{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-5.1-codex-mini","slug":"openai-gpt-5.1-codex-mini","name":"OpenAI: GPT-5.1-Codex-Mini","type":"model","url":"https://openrouter.ai/models/openai~gpt-5.1-codex-mini","page_url":"https://unfragile.ai/openai-gpt-5.1-codex-mini","categories":["model-training"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-5.1-codex-mini__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 code across 40+ programming languages by leveraging transformer-based sequence-to-sequence architecture trained on diverse codebases. The model uses byte-pair encoding tokenization optimized for code syntax, enabling it to understand language-specific patterns, indentation rules, and API conventions. Completion is context-aware, incorporating surrounding code structure and docstrings to produce semantically coherent suggestions.","intents":["Generate boilerplate code for common patterns (API handlers, database queries, utility functions)","Complete partial function implementations based on function signatures and docstrings","Translate code snippets between programming languages","Auto-fill method chains and library-specific idioms"],"best_for":["Solo developers building prototypes across multiple tech stacks","Teams needing rapid scaffolding for microservices or API endpoints","Developers learning new languages who need syntax-aware suggestions"],"limitations":["Context window limited to ~4,000 tokens; cannot handle entire large files or complex multi-file dependencies","May generate syntactically valid but semantically incorrect code without explicit type hints or docstrings","Performance degrades for niche or domain-specific languages with limited training data representation","No real-time linting or compilation feedback; generated code requires manual testing"],"requires":["OpenAI API key or OpenRouter proxy access","HTTP/REST client capability for API calls","Network connectivity to OpenAI or OpenRouter endpoints"],"input_types":["text (code snippets, function signatures, docstrings)","code (partial implementations, comments describing intent)"],"output_types":["code (generated functions, classes, modules)","text (explanatory comments, docstrings)"],"categories":["code-generation-editing","language-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_1","uri":"capability://text.generation.language.code.explanation.and.documentation.generation","name":"code explanation and documentation generation","description":"Analyzes provided code snippets and generates human-readable explanations, docstrings, and technical documentation by decomposing code into logical blocks and mapping them to natural language descriptions. The model uses attention mechanisms to identify variable dependencies, control flow patterns, and function purposes, then synthesizes explanations at multiple abstraction levels (line-by-line, function-level, module-level).","intents":["Generate docstrings and type hints for undocumented legacy code","Create README sections explaining complex algorithms or data structures","Produce inline comments explaining non-obvious implementation choices","Generate API documentation from function signatures and usage examples"],"best_for":["Teams maintaining legacy codebases with poor documentation","Open-source maintainers needing to scale documentation efforts","Technical writers creating developer guides from code samples"],"limitations":["Explanations may oversimplify complex business logic or domain-specific algorithms","Cannot infer intent from poorly structured or obfuscated code","Generated documentation requires human review for accuracy and tone consistency","Context window limits prevent explaining entire large modules in a single request"],"requires":["OpenAI API key or OpenRouter proxy access","Code samples in text format (not binary or compiled code)"],"input_types":["code (functions, classes, modules, scripts)","text (code snippets with context)"],"output_types":["text (docstrings, comments, explanations)","structured data (markdown documentation, JSON metadata)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_10","uri":"capability://text.generation.language.code.to.documentation.and.api.documentation.generation","name":"code-to-documentation and api documentation generation","description":"Generates comprehensive API documentation, README files, and technical guides from source code by extracting function signatures, docstrings, type hints, and usage examples. The model produces formatted documentation in Markdown, HTML, or reStructuredText with proper structure, cross-references, and example code snippets. Supports generation of API reference docs, getting-started guides, and architecture documentation.","intents":["Generate API reference documentation from code and docstrings","Create README files with installation, usage, and example sections","Write architecture documentation explaining system design and components","Generate migration guides and changelog documentation"],"best_for":["Open-source maintainers scaling documentation efforts","Technical writers creating documentation from code samples","Teams automating documentation generation in CI/CD pipelines"],"limitations":["Generated documentation requires human review for accuracy and tone consistency","Cannot infer business context or user-facing feature descriptions from code alone","May produce verbose or redundant