{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-x-ai-grok-code-fast-1","slug":"x-ai-grok-code-fast-1","name":"xAI: Grok Code Fast 1","type":"model","url":"https://openrouter.ai/models/x-ai~grok-code-fast-1","page_url":"https://unfragile.ai/x-ai-grok-code-fast-1","categories":["code-review-security","testing-quality"],"tags":["x-ai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-x-ai-grok-code-fast-1__cap_0","uri":"capability://code.generation.editing.agentic.code.reasoning.with.visible.traces","name":"agentic-code-reasoning-with-visible-traces","description":"Grok Code Fast 1 performs multi-step reasoning over code problems with intermediate reasoning traces exposed in the response stream, allowing developers to inspect and validate the model's decision-making process at each step. The architecture uses chain-of-thought decomposition internally, surfacing thought tokens alongside final outputs so users can debug reasoning failures or steer the model toward better solutions through follow-up prompts.","intents":["I need to understand why the model chose a particular code solution so I can correct it if needed","I want to debug complex coding tasks by seeing the model's intermediate reasoning steps","I need to validate that the model is reasoning correctly before trusting its code output","I want to iteratively refine code solutions by steering the model based on its visible reasoning"],"best_for":["AI engineers building agentic coding systems who need interpretability","developers debugging LLM-generated code and need to understand failure modes","teams implementing chain-of-thought prompting patterns for code tasks"],"limitations":["Visible reasoning traces increase token consumption and latency compared to non-reasoning models","Reasoning quality depends on problem complexity — very simple tasks may show redundant reasoning steps","Trace format and structure are proprietary to xAI; no standardized schema for parsing reasoning tokens"],"requires":["API key for xAI or OpenRouter access","HTTP client capable of streaming responses","Token budget sufficient for reasoning overhead (typically 2-4x base completion tokens)"],"input_types":["text","code snippets","natural language problem descriptions","structured code context with file paths"],"output_types":["code","reasoning traces (intermediate thought tokens)","explanations","structured code with comments"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-code-fast-1__cap_1","uri":"capability://code.generation.editing.fast.economical.code.generation","name":"fast-economical-code-generation","description":"Grok Code Fast 1 is optimized for speed and cost efficiency in code generation tasks, using a smaller model architecture and inference optimizations to reduce latency and token consumption compared to larger reasoning models. The model balances reasoning capability with inference speed through selective computation — applying deep reasoning only to complex code patterns while using faster heuristics for routine completions.","intents":["I need to generate code quickly for time-sensitive development tasks without waiting for heavy reasoning models","I want to reduce API costs when building high-volume code generation features","I need to integrate code generation into real-time developer tools with sub-second latency requirements","I want to batch-process many small coding tasks economically"],"best_for":["startups and indie developers with limited API budgets","teams building real-time IDE extensions requiring <500ms latency","high-volume code generation pipelines processing thousands of requests daily","developers prototyping agentic systems before scaling to production"],"limitations":["Smaller model capacity means reduced performance on highly complex multi-file refactoring tasks","May struggle with domain-specific code patterns not well-represented in training data","Speed optimizations may result in less thorough reasoning on edge cases compared to larger models","No fine-tuning or custom model variants available"],"requires":["OpenRouter API key or direct xAI API access","Standard HTTP/2 client for API calls","Typical token budget: 50-500 tokens per request depending on task complexity"],"input_types":["code","natural language descriptions","code context and file structure","test cases and requirements"],"output_types":["code","code snippets","refactored code","inline completions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-code-fast-1__cap_2","uri":"capability://code.generation.editing.multi.turn.agentic.code.steering","name":"multi-turn-agentic-code-steering","description":"Grok Code Fast 1 supports iterative refinement of code solutions through multi-turn conversations where developers can provide feedback, constraints, or corrections based on the model's visible reasoning traces. The model maintains conversation context across turns, allowing agents to steer the model toward better solutions by pointing out reasoning errors or requesting alternative approaches without re-submitting the full problem context.","intents":["I want to iteratively improve code quality by pointing out issues in the model's reasoning and asking for alternatives","I need to implement an agentic loop where the model generates code, I validate it, and the model refines based on feedback","I want to