{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-5.2-codex","slug":"openai-gpt-5.2-codex","name":"OpenAI: GPT-5.2-Codex","type":"model","url":"https://openrouter.ai/models/openai~gpt-5.2-codex","page_url":"https://unfragile.ai/openai-gpt-5.2-codex","categories":["code-editors"],"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-5.2-codex__cap_0","uri":"capability://code.generation.editing.multi.language.code.generation.with.context.aware.completion","name":"multi-language code generation with context-aware completion","description":"Generates syntactically correct, semantically meaningful code across 50+ programming languages by leveraging transformer-based token prediction trained on diverse codebases. The model uses attention mechanisms to understand surrounding code context, function signatures, and import statements to produce completions that respect language-specific idioms, type systems, and framework conventions. Supports both single-line completions and multi-function generation sequences.","intents":["I need to auto-complete a function body given its signature and docstring","Generate boilerplate code for a new class or module in my project","Fill in missing implementations across multiple files based on existing patterns","Complete code snippets in unfamiliar languages or frameworks"],"best_for":["full-stack developers building features across multiple languages","teams standardizing code patterns and reducing boilerplate writing time","developers learning new frameworks or languages through guided completion"],"limitations":["Context window limited to ~8K tokens; cannot reference entire large codebases in single request","May generate syntactically valid but logically incorrect code without semantic understanding of business logic","Performance degrades for domain-specific languages or proprietary frameworks with limited training data","No real-time IDE integration without third-party adapters; requires API calls with ~500ms latency per completion"],"requires":["OpenAI API key with GPT-5.2-Codex access","HTTP client library (curl, Python requests, Node.js fetch, etc.)","Network connectivity to OpenAI endpoints","Code context provided as text (no AST parsing required client-side)"],"input_types":["text (code snippet with cursor position)","structured prompt (language identifier, framework context, docstring)"],"output_types":["text (completed code)","structured JSON (completion with confidence scores, alternative suggestions)"],"categories":["code-generation-editing","language-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_1","uri":"capability://code.generation.editing.code.refactoring.and.structural.transformation","name":"code refactoring and structural transformation","description":"Analyzes existing code and applies transformations (renaming, extraction, inlining, pattern replacement) by understanding syntactic and semantic structure through language-specific parsing. The model generates refactoring instructions that preserve functionality while improving readability, performance, or adherence to design patterns. Supports both automated suggestions and interactive refinement loops where developers provide feedback on proposed changes.","intents":["Rename a function and update all call sites across a codebase","Extract repeated logic into a reusable utility function","Convert callback-based code to async/await or promise chains","Apply design pattern transformations (e.g., factory pattern, dependency injection)"],"best_for":["teams modernizing legacy codebases with large refactoring campaigns","developers improving code quality without manual AST manipulation","architects applying consistent patterns across distributed teams"],"limitations":["Cannot guarantee 100% correctness without test execution; may miss edge cases in conditional logic","Refactoring suggestions may not account for runtime behavior, dynamic typing, or reflection-based code","Requires explicit scope definition; cannot automatically determine impact radius across microservices or distributed systems","No built-in version control integration; changes must be manually reviewed and committed"],"requires":["OpenAI API key with GPT-5.2-Codex access","Source code provided as text or via file upload","Target language specification (Python, JavaScript, Java, etc.)","Optional: test suite or type definitions for validation"],"input_types":["text (source code)","structured prompt (refactoring intent, constraints, target pattern)"],"output_types":["text (refactored code)","structured JSON (diff representation, change summary, risk assessment)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_10","uri":"capability://safety.moderation.security.vulnerability.detection.and.remediation","name":"security vulnerability detection and remediation","description":"Scans code for security vulnerabilities (SQL injection, XSS, authentication bypass, cryptographic weaknesses, dependency vulnerabilities) using pattern matching and semantic analysis. The model identifies vulnerable code patterns, explains security implications, and generates secure implementations that follow OWASP guidelines. Supports both automated scanning and interactive security review where developers ask about specific security concerns.","intents":["Identify SQL injection vulnerabilities in database queries","Detect XSS vulnerabilities in user input handling","Find authentication or authorization bypass opportunities","Identify cryptographic weaknesses or insecure random number generation"],"best_for":["security-conscious teams integrating security scanning into development","compliance-driven organizations (PCI-DSS, HIPAA, SOC 2)","developers learning secure coding practices"],"limitations":["Cannot detect vulnerabilities requiring runtime context or specific deployment configuration","May produce false positives for intentional security patterns or domain-specific exceptions","Requires sufficient code context; may miss vulnerabilities in external dependencies or third-party code","Does not account for business logic vulnerabilities or authorization flaws"],"requires":["OpenAI API key with GPT-5.2-Codex