{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-x-ai-grok-4","slug":"x-ai-grok-4","name":"xAI: Grok 4","type":"model","url":"https://openrouter.ai/models/x-ai~grok-4","page_url":"https://unfragile.ai/x-ai-grok-4","categories":["image-generation"],"tags":["x-ai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-x-ai-grok-4__cap_0","uri":"capability://planning.reasoning.multi.modal.reasoning.with.256k.context.window","name":"multi-modal reasoning with 256k context window","description":"Processes both text and image inputs simultaneously within a 256,000 token context window, enabling extended reasoning chains across multi-page documents, codebases, and visual content. The architecture maintains token efficiency through selective attention mechanisms while preserving reasoning depth across long-form inputs, supporting complex multi-step problem decomposition without context truncation.","intents":["I need to analyze a 50-page technical specification alongside architectural diagrams in a single reasoning pass","I want to debug a complex codebase by providing full file context plus screenshots of runtime behavior","I need to reason about multi-image sequences (e.g., UI mockups, design iterations) in context"],"best_for":["research teams analyzing long-form documents with visual components","enterprise developers debugging complex systems with visual logs and code","AI engineers building reasoning-heavy applications requiring extended context"],"limitations":["256k context window is shared between input and reasoning — large image batches reduce available token budget for reasoning output","No explicit reasoning token budget control — reasoning depth is implicit and not user-configurable","Image encoding overhead reduces effective text context; exact token-to-image ratio not publicly specified"],"requires":["API access via OpenRouter or xAI direct API","Valid authentication credentials (API key)","Image inputs must be in supported formats (JPEG, PNG, WebP, GIF)"],"input_types":["text (plain text, markdown, code, structured prompts)","images (JPEG, PNG, WebP, GIF)","mixed text-image sequences"],"output_types":["text (reasoning chains, analysis, explanations)","structured reasoning traces"],"categories":["planning-reasoning","multi-modal-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_1","uri":"capability://tool.use.integration.parallel.tool.calling.with.structured.schema.binding","name":"parallel tool calling with structured schema binding","description":"Executes multiple tool invocations concurrently within a single model response using a schema-based function registry. The model generates structured JSON payloads matching predefined schemas, enabling orchestration of parallel API calls, database queries, and external service integrations without sequential round-trips. Implementation uses typed function signatures with validation against provided schemas before execution.","intents":["I need to fetch user data, check inventory, and process payment in parallel within a single model step","I want the model to call multiple APIs simultaneously and reason over their combined results","I need to execute 5+ database queries in parallel and aggregate results for decision-making"],"best_for":["agentic systems requiring high-throughput tool orchestration","multi-step workflows where parallel execution reduces latency","teams building complex integrations with multiple external services"],"limitations":["Schema validation is strict — malformed tool calls fail without graceful degradation","No built-in retry logic for failed parallel calls — requires external orchestration layer","Parallel execution latency depends on slowest tool; no timeout management per individual call","Tool calling adds ~50-100ms overhead per invocation due to schema validation and serialization"],"requires":["Tool schemas defined in JSON Schema format","API key for xAI or OpenRouter access","External tool execution environment (your application must handle actual tool invocation)"],"input_types":["text prompts with tool descriptions","JSON Schema definitions for tools","structured tool specifications"],"output_types":["JSON-formatted tool calls with parameters","structured function invocation payloads"],"categories":["tool-use-integration","function-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_10","uri":"capability://memory.knowledge.semantic.search.and.retrieval.augmented.generation.rag.support","name":"semantic search and retrieval-augmented generation (rag) support","description":"Integrates with external knowledge bases and document stores through tool calling, enabling retrieval-augmented generation where the model queries external sources and reasons over retrieved results. The model can formulate search queries, evaluate relevance of retrieved documents, and synthesize information from multiple sources. Implementation uses semantic understanding to identify relevant search terms and evaluate document relevance without explicit ranking.","intents":["I need the model to search a knowledge base and answer questions based on retrieved documents","I want to build a Q&A system that retrieves relevant documents and generates answers","I need to augment the model's knowledge with domain-specific documents"],"best_for":["teams building domain-specific Q&A systems","enterprise applications requiring knowledge base integration","research systems needing access to large document collections"],"limitations":["Retrieval quality depends on external search system — model cannot improve poor search results","No built-in vector database integration — requires external RAG infrastructure","Relevance evaluation is implicit; model may not always select most relevant documents","Context window limits affect number of documents that can be reasoned over simultaneously"],"requires":["API key for xAI or OpenRouter","External search/retrieval system (vector database, search engine, etc.)","Tool schema for search function"],"input_types":["natural language questions","search queries","document references"],"output_types":["search queries to external systems","synthesized answers from retrieved documents","source citations"],"categories":["memory-knowledge","retrieval-augmented-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_11","uri":"capability://planning.reasoning.adversarial.reasoning.and.edge.case.identification","name":"adversarial