constrain code generation by specifying architectural requirements after seeing the initial solution","I need to explore multiple solution approaches by asking the model to reconsider its reasoning"],"best_for":["AI engineers building multi-turn code generation agents","teams implementing human-in-the-loop code review workflows","developers creating interactive coding assistants with refinement loops","systems requiring iterative code optimization based on performance feedback"],"limitations":["Context window limitations may constrain very long multi-turn conversations with large code files","Model may repeat previous reasoning patterns if not explicitly contradicted","No built-in memory of previous conversation sessions — each new conversation starts fresh","Steering effectiveness depends on clarity of developer feedback; ambiguous corrections may be misinterpreted"],"requires":["API client supporting stateful conversation management","Mechanism to track and pass conversation history across API calls","Context window budget sufficient for code + reasoning traces + feedback (typically 8k-32k tokens)"],"input_types":["code","natural language feedback","constraint specifications","test results and error messages","architectural requirements"],"output_types":["refined code","updated reasoning traces","explanations of changes","alternative solutions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-code-fast-1__cap_3","uri":"capability://code.generation.editing.code.testing.and.quality.validation","name":"code-testing-and-quality-validation","description":"Grok Code Fast 1 can generate test cases, validate code correctness, and identify potential bugs through reasoning-based analysis of code logic and edge cases. The model uses its reasoning capability to trace through code execution paths, identify boundary conditions, and suggest test cases that cover critical scenarios, with reasoning traces showing the validation logic applied.","intents":["I want the model to generate comprehensive test cases for my code automatically","I need to identify potential bugs or edge cases in code before deployment","I want to validate that generated code handles error conditions properly","I need to understand why the model thinks code might fail in certain scenarios"],"best_for":["QA engineers automating test case generation","developers building code quality gates into CI/CD pipelines","teams implementing automated code review with reasoning-based validation","solo developers who need a second opinion on code correctness"],"limitations":["Model may miss subtle concurrency bugs or race conditions in multi-threaded code","Security vulnerability detection is limited to common patterns; zero-days or novel exploits may not be caught","Test case generation may not achieve 100% code coverage without explicit guidance","Reasoning traces show the model's logic but don't guarantee correctness of the validation itself"],"requires":["Code in supported languages (Python, JavaScript, Java, C++, Go, Rust, etc.)","Clear specification of code intent and expected behavior","Test framework context (pytest, Jest, JUnit, etc.) if generating framework-specific tests"],"input_types":["code","function signatures","docstrings and comments","existing test examples","error logs and failure reports"],"output_types":["test cases","bug reports","validation reasoning traces","edge case descriptions","code quality assessments"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-code-fast-1__cap_4","uri":"capability://text.generation.language.streaming.response.with.reasoning.tokens","name":"streaming-response-with-reasoning-tokens","description":"Grok Code Fast 1 streams responses token-by-token, including intermediate reasoning tokens, allowing developers to consume partial results in real-time and cancel long-running requests early. The streaming architecture separates reasoning tokens from output tokens, enabling clients to display reasoning progress separately from final code output or to aggregate reasoning before displaying final results.","intents":["I want to display reasoning progress to users in real-time as the model thinks through a problem","I need to cancel expensive requests early if the model's reasoning is going in the wrong direction","I want to build responsive UIs that show incremental code generation without waiting for full completion","I need to process reasoning and code output separately for different UI components"],"best_for":["frontend developers building interactive coding assistants with real-time feedback","teams implementing streaming code generation in web or desktop IDEs","developers building cost-conscious systems that can cancel requests mid-stream","systems requiring real-time reasoning visualization"],"limitations":["Streaming adds complexity to client-side code for handling partial tokens and error recovery","Network latency can cause stuttering in reasoning token display if not buffered properly","Early cancellation may leave the model in an inconsistent state if not handled carefully","Reasoning token format is not standardized; clients must parse xAI-specific token types"],"requires":["HTTP client with streaming/chunked transfer support (fetch with ReadableStream, axios with responseType: 'stream', etc.)","Token parser to distinguish reasoning tokens from output