documentation without proper curation","Requires well-structured code and clear docstrings for quality output"],"requires":["OpenAI API key or OpenRouter proxy access","Source code with docstrings and type hints"],"input_types":["code (functions, classes, modules with documentation)"],"output_types":["text (Markdown, HTML, or reStructuredText documentation)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_2","uri":"capability://code.generation.editing.code.debugging.and.error.diagnosis","name":"code debugging and error diagnosis","description":"Identifies bugs, runtime errors, and logic flaws in provided code by performing static analysis through the transformer's learned understanding of common error patterns, type mismatches, and control flow issues. The model generates diagnostic explanations and suggests fixes by reasoning about variable scope, function contracts, and expected behavior based on context and naming conventions.","intents":["Diagnose runtime errors from stack traces and error messages","Identify potential null pointer exceptions, off-by-one errors, and type mismatches","Suggest fixes for logic errors in algorithms or data processing pipelines","Explain why code fails and recommend refactoring approaches"],"best_for":["Developers debugging unfamiliar codebases or languages","Teams performing code reviews to catch subtle logic errors","Junior developers learning debugging patterns and best practices"],"limitations":["Cannot execute code or verify fixes; suggestions are heuristic-based and may be incorrect","Struggles with domain-specific bugs requiring deep knowledge of external libraries or frameworks","May miss bugs in code with unclear variable names or poor structure","Cannot debug issues requiring runtime state inspection or profiling data"],"requires":["OpenAI API key or OpenRouter proxy access","Code samples and error messages in text format"],"input_types":["code (buggy implementations)","text (error messages, stack traces, test case descriptions)"],"output_types":["text (diagnostic explanations, fix suggestions)","code (corrected implementations)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_3","uri":"capability://code.generation.editing.code.refactoring.and.optimization.suggestions","name":"code refactoring and optimization suggestions","description":"Analyzes code structure and suggests refactoring improvements by identifying code smells, inefficient patterns, and opportunities for simplification. The model uses learned knowledge of design patterns, performance optimization techniques, and language idioms to recommend changes that improve readability, maintainability, and performance. Suggestions include extracting functions, consolidating duplicated logic, and applying language-specific optimizations.","intents":["Suggest refactoring to reduce cyclomatic complexity and improve testability","Identify performance bottlenecks and recommend algorithmic improvements","Recommend design pattern applications (Factory, Strategy, Observer, etc.)","Suggest modernization of legacy code to current language standards"],"best_for":["Teams conducting code quality initiatives and technical debt reduction","Developers learning design patterns and best practices","Architects planning large-scale refactoring efforts"],"limitations":["Suggestions may not account for business constraints, legacy system dependencies, or performance trade-offs","Cannot validate that refactored code maintains backward compatibility","May suggest over-engineering for simple use cases","Requires human judgment to prioritize refactoring suggestions by impact and effort"],"requires":["OpenAI API key or OpenRouter proxy access","Code samples in text format with sufficient context"],"input_types":["code (implementations to refactor)","text (performance metrics, constraints, goals)"],"output_types":["code (refactored implementations)","text (explanation of changes and rationale)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_4","uri":"capability://code.generation.editing.natural.language.to.code.translation","name":"natural language to code translation","description":"Converts natural language descriptions, pseudocode, or specifications into executable code by parsing intent from prose descriptions and mapping them to language-specific implementations. The model uses instruction-following capabilities to interpret ambiguous requirements, infer data structures, and generate idiomatic code that follows the target language's conventions and best practices.","intents":["Generate code from algorithm descriptions or mathematical specifications","Convert pseudocode or flowcharts into working implementations","Implement features described in user stories or requirements documents","Generate test cases from natural language test specifications"],"best_for":["Non-technical stakeholders or product managers describing features to developers","Rapid prototyping where speed matters more than optimization","Teams using specification-driven development approaches"],"limitations":["Ambiguous or incomplete specifications may result in incorrect implementations","Generated code may not match existing codebase style or architecture","Cannot handle complex domain-specific requirements without detailed context","Requires iterative refinement; rarely produces production-ready code on first attempt"],"requires":["OpenAI API key or OpenRouter proxy