reasoning and edge case identification","description":"Analyzes problems to identify edge cases, potential failures, and adversarial inputs that could break proposed solutions. The model generates test cases, identifies boundary conditions, and reasons about failure modes without explicit prompting. Implementation uses reasoning patterns to systematically explore problem space and identify overlooked scenarios.","intents":["I need the model to identify edge cases in my code or algorithm","I want to understand potential failure modes before deploying a system","I need to generate comprehensive test cases covering edge cases"],"best_for":["quality assurance and testing teams","security-conscious development teams","teams building safety-critical systems"],"limitations":["Edge case identification is not exhaustive — model may miss domain-specific edge cases","Reasoning about security vulnerabilities requires domain expertise — generic security analysis is limited","No formal verification — identified edge cases are suggestions, not guarantees","Adversarial reasoning quality depends on problem domain familiarity"],"requires":["API key for xAI or OpenRouter","Clear problem description or code to analyze"],"input_types":["code or algorithm descriptions","system specifications","problem statements"],"output_types":["edge case descriptions","test case suggestions","failure mode analysis","boundary condition identification"],"categories":["planning-reasoning","testing-validation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_2","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.enforcement","name":"structured output generation with json schema enforcement","description":"Generates responses constrained to match a provided JSON Schema, ensuring output conforms to exact field names, types, and nesting structures. The model's token generation is guided by the schema constraints, preventing invalid JSON and guaranteeing parseable structured data. Implementation uses schema-aware decoding that prunes invalid token sequences during generation, ensuring 100% schema compliance without post-processing.","intents":["I need the model to always return a specific JSON structure (e.g., {name, email, age}) without parsing errors","I want to extract entities into a predefined schema and guarantee valid JSON output","I need to generate API responses that match my OpenAPI spec exactly"],"best_for":["data extraction pipelines requiring guaranteed schema compliance","API backends needing deterministic response structures","teams building LLM-powered data transformation workflows"],"limitations":["Schema enforcement reduces model expressiveness — complex nested structures may constrain reasoning quality","Large schemas (100+ fields) increase token overhead and latency by 15-25%","No support for conditional schemas or dynamic field requirements — schema must be static","Schema validation is strict; optional fields still require explicit null handling"],"requires":["JSON Schema definition (draft 7 or later)","API key for xAI or OpenRouter","Schema must be valid and non-circular"],"input_types":["text prompts","JSON Schema definitions"],"output_types":["JSON (guaranteed valid, schema-compliant)","structured data objects"],"categories":["data-processing-analysis","structured-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_3","uri":"capability://planning.reasoning.extended.reasoning.with.implicit.chain.of.thought","name":"extended reasoning with implicit chain-of-thought","description":"Performs multi-step reasoning internally without explicit token-counting or reasoning budget controls, generating coherent reasoning chains that decompose complex problems into sub-steps. The model allocates reasoning depth implicitly based on problem complexity, using attention mechanisms to identify critical reasoning paths. Output includes both reasoning traces and final answers, enabling transparency into decision-making without explicit reasoning token management.","intents":["I need the model to show its work on a complex math or logic problem","I want to understand how the model arrived at a conclusion through step-by-step reasoning","I need to debug model reasoning for accuracy validation"],"best_for":["research and analysis workflows requiring transparent reasoning","educational applications where reasoning traces add pedagogical value","quality assurance teams validating model decision-making"],"limitations":["Reasoning depth is implicit and not user-controllable — cannot request 'more reasoning' or 'less reasoning'","Reasoning traces consume output tokens, reducing space for final answer — trade-off not configurable","No explicit reasoning token budget; reasoning allocation is opaque","Reasoning quality degrades on highly specialized domains without domain-specific examples"],"requires":["API key for xAI or OpenRouter","Prompts structured to encourage step-by-step thinking"],"input_types":["text prompts","complex problems (math, logic, analysis)","multi-step scenarios"],"output_types":["reasoning traces (step-by-step explanations)","final answers","structured reasoning chains"],"categories":["planning-reasoning","chain-of-thought"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_4","uri":"capability://code.generation.editing.multi.language.code.generation.and.analysis","name":"multi-language code generation and analysis","description":"Generates, analyzes, and refactors code across 40+ programming languages using language-agnostic reasoning patterns. The model understands syntax, semantics, and idioms for each language, enabling cross-language code translation, bug detection, and optimization suggestions. Implementation uses abstract syntax tree (AST) reasoning internally, allowing structural code understanding without language-specific parsing.","intents":["I need to generate Python code that's equivalent to a TypeScript implementation","I want to analyze a multi-language codebase and identify cross-language bugs","I need to refactor code while maintaining language-specific best practices"],"best_for":["polyglot development teams working across multiple languages","code migration projects (e.g., Python to Go, JavaScript to Rust)","teams