tokens","Error handling for stream interruption and reconnection logic"],"input_types":["code","natural language prompts","streaming request bodies"],"output_types":["streaming text tokens","reasoning tokens (intermediate thoughts)","code tokens","structured streaming events"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-code-fast-1__cap_5","uri":"capability://code.generation.editing.language.agnostic.code.generation","name":"language-agnostic-code-generation","description":"Grok Code Fast 1 supports code generation across multiple programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#, PHP, etc.) with language-aware reasoning that understands language-specific idioms, standard libraries, and best practices. The model applies language-specific reasoning patterns to generate idiomatic code rather than generic translations.","intents":["I need to generate code in multiple languages from a single natural language specification","I want the model to understand language-specific idioms and best practices for the target language","I need to refactor code from one language to another while maintaining semantic equivalence","I want to generate polyglot solutions that work across multiple language ecosystems"],"best_for":["polyglot development teams working across multiple languages","developers building code generation tools that support multiple language targets","teams migrating codebases between languages","open-source projects with multi-language support requirements"],"limitations":["Code quality varies by language — better support for mainstream languages (Python, JavaScript, Java) than niche languages","Language-specific libraries and frameworks may not be well-represented in training data","Refactoring between languages may lose language-specific optimizations or idioms","No explicit language version specification — generated code targets recent stable versions"],"requires":["Explicit language specification in the prompt","Language-specific context (framework, library versions, coding standards)","Knowledge of target language syntax and semantics to validate output"],"input_types":["code in any supported language","natural language specifications","language-agnostic pseudocode","algorithm descriptions"],"output_types":["code in target language","language-specific idioms and patterns","framework-specific implementations"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-code-fast-1__cap_6","uri":"capability://code.generation.editing.context.aware.code.completion","name":"context-aware-code-completion","description":"Grok Code Fast 1 performs code completion that understands surrounding code context, including variable definitions, function signatures, imported libraries, and project structure, to generate contextually appropriate completions. The model uses reasoning to infer intent from context rather than simple pattern matching, enabling more accurate completions for complex scenarios.","intents":["I want code completions that understand the full context of my file, not just the immediate line","I need completions that respect my project's coding style and conventions","I want the model to infer my intent from surrounding code and suggest the most likely next step","I need completions that work correctly with my project's dependencies and imports"],"best_for":["IDE extension developers building intelligent code completion","teams with strong coding conventions who want completions that match their style","developers working in large codebases where context is critical","projects with custom frameworks or domain-specific patterns"],"limitations":["Context window limits how much surrounding code can be provided (typically 8k-32k tokens)","Model may not understand project-specific conventions without explicit examples","Completion quality degrades if context is incomplete or ambiguous","No built-in caching of context across multiple completions in the same file"],"requires":["Code context from the current file (typically 50-500 lines around cursor)","Import statements and dependency information","Optional: project structure or configuration files for additional context"],"input_types":["code","cursor position","surrounding code context","import statements","project configuration"],"output_types":["code completions","multi-line suggestions","function implementations","code snippets"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-code-fast-1__cap_7","uri":"capability://code.generation.editing.code.refactoring.with.reasoning","name":"code-refactoring-with-reasoning","description":"Grok Code Fast 1 can refactor code while maintaining semantic equivalence, using reasoning to understand the original intent and constraints before suggesting improvements. The model reasons about refactoring trade-offs (readability vs performance, maintainability vs brevity) and exposes this reasoning so developers can understand why specific refactoring choices were made.","intents":["I want to refactor code for readability while maintaining the same functionality","I need to optimize code performance and want to understand the trade-offs involved","I want to modernize legacy code to use current language features and best practices","I need to refactor code to match my team's coding standards and conventions"],"best_for":["teams performing large-scale code