access","Clear, detailed natural language descriptions"],"input_types":["text (natural language descriptions, pseudocode, specifications)"],"output_types":["code (executable implementations in target language)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_5","uri":"capability://code.generation.editing.cross.language.code.translation","name":"cross-language code translation","description":"Translates code from one programming language to another by understanding semantic intent and mapping language-specific constructs to equivalent idioms in the target language. The model preserves logic and functionality while adapting to target language conventions, libraries, and performance characteristics. Translation handles differences in type systems, memory management, concurrency models, and standard library APIs.","intents":["Migrate codebases from one language to another (Python to Go, Java to Kotlin, etc.)","Port algorithms across languages while maintaining correctness","Generate language-specific implementations from a reference implementation","Create polyglot microservices by translating core logic to multiple languages"],"best_for":["Teams migrating between technology stacks","Organizations building polyglot systems with shared business logic","Developers learning new languages by translating familiar code"],"limitations":["May not preserve performance characteristics across languages (e.g., Python to C++ translation may not achieve expected speedups)","Cannot translate language-specific features that don't have equivalents (e.g., Python decorators to Java)","Requires manual testing and validation; semantic equivalence is not guaranteed","Context window limits prevent translating large files; requires splitting into smaller units"],"requires":["OpenAI API key or OpenRouter proxy access","Source code in text format","Specification of target programming language"],"input_types":["code (source code in any supported language)"],"output_types":["code (translated code in target language)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_6","uri":"capability://code.generation.editing.test.case.generation.and.test.code.writing","name":"test case generation and test code writing","description":"Generates comprehensive test cases and test code by analyzing function signatures, docstrings, and implementation logic to identify edge cases, boundary conditions, and expected behaviors. The model produces unit tests, integration tests, and property-based tests in the target testing framework, with assertions that validate both happy paths and error conditions.","intents":["Generate unit tests for existing functions with edge case coverage","Create test fixtures and mock objects for integration testing","Write parameterized tests for functions with multiple input combinations","Generate property-based tests for algorithmic correctness validation"],"best_for":["Teams improving test coverage on legacy codebases","Developers practicing test-driven development","QA engineers automating test case generation from specifications"],"limitations":["Generated tests may miss domain-specific edge cases or business logic constraints","Cannot generate tests for non-deterministic or time-dependent code without additional context","Test quality depends on clarity of function documentation and type hints","May generate redundant or overlapping test cases"],"requires":["OpenAI API key or OpenRouter proxy access","Well-documented function signatures and implementations"],"input_types":["code (functions or classes to test)","text (test specifications, edge case descriptions)"],"output_types":["code (test code in target testing framework)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_7","uri":"capability://code.generation.editing.sql.query.generation.and.optimization","name":"sql query generation and optimization","description":"Generates SQL queries from natural language descriptions and database schemas by understanding table relationships, column types, and query semantics. The model produces optimized queries with appropriate JOINs, aggregations, and indexes, and can suggest query rewrites to improve performance. Supports multiple SQL dialects (PostgreSQL, MySQL, T-SQL, etc.) with dialect-specific optimizations.","intents":["Generate SELECT queries from natural language descriptions of data requirements","Write complex JOINs and aggregations for reporting and analytics","Optimize slow queries by suggesting index strategies and query rewrites","Generate INSERT, UPDATE, DELETE statements for data manipulation"],"best_for":["Data analysts and business intelligence teams writing ad-hoc queries","Developers building database-driven applications without SQL expertise","Database administrators optimizing slow queries"],"limitations":["Cannot validate queries against actual database schema without connection","May generate syntactically correct but semantically incorrect queries without full schema context","Performance optimization suggestions are heuristic-based; actual performance depends on data distribution and indexes","Cannot handle complex business logic or domain-specific query patterns without examples"],"requires":["OpenAI API key or OpenRouter proxy access","Database schema description (table names, column names, types, relationships)"],"input_types":["text (natural language query descriptions, schema definitions)","code (SQL snippets for