building language-agnostic code analysis tools"],"limitations":["Code generation quality varies by language popularity — less common languages (e.g., Elixir, Clojure) produce lower-quality output","No real-time compilation or execution validation — generated code requires testing","Context window limitations affect analysis of very large codebases (100k+ LOC)","Language-specific idioms and performance patterns may not be captured for niche languages"],"requires":["API key for xAI or OpenRouter","Code samples or descriptions in supported languages"],"input_types":["code snippets","full source files","code descriptions in natural language","mixed code and text prompts"],"output_types":["code (in requested language)","code analysis reports","refactoring suggestions","cross-language translations"],"categories":["code-generation-editing","multi-language-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_5","uri":"capability://image.visual.vision.based.document.understanding.and.extraction","name":"vision-based document understanding and extraction","description":"Analyzes images of documents (PDFs rendered as images, scanned documents, screenshots) to extract structured information including text, tables, forms, and layout relationships. The model performs OCR-like text extraction with semantic understanding of document structure, enabling form field extraction, table parsing, and document classification without separate OCR preprocessing. Implementation uses visual attention mechanisms to identify document regions and their semantic relationships.","intents":["I need to extract invoice data (line items, totals, dates) from a scanned PDF image","I want to parse a form image and extract field values into structured JSON","I need to classify documents by type and extract key information from each type"],"best_for":["document processing pipelines (invoices, receipts, forms)","data entry automation reducing manual data extraction","teams building document classification and information extraction systems"],"limitations":["Accuracy degrades on low-resolution images (<150 DPI) or heavily skewed documents","Handwritten text recognition is unreliable — works best with printed text","Complex multi-page documents require separate image inputs per page","Table extraction may fail on non-standard layouts or merged cells","No native PDF parsing — PDFs must be converted to images first"],"requires":["API key for xAI or OpenRouter","Document images in JPEG, PNG, WebP, or GIF format","Minimum image resolution of 150 DPI for reliable extraction"],"input_types":["document images (scanned PDFs, screenshots, photos)","form images","table images"],"output_types":["extracted text","structured JSON (form fields, table data)","document classification labels","layout analysis"],"categories":["image-visual","document-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_6","uri":"capability://tool.use.integration.real.time.api.integration.with.error.handling.and.fallback.logic","name":"real-time api integration with error handling and fallback logic","description":"Integrates with external APIs through tool calling with built-in error handling patterns, enabling graceful degradation when external services fail. The model can reason about API errors, retry logic, and fallback strategies, generating code that handles rate limiting, timeouts, and service unavailability. Implementation uses structured error schemas to communicate failure modes back to the model for adaptive response generation.","intents":["I need the model to call an API and handle 429 (rate limit) errors with exponential backoff","I want the model to fall back to cached data if an API call fails","I need to integrate multiple APIs with different error handling strategies"],"best_for":["agentic systems requiring robust external service integration","production systems where API failures must be handled gracefully","teams building resilient multi-service orchestration"],"limitations":["Error handling is model-generated, not guaranteed — model may not always choose optimal retry strategies","No built-in circuit breaker pattern — requires external implementation","Timeout handling is implicit; no explicit timeout configuration per tool call","Fallback logic must be explicitly specified in prompts — no automatic fallback discovery"],"requires":["API key for xAI or OpenRouter","Tool schemas with error response definitions","External API endpoints with documented error codes"],"input_types":["tool schemas with error definitions","API endpoint specifications","error handling instructions in prompts"],"output_types":["tool calls with error handling logic","fallback strategy selections","retry instructions"],"categories":["tool-use-integration","error-handling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_7","uri":"capability://image.visual.image.analysis.with.spatial.reasoning.and.relationship.detection","name":"image analysis with spatial reasoning and relationship detection","description":"Analyzes images to identify objects, spatial relationships, and contextual relationships between elements. The model performs scene understanding, object detection, and relationship reasoning in a single pass, enabling tasks like diagram analysis, UI/UX evaluation, and visual debugging. Implementation uses visual attention mechanisms to track object positions and relationships, supporting queries about 'what is next to X' or 'how are these elements related'.","intents":["I need to analyze a UI mockup and identify layout issues or accessibility problems","I want to understand a complex diagram (architecture, flowchart) and explain relationships","I need to debug a visual bug by analyzing screenshots and identifying the root cause"],"best_for":["design and UX teams evaluating mockups and prototypes","developers debugging visual issues through screenshots","teams analyzing complex diagrams and technical illustrations"],"limitations":["Spatial reasoning is approximate — exact pixel measurements are unreliable","Small or obscured objects may not be detected reliably","Relationship detection works best for obvious spatial relationships; subtle relationships may be missed","No support for video analysis — image-only capability"],"requires":["API key for xAI or OpenRouter","Image inputs in