modernization","developers maintaining legacy codebases","teams establishing new coding standards and needing to retrofit existing code","performance optimization efforts where reasoning about trade-offs is important"],"limitations":["Refactoring may introduce subtle behavioral changes in edge cases not covered by visible reasoning","Performance optimizations suggested may not be valid for all runtime environments or hardware","Large files may exceed context window, requiring refactoring in chunks","No built-in testing to verify semantic equivalence after refactoring"],"requires":["Complete code to be refactored (or clear boundaries if refactoring in chunks)","Clear refactoring objectives (readability, performance, modernization, etc.)","Optional: existing tests to validate semantic equivalence"],"input_types":["code","refactoring objectives","coding standards or style guides","performance constraints","test cases"],"output_types":["refactored code","reasoning traces explaining refactoring decisions","trade-off analysis","migration guides for large refactorings"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-code-fast-1__cap_8","uri":"capability://code.generation.editing.api.and.integration.code.generation","name":"api-and-integration-code-generation","description":"Grok Code Fast 1 can generate code for API integrations, including REST client code, SDK usage, authentication handling, and error handling patterns, using reasoning to understand API documentation and generate correct, idiomatic integration code. The model reasons about API contracts, error codes, and best practices for each API type.","intents":["I want to generate boilerplate code for integrating with a REST API","I need to understand how to use an SDK correctly and want the model to generate example code","I want to generate authentication and error handling code for API integrations","I need to generate code that handles API rate limiting and retries correctly"],"best_for":["backend developers building API integrations","teams standardizing on specific APIs or SDKs","developers learning new APIs and needing working examples","teams building API client libraries or wrappers"],"limitations":["Model knowledge of APIs is limited to training data cutoff; newer APIs or API versions may not be well-supported","Generated code may not handle all edge cases or error scenarios specific to an API","Authentication patterns vary widely; generated code may need customization for specific auth schemes","No built-in validation that generated code actually works against the target API"],"requires":["API documentation or specification (OpenAPI, GraphQL schema, etc.)","API endpoint URLs and authentication credentials","Target language and framework context"],"input_types":["API documentation","OpenAPI/Swagger specs","GraphQL schemas","natural language API descriptions","example API requests/responses"],"output_types":["API client code","SDK usage examples","authentication code","error handling patterns","integration guides"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["API key for xAI or OpenRouter access","HTTP client capable of streaming responses","Token budget sufficient for reasoning overhead (typically 2-4x base completion tokens)","OpenRouter API key or direct xAI API access","Standard HTTP/2 client for API calls","Typical token budget: 50-500 tokens per request depending on task complexity","API client supporting stateful conversation management","Mechanism to track and pass conversation history across API calls","Context window budget sufficient for code + reasoning traces + feedback (typically 8k-32k tokens)","Code in supported languages (Python, JavaScript, Java, C++, Go, Rust, etc.)"],"failure_modes":["Visible reasoning traces increase token consumption and latency compared to non-reasoning models","Reasoning quality depends on problem complexity — very simple tasks may show redundant reasoning steps","Trace format and structure are proprietary to xAI; no standardized schema for parsing reasoning tokens","Smaller model capacity means reduced performance on highly complex multi-file refactoring tasks","May struggle with domain-specific code patterns not well-represented in training data","Speed optimizations may result in less thorough reasoning on edge cases compared to larger models","No fine-tuning or custom model variants available","Context window limitations may constrain very long multi-turn conversations with large code files","Model may repeat previous reasoning patterns if not explicitly contradicted","No built-in memory of previous conversation sessions — each new conversation starts fresh","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.34,"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":"2026-05-24T12:16:25.059Z","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=x-ai-grok-code-fast-1","compare_url":"https://unfragile.ai/compare?artifact=x-ai-grok-code-fast-1"}},"signature":"boIw0+WfUDjjsRjE9f3ICF0pWBlWhypOlUo4X3JM6gFKQgNj01XxBfGt2seGN+p07i0Ki1miHrVWyKEW0swRAQ==","signedAt":"2026-06-21T21:25:23.229Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/x-ai-grok-code-fast-1","artifact":"https://unfragile.ai/x-ai-grok-code-fast-1","verify":"https://unfragile.ai/api/v1/verify?slug=x-ai-grok-code-fast-1","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"}}