access","Source code provided as text","Optional: dependency manifest (package.json, requirements.txt, pom.xml) for vulnerability scanning","Optional: security policy or compliance requirements"],"input_types":["text (source code)","structured prompt (security focus, compliance requirements)"],"output_types":["text (vulnerability report with explanations)","structured JSON (vulnerability list with severity, remediation suggestions)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_2","uri":"capability://code.generation.editing.code.review.and.quality.analysis.with.architectural.insights","name":"code review and quality analysis with architectural insights","description":"Evaluates code for bugs, performance issues, security vulnerabilities, and architectural anti-patterns by applying learned heuristics from security research, performance benchmarks, and design pattern literature. The model identifies problematic patterns (SQL injection vectors, memory leaks, race conditions, tight coupling) and suggests fixes with explanations of why the issue matters. Supports both automated scanning and interactive review sessions where developers ask clarifying questions.","intents":["Identify potential security vulnerabilities before code review","Detect performance bottlenecks and suggest optimization strategies","Flag architectural violations (e.g., circular dependencies, god objects)","Explain why a code pattern is problematic and suggest alternatives"],"best_for":["security-conscious teams integrating automated code scanning into CI/CD","performance-critical applications (databases, real-time systems)","teams without dedicated security engineers or architects"],"limitations":["Cannot detect vulnerabilities requiring runtime context (e.g., SQL injection only if query construction is visible in code)","May produce false positives for domain-specific patterns that violate general best practices intentionally","Performance analysis is static; cannot identify bottlenecks that only manifest under specific load conditions","Requires sufficient code context; may miss issues in tightly coupled external dependencies"],"requires":["OpenAI API key with GPT-5.2-Codex access","Source code provided as text","Language specification and optional framework context (Django, Spring, etc.)","Optional: test cases or performance benchmarks for validation"],"input_types":["text (source code)","structured prompt (review focus: security, performance, architecture, style)"],"output_types":["text (review comments with explanations)","structured JSON (issue list with severity, category, suggested fix, line numbers)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_3","uri":"capability://text.generation.language.technical.documentation.generation.from.code","name":"technical documentation generation from code","description":"Analyzes code structure and generates human-readable documentation (API docs, README sections, architecture diagrams in text form) by extracting intent from function signatures, type annotations, and code patterns. The model infers purpose, parameters, return values, and usage examples from implementation details and generates documentation in multiple formats (Markdown, Sphinx, JSDoc, OpenAPI). Supports both full-codebase documentation generation and targeted documentation for specific modules or functions.","intents":["Generate API documentation from function signatures and type hints","Create README sections explaining module architecture and usage","Write docstrings for undocumented legacy code","Generate OpenAPI/Swagger specs from REST endpoint implementations"],"best_for":["teams with underdocumented legacy systems needing rapid documentation","open-source projects seeking to improve contributor onboarding","API teams automating documentation updates alongside code changes"],"limitations":["Generated documentation may be inaccurate if code is poorly structured or uses non-obvious patterns","Cannot infer business context or user-facing intent from implementation alone","Requires well-named functions and variables; cryptic naming produces poor documentation","Does not automatically update documentation when code changes; requires re-generation and manual review"],"requires":["OpenAI API key with GPT-5.2-Codex access","Source code provided as text","Target documentation format specification (Markdown, RST, JSDoc, etc.)","Optional: existing documentation samples for style matching"],"input_types":["text (source code)","structured prompt (documentation scope, target audience, format)"],"output_types":["text (Markdown, RST, or other documentation format)","structured JSON (documentation metadata, cross-references)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_4","uri":"capability://code.generation.editing.test.case.generation.and.test.coverage.optimization","name":"test case generation and test coverage optimization","description":"Generates unit tests, integration tests, and edge-case test scenarios by analyzing function signatures, type systems, and code logic to identify input domains and expected behaviors. The model produces test code in framework-specific syntax (pytest, Jest, JUnit, etc.) with assertions that validate both happy paths and error conditions. Supports coverage analysis to identify untested code paths and suggests tests to improve coverage metrics.","intents":["Generate unit tests for a function given its signature and implementation","Create edge-case tests for boundary conditions and error handling","Identify untested code paths and suggest tests to improve coverage","Generate integration tests for multi-component workflows"],"best_for":["teams with low test coverage seeking rapid improvement","developers writing tests for legacy code without existing test suites","QA teams automating test generation for regression testing"],"limitations":["Generated tests may not cover business logic requirements; only structural coverage","Cannot generate meaningful tests without understanding expected behavior (requires docstrings or type hints)","May produce flaky tests if code has non-deterministic behavior or external dependencies","Requires