optimization)"],"output_types":["code (SQL queries in target dialect)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_8","uri":"capability://code.generation.editing.api.endpoint.and.rest.service.generation","name":"api endpoint and rest service generation","description":"Generates complete REST API implementations from specifications by creating route handlers, request/response validation, error handling, and documentation. The model produces framework-specific code (Express.js, FastAPI, Spring Boot, etc.) with proper HTTP semantics, status codes, and middleware integration. Includes OpenAPI/Swagger documentation generation.","intents":["Generate REST API endpoints from OpenAPI specifications","Create CRUD operations for database-backed resources","Implement request validation and error handling middleware","Generate API documentation and client SDKs"],"best_for":["Backend developers rapidly prototyping APIs","Teams building microservices with consistent patterns","API-first development teams generating implementations from specifications"],"limitations":["Generated code may lack production-grade error handling and logging","Cannot handle complex business logic or domain-specific validation without detailed specifications","Security considerations (authentication, authorization) require manual review and implementation","Performance and scalability optimizations are not automatic"],"requires":["OpenAI API key or OpenRouter proxy access","API specification (OpenAPI, Swagger, or natural language description)"],"input_types":["text (API specifications, endpoint descriptions)","structured data (OpenAPI/Swagger definitions)"],"output_types":["code (framework-specific API implementations)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-mini__cap_9","uri":"capability://code.generation.editing.infrastructure.as.code.generation","name":"infrastructure-as-code generation","description":"Generates infrastructure definitions in Terraform, CloudFormation, Kubernetes manifests, and Docker configurations from natural language descriptions or architecture diagrams. The model understands cloud provider APIs, resource dependencies, and best practices to produce production-ready infrastructure code with proper networking, security, and scalability configurations.","intents":["Generate Terraform modules for cloud infrastructure provisioning","Create Kubernetes manifests for containerized application deployment","Write Docker Compose configurations for local development environments","Generate CloudFormation templates for AWS infrastructure"],"best_for":["DevOps engineers and platform teams automating infrastructure provisioning","Developers deploying applications without deep infrastructure expertise","Teams standardizing infrastructure patterns across projects"],"limitations":["Generated infrastructure may not align with organization-specific policies or compliance requirements","Cannot validate configurations against actual cloud provider APIs without credentials","Security configurations (IAM policies, network rules) require manual review","Cost optimization and resource sizing are heuristic-based"],"requires":["OpenAI API key or OpenRouter proxy access","Architecture description or infrastructure requirements"],"input_types":["text (infrastructure requirements, architecture descriptions)"],"output_types":["code (Terraform, CloudFormation, Kubernetes, Docker configurations)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key or OpenRouter proxy access","HTTP/REST client capability for API calls","Network connectivity to OpenAI or OpenRouter endpoints","Code samples in text format (not binary or compiled code)","Source code with docstrings and type hints","Code samples and error messages in text format","Code samples in text format with sufficient context","Clear, detailed natural language descriptions","Source code in text format","Specification of target programming language"],"failure_modes":["Context window limited to ~4,000 tokens; cannot handle entire large files or complex multi-file dependencies","May generate syntactically valid but semantically incorrect code without explicit type hints or docstrings","Performance degrades for niche or domain-specific languages with limited training data representation","No real-time linting or compilation feedback; generated code requires manual testing","Explanations may oversimplify complex business logic or domain-specific algorithms","Cannot infer intent from poorly structured or obfuscated code","Generated documentation requires human review for accuracy and tone consistency","Context window limits prevent explaining entire large modules in a single request","Cannot infer business context or user-facing feature descriptions from code alone","May produce verbose or redundant documentation without proper curation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"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-5.1-codex-mini","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-5.1-codex-mini"}},"signature":"BtRx307eJ4O5O+JmJi+P0H2dBXgFm/YyA7eysEdkXLp7QT4OrU3XLfPsc8RKvd6ct2864n15m73rf8wxgGSCCQ==","signedAt":"2026-06-21T16:01:40.278Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-5.1-codex-mini","artifact":"https://unfragile.ai/openai-gpt-5.1-codex-mini","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-5.1-codex-mini","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"}}