{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"grok-2","slug":"grok-2","name":"Grok-2","type":"model","url":"https://x.ai/grok","page_url":"https://unfragile.ai/grok-2","categories":["llm-apis","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"grok-2__cap_0","uri":"capability://search.retrieval.real.time.social.discourse.analysis.with.x.platform.integration","name":"real-time social discourse analysis with x platform integration","description":"Grok-2 integrates directly with X (Twitter) platform APIs to access live feed data, trending topics, and real-time conversations, enabling the model to ground responses in current events and social discourse without relying on static training data cutoffs. The architecture appears to use a retrieval-augmented generation (RAG) pattern where X API calls are triggered contextually during inference to fetch relevant tweets, user discussions, and trending hashtags that inform the model's responses. This differs fundamentally from standard LLMs that operate on fixed knowledge cutoffs.","intents":["I need current information about what's trending on social media right now","Analyze real-time public sentiment on a breaking news event or topic","Get context about ongoing conversations and discourse on X without manual searching","Build applications that need live social data integrated with conversational AI"],"best_for":["news analysts and journalists needing real-time social context","product teams building social listening features","developers building X-integrated applications requiring current discourse analysis","researchers studying real-time information propagation and trends"],"limitations":["Requires active X API access and rate limits apply (standard X API tier limits of 300-450 requests per 15 minutes)","Real-time data retrieval adds latency to response generation (estimated 500ms-2s additional per query)","Dependent on X platform availability and API stability","Cannot access private/protected tweets or accounts without appropriate authentication","Historical data retrieval limited to X API's standard lookback window (typically 7 days for standard endpoints)"],"requires":["Active internet connection for X API calls","X API access (free tier or paid tier depending on usage volume)","Grok-2 API key or web interface access","Understanding of X API rate limits and authentication requirements"],"input_types":["natural language queries","topic names and hashtags","user handles and mentions","temporal queries (e.g., 'what's trending now')"],"output_types":["conversational text responses with cited social data","structured summaries of trending topics","sentiment analysis of discourse","curated tweet excerpts with context"],"categories":["search-retrieval","memory-knowledge","real-time-data-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_1","uri":"capability://memory.knowledge.extended.context.window.reasoning.with.128k.token.capacity","name":"extended context window reasoning with 128k token capacity","description":"Grok-2 processes up to 128,000 tokens in a single context window, enabling analysis of long documents, multi-file codebases, extended conversations, and complex reasoning tasks without context truncation. The architecture uses efficient attention mechanisms (likely sparse or hierarchical attention patterns) to manage the computational overhead of long sequences while maintaining coherent reasoning across the full context. This allows the model to maintain consistency and reference details across much longer inputs than standard 4K-8K context models.","intents":["Analyze entire codebases or large documentation sets in a single request","Maintain coherent multi-turn conversations with full history without losing context","Process long research papers, legal documents, or technical specifications end-to-end","Perform complex reasoning tasks requiring reference to many prior statements or examples"],"best_for":["developers working with large codebases requiring full-file context","researchers analyzing lengthy academic papers or datasets","legal and compliance teams reviewing extended documents","content creators managing long-form writing projects with consistent context"],"limitations":["Latency increases with context length (estimated 2-5x slower for 128K tokens vs 4K tokens)","API costs scale with token usage (both input and output tokens counted)","Attention mechanism efficiency degrades at extreme lengths despite optimizations","Not all use cases benefit from 128K context — many tasks plateau in quality at 16K-32K tokens"],"requires":["Grok-2 API access or web interface","Sufficient API quota for large token counts","Client-side token counting to avoid exceeding limits","Understanding of token economics for cost estimation"],"input_types":["long-form text documents","multiple code files concatenated","extended conversation histories","research papers and technical specifications","images (within context window)"],"output_types":["coherent analysis spanning full input","code refactoring with full-codebase awareness","detailed summaries with cross-references","reasoning chains referencing multiple input sections"],"categories":["memory-knowledge","planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_10","uri":"capability://planning.reasoning.instruction.following.and.task.decomposition","name":"instruction-following and task decomposition","description":"Grok-2 follows