test framework setup and dependency management; generated tests assume framework availability"],"requires":["OpenAI API key with GPT-5.2-Codex access","Source code provided as text","Test framework specification (pytest, Jest, JUnit, etc.)","Optional: existing test examples for style matching"],"input_types":["text (source code)","structured prompt (test scope, framework, coverage goals)"],"output_types":["text (test code in target framework syntax)","structured JSON (test metadata, coverage analysis)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_5","uri":"capability://code.generation.editing.interactive.debugging.assistance.with.hypothesis.generation","name":"interactive debugging assistance with hypothesis generation","description":"Helps developers diagnose bugs by analyzing error messages, stack traces, and code context to generate hypotheses about root causes and suggest debugging strategies. The model correlates error symptoms with common bug patterns (off-by-one errors, null pointer dereferences, type mismatches, race conditions) and recommends targeted debugging steps (breakpoint placement, logging additions, test cases). Supports iterative debugging where developers provide additional context and the model refines hypotheses.","intents":["Understand what a cryptic error message or stack trace means","Generate hypotheses about the root cause of a bug","Suggest debugging steps and breakpoint placement","Identify similar bugs in related code sections"],"best_for":["junior developers learning debugging techniques","teams debugging complex distributed systems or concurrent code","developers working in unfamiliar codebases or languages"],"limitations":["Cannot execute code or inspect runtime state; relies on static analysis and error messages","May generate incorrect hypotheses if error messages are misleading or incomplete","Debugging suggestions are generic; require domain knowledge to apply effectively","Cannot identify bugs that don't produce error messages (silent failures, logic errors)"],"requires":["OpenAI API key with GPT-5.2-Codex access","Error message or stack trace provided as text","Relevant source code context","Optional: reproduction steps or test case"],"input_types":["text (error message, stack trace, code snippet)","structured prompt (context about recent changes, environment)"],"output_types":["text (debugging hypothesis and suggested steps)","structured JSON (root cause analysis, suggested fixes)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_6","uri":"capability://code.generation.editing.code.to.code.translation.across.programming.languages","name":"code-to-code translation across programming languages","description":"Translates code from one programming language to another by understanding semantic intent and adapting to target language idioms, standard libraries, and type systems. The model preserves functionality while leveraging language-specific features (e.g., Python list comprehensions, Rust ownership, Go goroutines) to produce idiomatic target code. Supports both single-file translation and multi-file projects with dependency mapping.","intents":["Port a Python script to JavaScript for browser execution","Migrate a Java service to Go for improved performance","Convert legacy COBOL to modern Python","Translate algorithm implementations across languages for comparison"],"best_for":["teams migrating between technology stacks","polyglot organizations standardizing on new languages","developers learning new languages by translating familiar code"],"limitations":["Cannot translate code that relies on language-specific features without equivalent in target language","Performance characteristics may differ significantly (e.g., Python to C++ translation may require manual optimization)","Standard library differences may require manual adaptation of I/O, networking, or system calls","Type system differences (dynamic vs static) may require significant refactoring"],"requires":["OpenAI API key with GPT-5.2-Codex access","Source code provided as text","Source and target language specification","Optional: target language style guide or existing codebase samples"],"input_types":["text (source code)","structured prompt (source language, target language, constraints)"],"output_types":["text (translated code)","structured JSON (translation notes, manual intervention points)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_7","uri":"capability://planning.reasoning.architectural.pattern.recommendation.and.implementation","name":"architectural pattern recommendation and implementation","description":"Analyzes code structure and recommends architectural patterns (MVC, microservices, event-driven, CQRS) based on codebase size, complexity, and functional requirements. The model generates skeleton implementations of recommended patterns, refactors existing code to conform to patterns, and explains trade-offs between alternatives. Supports both greenfield architecture design and incremental refactoring of existing systems.","intents":["Recommend an architecture for a new project based on requirements","Identify architectural anti-patterns in existing code","Generate skeleton code for a recommended pattern","Explain trade-offs between architectural alternatives"],"best_for":["architects designing systems for scalability and maintainability","teams refactoring monoliths into microservices","startups seeking guidance on architecture decisions"],"limitations":["Recommendations are based on code structure, not business requirements or operational constraints","Cannot account for team expertise, organizational structure, or deployment infrastructure","Pattern implementation requires significant manual work; generated code is skeleton only","Trade-offs are generic; specific trade-offs depend on operational context (latency, throughput, cost)"],"requires":["OpenAI API key with GPT-5.2-Codex access","Source code provided as text or codebase structure description","Optional: requirements document or architecture decision records"],"input_types":["text (source code or architecture description)","structured prompt (requirements, constraints, team