JPEG, PNG, WebP, or GIF format"],"input_types":["UI mockups and screenshots","diagrams and flowcharts","architectural drawings","photographs with multiple objects"],"output_types":["object identification and locations","spatial relationship descriptions","layout analysis","visual issue identification"],"categories":["image-visual","spatial-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_8","uri":"capability://code.generation.editing.context.aware.code.completion.with.codebase.understanding","name":"context-aware code completion with codebase understanding","description":"Generates code completions that understand the broader codebase context, including imported modules, defined functions, and coding patterns. The model analyzes surrounding code to infer intent and generate completions that match existing code style and architecture. Implementation uses AST-level code understanding to track scope, variable types, and function signatures, enabling semantically-aware completions without full codebase indexing.","intents":["I need code completion that understands my project's architecture and patterns","I want the model to suggest the next function call based on what I've already written","I need completions that match my codebase's style and naming conventions"],"best_for":["developers using Grok 4 via API in IDE integrations","teams building custom code editors with LLM-powered completion","developers working in large codebases with consistent patterns"],"limitations":["Context window limits prevent full codebase analysis for very large projects (100k+ LOC)","Completion quality depends on code clarity — poorly documented code produces lower-quality suggestions","No real-time compilation feedback — suggestions may not compile without testing","Requires explicit context provision; no automatic codebase indexing"],"requires":["API key for xAI or OpenRouter","Code context (surrounding code, imports, function signatures)","Cursor position or completion trigger point"],"input_types":["partial code with cursor position","surrounding code context","import statements and module definitions"],"output_types":["code completions","multiple completion suggestions","completion explanations"],"categories":["code-generation-editing","code-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-x-ai-grok-4__cap_9","uri":"capability://text.generation.language.multi.turn.conversation.with.memory.and.context.preservation","name":"multi-turn conversation with memory and context preservation","description":"Maintains conversation state across multiple turns, preserving context from previous messages and building on prior reasoning. The model tracks conversation history implicitly within the context window, enabling follow-up questions, clarifications, and iterative refinement without explicit context management. Implementation uses attention mechanisms to weight recent context more heavily while maintaining access to earlier conversation threads.","intents":["I need to ask follow-up questions and have the model remember previous context","I want to iteratively refine a solution through multiple conversation turns","I need to maintain a conversation about a complex topic across many exchanges"],"best_for":["interactive debugging and problem-solving workflows","iterative design and refinement processes","educational and tutoring applications"],"limitations":["Context window is shared between conversation history and new input — long conversations reduce space for new queries","No explicit conversation memory beyond context window — conversations longer than 256k tokens lose early context","No built-in conversation persistence — requires external storage for multi-session conversations","Context window limitations affect ability to reference very early conversation turns"],"requires":["API key for xAI or OpenRouter","Conversation history passed as input (no server-side session storage)"],"input_types":["conversation messages (user and assistant turns)","follow-up questions","clarifications and refinements"],"output_types":["contextual responses","follow-up suggestions","refined solutions"],"categories":["text-generation-language","conversation-management"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or xAI direct API","Valid authentication credentials (API key)","Image inputs must be in supported formats (JPEG, PNG, WebP, GIF)","Tool schemas defined in JSON Schema format","API key for xAI or OpenRouter access","External tool execution environment (your application must handle actual tool invocation)","API key for xAI or OpenRouter","External search/retrieval system (vector database, search engine, etc.)","Tool schema for search function","Clear problem description or code to analyze"],"failure_modes":["256k context window is shared between input and reasoning — large image batches reduce available token budget for reasoning output","No explicit reasoning token budget control — reasoning depth is implicit and not user-configurable","Image encoding overhead reduces effective text context; exact token-to-image ratio not publicly specified","Schema validation is strict — malformed tool calls fail without graceful degradation","No built-in retry logic for failed parallel calls — requires external orchestration layer","Parallel execution latency depends on slowest tool; no timeout management per individual call","Tool calling adds ~50-100ms overhead per invocation due to schema validation and serialization","Retrieval quality depends on external search system — model cannot improve poor search results","No built-in vector database integration — requires external RAG infrastructure","Relevance evaluation is implicit; model may not always select most relevant documents","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":"2026-05-24T12:16:25.059Z","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-4","compare_url":"https://unfragile.ai/compare?artifact=x-ai-grok-4"}},"signature":"hI5854WNjRe7aBmnpm+NeWFPy8LT3IeQI2hlr7pyZd1xsSEeag421M2ota2onvMMwPLpeRrZjm/UuT4aOYi+Bg==","signedAt":"2026-06-22T04:34:03.472Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/x-ai-grok-4","artifact":"https://unfragile.ai/x-ai-grok-4","verify":"https://unfragile.ai/api/v1/verify?slug=x-ai-grok-4","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"}}