complex instructions and decomposes multi-step tasks into manageable subtasks, executing each step logically and coherently. The model understands task requirements, identifies dependencies between steps, and provides structured solutions that address all aspects of the instruction. This capability is enabled by instruction tuning during training and strong reasoning capabilities that allow the model to plan and execute complex workflows.","intents":["Execute complex multi-step instructions with multiple requirements","Break down ambiguous or complex tasks into clear subtasks","Follow specific formatting, style, or structural requirements","Manage tasks with dependencies and conditional logic"],"best_for":["developers building task automation and workflow systems","teams needing AI to execute complex procedures reliably","content creators with specific formatting or structural requirements","researchers running complex experimental protocols"],"limitations":["Instruction-following quality degrades with very long or ambiguous instructions","Model may miss edge cases or implicit requirements not explicitly stated","Complex conditional logic may be misinterpreted without clear specification","No ability to verify task completion or validate outputs against requirements","Cannot execute actual system tasks — only generates plans and descriptions"],"requires":["Grok-2 API access or web interface","Clear, well-structured instructions","Explicit specification of requirements and constraints","Manual verification of task execution and outputs"],"input_types":["complex multi-step instructions","task descriptions with requirements","formatting and structural specifications","conditional logic and dependencies"],"output_types":["structured task decompositions","step-by-step execution plans","formatted outputs matching specifications","reasoning chains explaining task execution"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_2","uri":"capability://image.visual.multimodal.image.understanding.and.visual.reasoning","name":"multimodal image understanding and visual reasoning","description":"Grok-2 accepts images as input alongside text and performs visual understanding tasks including object detection, scene analysis, text extraction from images (OCR), and visual reasoning. The model processes images through a vision encoder (likely a ViT-style architecture) that converts visual information into token embeddings compatible with the language model's transformer, enabling seamless integration of visual and textual reasoning in a single forward pass. This allows users to ask questions about images, analyze diagrams, or extract information from visual content without separate preprocessing.","intents":["Extract text from screenshots, documents, or images (OCR functionality)","Analyze charts, diagrams, and technical drawings to understand their content","Answer questions about image content and visual relationships","Debug visual issues by analyzing screenshots of UI or error states"],"best_for":["developers debugging UI issues and visual bugs","teams processing documents and extracting structured data from images","researchers analyzing visual data and diagrams","content creators needing image analysis and description generation"],"limitations":["Image resolution and quality affect accuracy (very low-res or heavily compressed images may fail)","OCR accuracy varies by font, language, and image quality (estimated 85-95% accuracy for standard documents)","Cannot process video input — only static images","Image processing adds latency (estimated 200-500ms per image depending on resolution)","No fine-grained pixel-level manipulation or image generation capabilities"],"requires":["Grok-2 API access or web interface","Image files in supported formats (JPEG, PNG, WebP, GIF)","Image size within API limits (typically 20MB or less)","Proper image encoding/transmission to API endpoint"],"input_types":["JPEG images","PNG images","WebP images","GIF images","Screenshots","Diagrams and charts","Documents and scanned pages"],"output_types":["natural language descriptions of image content","extracted text (OCR output)","structured data extracted from visual content","answers to visual reasoning questions","analysis of relationships and patterns in images"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_3","uri":"capability://text.generation.language.conversational.reasoning.with.distinctive.personality.and.wit","name":"conversational reasoning with distinctive personality and wit","description":"Grok-2 is trained with a distinctive conversational style that combines technical helpfulness with humor and personality, making interactions more engaging than standard corporate LLM responses. This is achieved through instruction tuning and RLHF (Reinforcement Learning from Human Feedback) that optimizes for personality consistency while maintaining accuracy and helpfulness. The model balances being informative with being entertaining, using context-aware humor and witty responses that don't compromise on technical correctness or safety.","intents":["Get technical help with a more engaging and entertaining conversational experience","Receive accurate information delivered with personality and humor","Have natural conversations that feel less robotic than standard LLM interactions","Engage with an AI that can understand and respond to sarcasm and wit in user queries"],"best_for":["individual