{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-5.1-codex-max","slug":"openai-gpt-5.1-codex-max","name":"OpenAI: GPT-5.1-Codex-Max","type":"model","url":"https://openrouter.ai/models/openai~gpt-5.1-codex-max","page_url":"https://unfragile.ai/openai-gpt-5.1-codex-max","categories":["model-training"],"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-5.1-codex-max__cap_0","uri":"capability://code.generation.editing.agentic.long.context.code.generation.with.reasoning","name":"agentic long-context code generation with reasoning","description":"Generates code across multi-file projects using an updated reasoning stack that decomposes complex development tasks into sub-steps before execution. 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The model understands language-specific paradigms (functional vs imperative, memory management, concurrency models) and generates code that follows community standards and best practices for each target language, not generic pseudo-code.","intents":["I need to generate code in a language I'm less familiar with but want it to follow that language's idioms","I want to translate logic across languages while preserving language-specific optimizations","I need code that uses the right libraries and patterns for a specific language ecosystem","I want generated code that passes linters and style checkers without modification"],"best_for":["polyglot teams building systems across multiple languages","developers learning new languages who want idiomatic examples","teams maintaining SDKs or libraries across language ecosystems"],"limitations":["Quality varies by language — better for mainstream languages (Python, JavaScript, Go, Rust) than niche languages","Cannot guarantee generated code is production-ready without testing","Language-specific libraries and frameworks evolve faster than model training data","May not understand cutting-edge language features released after training cutoff"],"requires":["OpenAI API key","Target language specification in request","Optional: language-specific linter/formatter for post-processing"],"input_types":["natural language requirements","pseudocode or logic description","code in one language to translate","language target specification"],"output_types":["syntactically correct source code","idiomatic code following language conventions","code with appropriate error handling for the language"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-max__cap_3","uri":"capability://code.generation.editing.debugging.and.error.diagnosis.with.execution.context","name":"debugging and error diagnosis with execution context","description":"Analyzes error messages, stack traces, and code context to diagnose root causes and suggest fixes. The model reasons about the relationship between error symptoms and underlying code issues, considering type mismatches, logic errors, resource leaks, and concurrency problems. It can trace execution paths and identify where assumptions break down, generating targeted fixes rather than generic suggestions.","intents":["I have an error and need the model to trace through my code to find the root cause","I want suggestions for fixing bugs that considers the full execution context","I need help understanding why a test is failing and what code change will fix it","I want the model to identify potential runtime errors before they occur"],"best_for":["developers debugging complex systems with non-obvious failure modes","teams using the model as a code review tool to catch bugs before production","developers working with unfamiliar codebases who need help understanding error chains"],"limitations":["Requires sufficient context (error message + relevant code) — incomplete information leads to inaccurate diagnosis","Cannot debug issues that require runtime state inspection or debugger breakpoints","May miss race conditions or timing-dependent bugs that don't consistently reproduce","Limited to errors that manifest in logs or stack traces; silent logic errors are harder to detect"],"requires":["OpenAI API key","Error message or stack trace","Relevant source code context","Optional: test case that reproduces the error"],"input_types":["error messages and stack traces","source code","test cases or reproduction steps","execution logs"],"output_types":["root cause analysis","suggested code fixes","explanation of why the error occurred","prevention strategies"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-max__cap_4","uri":"capability://code.generation.editing.code.review.and.architectural.analysis.with.pattern.recognition","name":"code review and architectural analysis with pattern recognition","description":"Analyzes code for architectural issues, design pattern violations, performance problems, and maintainability concerns by recognizing structural patterns and reasoning about long-term implications. 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The model analyzes function signatures, logic, and dependencies to synthesize tests that cover normal paths, boundary conditions, error cases, and integration scenarios. It generates tests in the appropriate testing framework for the target language and includes assertions that verify both correctness and side effects.","intents":["I want the model to generate test cases that cover edge cases I might miss","I need to write tests for legacy code that has no test coverage","I want integration tests that verify multiple components work together correctly","I need to generate tests that verify error handling and edge cases"],"best_for":["teams improving test coverage on legacy codebases","developers writing tests for complex logic with many edge cases","teams using test-driven development who want assistance generating test cases"],"limitations":["Generated tests may not cover all domain-specific edge cases without explicit guidance","Cannot generate tests for non-deterministic or time-dependent behavior without additional context","Test quality depends on code clarity — poorly documented code produces weaker tests","May generate redundant tests or miss subtle interaction bugs"],"requires":["OpenAI