expertise)"],"output_types":["text (architecture recommendation with rationale)","structured JSON (pattern skeleton, refactoring roadmap)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_8","uri":"capability://code.generation.editing.natural.language.to.code.generation.with.intent.understanding","name":"natural language to code generation with intent understanding","description":"Converts natural language descriptions (requirements, specifications, comments) into executable code by understanding intent and inferring implementation details. The model parses requirements for functional and non-functional constraints, generates code that satisfies constraints, and produces comments explaining implementation choices. Supports iterative refinement where developers provide feedback and the model adjusts generated code.","intents":["Generate a function from a natural language description","Implement a feature from a requirements specification","Convert pseudocode or algorithm descriptions into working code","Generate boilerplate from a feature description"],"best_for":["non-technical stakeholders specifying features in natural language","developers rapidly prototyping features from specifications","teams using specification-driven development"],"limitations":["Ambiguous or incomplete requirements produce incorrect or incomplete code","Cannot infer non-functional requirements (performance, security, scalability) without explicit specification","Generated code may not follow team conventions or style guidelines","Requires multiple iterations to refine code quality; initial generation is often incomplete"],"requires":["OpenAI API key with GPT-5.2-Codex access","Natural language requirement or specification","Target language specification","Optional: existing codebase samples for style matching"],"input_types":["text (natural language description, requirements, pseudocode)"],"output_types":["text (generated code)","structured JSON (implementation notes, assumptions, clarification questions)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.2-codex__cap_9","uri":"capability://code.generation.editing.performance.optimization.analysis.and.code.generation","name":"performance optimization analysis and code generation","description":"Identifies performance bottlenecks in code by analyzing algorithmic complexity, memory usage patterns, and I/O operations, then generates optimized implementations. The model suggests specific optimizations (caching, indexing, parallelization, algorithm selection) with estimated performance improvements and trade-offs. Supports both algorithmic optimization and infrastructure-level suggestions (database indexing, query optimization).","intents":["Identify performance bottlenecks in slow code","Suggest algorithm optimizations with complexity analysis","Generate optimized implementations with caching or memoization","Recommend database query optimizations or indexing strategies"],"best_for":["performance-critical applications (real-time systems, high-throughput services)","teams optimizing existing code for scale","developers learning performance optimization techniques"],"limitations":["Static analysis cannot identify bottlenecks that only manifest under specific load conditions","Optimization suggestions are generic; actual improvements depend on data distribution and access patterns","May suggest premature optimization for non-critical code paths","Requires profiling data or load testing to validate optimization effectiveness"],"requires":["OpenAI API key with GPT-5.2-Codex access","Source code provided as text","Optional: profiling data or performance benchmarks","Optional: data characteristics (size, distribution, access patterns)"],"input_types":["text (source code)","structured prompt (performance goals, constraints, profiling data)"],"output_types":["text (optimization suggestions with rationale)","structured JSON (optimized code, complexity analysis, estimated improvements)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-5.2-Codex access","HTTP client library (curl, Python requests, Node.js fetch, etc.)","Network connectivity to OpenAI endpoints","Code context provided as text (no AST parsing required client-side)","Source code provided as text or via file upload","Target language specification (Python, JavaScript, Java, etc.)","Optional: test suite or type definitions for validation","Source code provided as text","Optional: dependency manifest (package.json, requirements.txt, pom.xml) for vulnerability scanning","Optional: security policy or compliance requirements"],"failure_modes":["Context window limited to ~8K tokens; cannot reference entire large codebases in single request","May generate syntactically valid but logically incorrect code without semantic understanding of business logic","Performance degrades for domain-specific languages or proprietary frameworks with limited training data","No real-time IDE integration without third-party adapters; requires API calls with ~500ms latency per completion","Cannot guarantee 100% correctness without test execution; may miss edge cases in conditional logic","Refactoring suggestions may not account for runtime behavior, dynamic typing, or reflection-based code","Requires explicit scope definition; cannot automatically determine impact radius across microservices or distributed systems","No built-in version control integration; changes must be manually reviewed and committed","Cannot detect vulnerabilities requiring runtime context or specific deployment configuration","May produce false positives for intentional security patterns or domain-specific exceptions","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-5.2-codex","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-5.2-codex"}},"signature":"QVtVJ5KAbXCpR4/dzFQf4okNQrJThP6rO277pap4q5iwejiffhdr6+ZU9IK6CIDvPnjNtKU3XPUDfQd6pMaMCw==","signedAt":"2026-06-21T06:25:22.524Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-5.2-codex","artifact":"https://unfragile.ai/openai-gpt-5.2-codex","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-5.2-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"}}