developers and builders preferring conversational AI with personality","teams building chatbots or conversational interfaces that need to feel human-like","content creators and writers seeking AI assistance with engaging tone","users who find standard corporate LLM responses impersonal or boring"],"limitations":["Personality-driven responses may be less appropriate for formal/professional contexts (legal, medical, financial advice)","Humor and wit are subjective — responses may not align with all user preferences or cultural contexts","Personality consistency may vary across different conversation topics or contexts","Witty responses could potentially obscure important caveats or limitations in technical advice"],"requires":["Grok-2 API access or web interface","Acceptance of conversational style (cannot be disabled or customized)","Understanding that personality is part of the model's design, not a bug"],"input_types":["natural language queries","technical questions","creative prompts","sarcastic or humorous user input"],"output_types":["witty conversational responses","technically accurate answers with personality","humorous explanations of complex topics","engaging dialogue that maintains context and tone"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_4","uri":"capability://planning.reasoning.benchmark.competitive.reasoning.and.problem.solving","name":"benchmark-competitive reasoning and problem-solving","description":"Grok-2 achieves competitive performance on standard AI benchmarks (MMLU, HumanEval, and others) comparable to GPT-4o and Claude 3.5 Sonnet, indicating strong reasoning capabilities across diverse domains including mathematics, coding, knowledge, and logic. This performance is achieved through large-scale training on diverse data, advanced architecture design, and optimization for both accuracy and efficiency. The model demonstrates strong few-shot learning, chain-of-thought reasoning, and the ability to handle complex multi-step problems across technical and non-technical domains.","intents":["Solve complex math problems and reasoning tasks reliably","Generate correct code solutions for programming challenges","Answer knowledge-based questions across diverse domains accurately","Perform multi-step reasoning and problem decomposition"],"best_for":["developers building AI-powered coding assistants or tutoring systems","teams needing reliable reasoning capabilities for technical problem-solving","researchers evaluating LLM capabilities on standardized benchmarks","builders requiring a model with proven performance across diverse reasoning tasks"],"limitations":["Benchmark performance doesn't guarantee real-world accuracy for specialized domains (medical, legal, scientific)","Performance varies significantly by task type — some domains may underperform despite strong average benchmarks","Reasoning quality degrades with very long chains of thought (>10 steps) or ambiguous problem statements","No specialized fine-tuning for domain-specific reasoning (would require additional training)"],"requires":["Grok-2 API access or web interface","Clear problem statements and sufficient context for reasoning","Understanding that benchmark performance is average — individual queries may vary"],"input_types":["math problems","coding challenges","knowledge questions","logic puzzles","multi-step reasoning prompts"],"output_types":["step-by-step reasoning chains","correct code solutions","mathematical derivations","logical conclusions with justification"],"categories":["planning-reasoning","code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_5","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Grok-2 generates code across multiple programming languages (Python, JavaScript, Java, C++, etc.) and provides solutions to technical problems including debugging, refactoring, and algorithm design. The model understands code structure, syntax, and semantics, enabling it to generate syntactically correct and logically sound code that solves stated problems. Code generation is informed by the model's training on diverse codebases and its strong performance on HumanEval benchmarks, indicating reliable code quality for common programming tasks.","intents":["Generate code snippets or complete functions to solve specific problems","Debug existing code by analyzing errors and suggesting fixes","Refactor code for better readability, performance, or maintainability","Explain code logic and help understand how existing code works"],"best_for":["individual developers seeking code generation assistance","teams building code-generation-powered IDEs or development tools","junior developers learning programming concepts through generated examples","developers working across multiple languages needing quick syntax help"],"limitations":["Generated code may not follow project-specific conventions or patterns without explicit guidance","Code quality varies by language — better performance on popular languages (Python, JavaScript) vs niche languages","Generated code may lack error handling, edge case coverage, or production-ready robustness","No built-in integration with version control, testing frameworks, or CI/CD pipelines","Cannot access or understand project-specific context without explicit file/context provision"],"requires":["Grok-2 API access or web interface","Clear problem description or code context for generation","Manual review and