API key","Source code to test","Target testing framework specification","Optional: existing test examples to match style"],"input_types":["source code (functions, classes, modules)","function signatures and documentation","existing test examples (for style matching)"],"output_types":["test code in target framework","test cases covering normal and edge cases","assertions and expected values"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-max__cap_6","uri":"capability://text.generation.language.documentation.generation.and.code.explanation","name":"documentation generation and code explanation","description":"Generates comprehensive documentation by analyzing code structure, logic, and dependencies to produce accurate descriptions of functionality, parameters, return values, and usage examples. The model generates documentation in multiple formats (docstrings, markdown, API docs) and can explain complex logic in natural language, making code more maintainable and accessible to new team members.","intents":["I need to generate docstrings for functions that currently have none","I want to create API documentation from my source code","I need to explain complex algorithms in natural language for team members","I want to generate README sections that describe how to use my code"],"best_for":["teams improving code documentation on legacy projects","open-source maintainers generating API documentation","developers onboarding new team members who need code explanations"],"limitations":["Generated documentation may be inaccurate if code is unclear or poorly structured","Cannot generate documentation for undocumented external dependencies without additional context","May produce verbose or redundant documentation that requires manual editing","Struggles with domain-specific terminology not present in training data"],"requires":["OpenAI API key","Source code to document","Optional: documentation style guide or examples"],"input_types":["source code","function signatures","code comments (if present)"],"output_types":["docstrings in language-specific format","markdown documentation","API documentation","usage examples"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-max__cap_7","uri":"capability://tool.use.integration.schema.based.function.calling.with.multi.provider.support","name":"schema-based function calling with multi-provider support","description":"Enables the model to call external functions and APIs by defining function schemas and routing calls to appropriate providers (OpenAI, Anthropic, Ollama, or custom endpoints). The model reasons about which functions to call, in what order, and with what parameters to accomplish user goals, supporting both synchronous and asynchronous execution with error handling and retry logic.","intents":["I want the model to call my APIs to fetch data or perform actions","I need the model to use tools to accomplish complex tasks that require external data","I want to build an agent that can orchestrate multiple API calls","I need function calling that works across different LLM providers"],"best_for":["developers building LLM agents that need external tool access","teams building multi-provider LLM applications","applications requiring real-time data fetching or action execution"],"limitations":["Requires explicit function schema definitions — no automatic API discovery","Model may hallucinate function calls or parameters if schemas are unclear","Error handling depends on function implementation — model cannot recover from all failures","Latency increases with number of function calls due to round-trip overhead"],"requires":["OpenAI API key (or alternative provider key)","Function schema definitions in JSON Schema format","HTTP endpoints or SDK bindings for functions","Error handling and retry logic in application layer"],"input_types":["natural language user requests","function schemas","function execution results"],"output_types":["function calls with parameters","execution results","final response to user"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-max__cap_8","uri":"capability://memory.knowledge.context.window.optimization.and.token.management","name":"context window optimization and token management","description":"Intelligently manages context by prioritizing relevant information, compressing less important details, and maintaining coherence across extended interactions. The model uses techniques like hierarchical summarization and selective context inclusion to maximize useful information within token limits, enabling longer conversations and larger codebase analysis without losing critical context.","intents":["I want to work with large codebases without hitting context limits","I need the model to remember important context across many turns of conversation","I want to analyze large documents while keeping the most relevant information","I need efficient context usage to reduce API costs"],"best_for":["developers working with large codebases (>100K lines)","applications requiring extended multi-turn conversations","teams optimizing API costs by reducing token consumption"],"limitations":["Context compression may lose important details if prioritization is incorrect","Very large codebases (>1M tokens) may still exceed context limits","Requires explicit guidance on what information is most important","Hierarchical summarization adds latency to initial context processing"],"requires":["OpenAI API key","Context management layer in application","Optional: codebase indexing or summarization"],"input_types":["source code","conversation history","document content"],"output_types":["prioritized context","compressed summaries","token usage estimates"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5.1-codex-max__cap_9","uri":"capability://planning.reasoning.agentic.task.decomposition.and.planning","name":"agentic task decomposition and planning","description":"Breaks down complex development tasks into sub-tasks and creates execution plans by reasoning about dependencies, prerequisites, and optimal ordering. 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