multi-format support may produce inconsistent schemas across formats"],"requires":["OpenAI API key with GPT-5-Codex access","Source code with type annotations or natural language API specification","Target schema format specified (OpenAPI, GraphQL, Protobuf, etc.)"],"input_types":["source code (REST endpoints, GraphQL resolvers, etc.)","type definitions","natural language API specifications","existing schemas"],"output_types":["OpenAPI/Swagger schemas","GraphQL schemas","Protocol Buffer definitions","client/server code stubs","validation reports"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-codex__cap_9","uri":"capability://code.generation.editing.infrastructure.as.code.generation.with.cloud.provider.support","name":"infrastructure-as-code generation with cloud provider support","description":"Generates infrastructure definitions (Terraform, CloudFormation, Kubernetes manifests) from natural language specifications or existing infrastructure. Uses cloud provider knowledge to generate production-ready configurations with security best practices, auto-scaling policies, monitoring, and disaster recovery patterns — supporting AWS, GCP, Azure, and Kubernetes.","intents":["Generate Terraform configurations from infrastructure requirements","Create Kubernetes manifests for containerized applications","Generate CloudFormation templates for AWS infrastructure","Migrate infrastructure definitions between cloud providers"],"best_for":["DevOps teams automating infrastructure provisioning","Startups rapidly deploying infrastructure without dedicated DevOps engineers","Teams migrating between cloud providers","Organizations standardizing infrastructure patterns across teams"],"limitations":["Generated configurations require validation and testing before production deployment","Cannot infer non-functional requirements (cost optimization, compliance, disaster recovery) without explicit specification","Cloud provider APIs and best practices change frequently — generated code may become outdated","Security configurations require human review — generated defaults may not meet compliance requirements"],"requires":["OpenAI API key with GPT-5-Codex access","Infrastructure requirements specification (natural language or existing configs)","Target cloud provider and IaC tool specified (Terraform, CloudFormation, Kubernetes, etc.)"],"input_types":["natural language infrastructure requirements","existing infrastructure configurations","architecture diagrams","application specifications"],"output_types":["Terraform configurations","CloudFormation templates","Kubernetes manifests","Helm charts","deployment guides"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-5-Codex model access","Codebase files provided as context (via prompt, file uploads, or API payload)","Supported language syntax understanding (Python, JavaScript, TypeScript, Go, Rust, Java, C++, etc.)","OpenAI API key with GPT-5-Codex access","Error logs, stack traces, or test output in text format","Source code context (relevant files or functions)","Database schema information (table definitions, relationships)","Target database system specified (PostgreSQL, MySQL, SQL Server, etc.)","Optional: execution plans or performance metrics","Dependency files (package.json, requirements.txt, Gemfile, etc.)"],"failure_modes":["Context window, while extended, has finite limits — very large monorepos (>10M tokens) may require chunking strategies","Generated code quality degrades if codebase lacks consistent patterns or has highly fragmented architecture","No built-in verification that generated code actually compiles or passes existing test suites","Requires explicit codebase indexing/preparation — cannot auto-discover all relevant context from unstructured file systems","Requires structured error output (stack traces, logs) — unstructured error messages reduce accuracy","Cannot execute code directly or attach to live debuggers — analysis is static/post-hoc only","May miss subtle bugs that require runtime state inspection (memory corruption, timing-dependent issues)","Suggestions are probabilistic — generated fixes may not address root cause without human verification","Query optimization depends on actual data distribution and statistics — suggestions may not apply to all datasets","Cannot analyze execution plans without database access — recommendations are based on query structure alone","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"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-codex","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-5-codex"}},"signature":"sB3tsNQL6/cwRuHIrW2PITBqOj4B8yfmziGAUcwOAPcVsltzudqA60LB2rdb7WdJ0A0XHiHKYMKk++Y83uANCw==","signedAt":"2026-06-22T00:13:09.484Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-5-codex","artifact":"https://unfragile.ai/openai-gpt-5-codex","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-5-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"}}