testing of generated code before production use","Understanding of the target programming language and framework"],"input_types":["natural language problem descriptions","code snippets for debugging or refactoring","algorithm descriptions","error messages and stack traces"],"output_types":["complete code functions or scripts","code snippets and examples","debugging suggestions and fixes","refactored code with explanations","algorithm implementations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_6","uri":"capability://text.generation.language.knowledge.synthesis.across.diverse.domains","name":"knowledge synthesis across diverse domains","description":"Grok-2 synthesizes information across diverse knowledge domains (science, history, technology, culture, etc.) to provide comprehensive answers to broad questions. The model's training on diverse data sources enables it to connect concepts across disciplines, provide nuanced explanations, and contextualize information within broader frameworks. This capability is particularly valuable for exploratory queries where users need synthesis rather than retrieval of a single fact.","intents":["Get comprehensive explanations of complex topics spanning multiple domains","Understand how concepts in one field relate to or inform other fields","Explore historical context and evolution of ideas across time","Synthesize information from multiple perspectives on a topic"],"best_for":["students and educators seeking comprehensive topic explanations","researchers exploring interdisciplinary connections","content creators developing educational or explanatory content","professionals needing broad context for decision-making"],"limitations":["Knowledge is limited to training data cutoff (specific cutoff date not publicly disclosed)","Synthesis quality varies by domain — stronger in well-represented areas (tech, science) vs niche topics","Cannot distinguish between common knowledge and specialized expertise without explicit context","Potential for hallucination or conflation of concepts across domains if not carefully prompted","No real-time knowledge updates except through X integration for current events"],"requires":["Grok-2 API access or web interface","Clear topic or question for synthesis","Critical evaluation of synthesized information, especially for specialized domains"],"input_types":["broad topic questions","exploratory queries","requests for connections between concepts","historical or contextual questions"],"output_types":["comprehensive topic explanations","interdisciplinary connections and insights","historical context and evolution","nuanced perspectives on complex topics"],"categories":["text-generation-language","memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_7","uri":"capability://tool.use.integration.free.tier.api.access.with.no.authentication.friction","name":"free-tier api access with no authentication friction","description":"Grok-2 is offered free through xAI's platform, removing financial barriers to access and experimentation. The free tier provides access to the full model capabilities without requiring credit card information or paid subscription, lowering the barrier to entry for developers, students, and builders exploring the model. This is a business model decision that prioritizes adoption and user growth over immediate monetization, contrasting with competitors' freemium models that often limit free tier capabilities.","intents":["Experiment with Grok-2 capabilities without financial commitment","Build prototypes and MVPs using a capable LLM without upfront costs","Evaluate Grok-2 against competitors before committing to paid usage","Access AI capabilities for educational or personal projects with no cost"],"best_for":["individual developers and hobbyists with limited budgets","students and educators exploring AI capabilities","startups and small teams prototyping AI features","researchers comparing LLM capabilities across models"],"limitations":["Free tier may have rate limits or usage quotas (specific limits not publicly disclosed)","No guaranteed uptime or SLA for free tier (typical for free offerings)","Potential for service degradation during high-traffic periods","Free tier may be discontinued or limited in future versions","No dedicated support or priority access for free tier users"],"requires":["X account or ability to create one","Internet connection to access Grok-2 interface or API","No credit card or payment method required"],"input_types":["any input supported by Grok-2 (text, images, code, etc.)"],"output_types":["any output supported by Grok-2 (text, code, analysis, etc.)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_8","uri":"capability://memory.knowledge.contextual.awareness.of.current.events.and.trending.topics","name":"contextual awareness of current events and trending topics","description":"Through real-time X integration, Grok-2 maintains awareness of current events, trending topics, and real-time discourse, allowing it to ground responses in what's happening now rather than relying solely on training data. The model can reference recent news, viral discussions, and emerging trends when relevant to user queries, providing responses that feel current and informed. This is achieved through the real-time X data retrieval capability that feeds live information into the reasoning process during inference.","intents":["Get current information about breaking news or recent events","Understand what's trending and why topics are gaining attention","Analyze how current events are being discussed and perceived in real-time","Build applications that need to stay informed about current discourse"],"best_for":["news organizations and journalists needing AI-assisted current events analysis","social media managers tracking trends and discourse","product teams building trend-aware features","researchers studying real-time information dynamics"],"limitations":["Contextual awareness is limited to X platform — other social networks and news sources not integrated","Real-time data retrieval adds latency to responses (500ms-2s additional)","Trending topics are X-specific and may not represent broader public discourse","Historical context for trends is limited to X's API lookback window","Potential for bias toward X users' perspectives vs broader population"],"requires":["Active internet connection for real-time data retrieval","X API access (included with Grok-2 access)","Understanding that context is X-specific, not comprehensive"],"input_types":["queries about current events","questions about trending topics","requests for real-time discourse analysis"],"output_types":["current event summaries with real-time context","trend analysis and explanations","real-time sentiment and discourse analysis","citations of relevant tweets and discussions"],"categories":["memory-knowledge","search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__cap_9","uri":"capability://text.generation.language.multi.turn.conversation.management.with.context.retention","name":"multi-turn conversation management with context retention","description":"Grok-2 maintains coherent multi-turn conversations by retaining context across multiple exchanges, allowing users to build on previous statements, ask follow-up questions, and have natural back-and-forth dialogue. The model tracks conversation history, understands pronouns and references to earlier statements, and maintains consistency in reasoning and personality across turns. This is enabled by the 128K context window which allows full conversation history to be included in each forward pass, and by attention mechanisms that effectively weight recent and relevant context.","intents":["Have natural multi-turn conversations without repeating context","Ask follow-up questions that build on previous responses","Maintain consistent reasoning and personality across conversation","Refine and iterate on ideas through dialogue"],"best_for":["users preferring conversational interaction over single-query-response","teams building conversational AI applications and chatbots","developers iteratively refining code or solutions through dialogue","content creators collaborating with AI through extended conversations"],"limitations":["Context window fills up with very long conversations (128K tokens supports ~50-100 turns depending on response length)","Conversation quality may degrade if context becomes too long or unfocused","No persistent conversation storage — context is lost when session ends (unless explicitly saved)","Model may lose track of very early conversation context even within 128K window if later context is dense"],"requires":["Grok-2 API access or web interface","Maintaining conversation session (API clients must manage conversation history)","Understanding of token counting to avoid exceeding context limits"],"input_types":["initial query","follow-up questions","clarifications and refinements","requests to build on previous responses"],"output_types":["contextually-aware responses","follow-up answers referencing previous context","refined solutions based on dialogue","consistent personality and reasoning across turns"],"categories":["text-generation-language","memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"grok-2__headline","uri":"capability://llm.apis.real.time.conversational.ai.model.with.social.media.integration","name":"real-time conversational ai model with social media integration","description":"Grok-2 is a cutting-edge conversational AI model that leverages real-time data from the X (Twitter) platform, providing users with current events and trends while maintaining a unique personality that blends helpfulness with wit.","intents":["best conversational AI model","conversational AI for real-time information","AI model with social media integration","top LLM for current events analysis","AI with vision capabilities for image understanding"],"best_for":["real-time data retrieval","social discourse analysis"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["llm-apis","rag-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Active internet connection for X API calls","X API access (free tier or paid tier depending on usage volume)","Grok-2 API key or web interface access","Understanding of X API rate limits and authentication requirements","Grok-2 API access or web interface","Sufficient API quota for large token counts","Client-side token counting to avoid exceeding limits","Understanding of token economics for cost estimation","Clear, well-structured instructions","Explicit specification of requirements and constraints"],"failure_modes":["Requires active X API access and rate limits apply (standard X API tier limits of 300-450 requests per 15 minutes)","Real-time data retrieval adds latency to response generation (estimated 500ms-2s additional per query)","Dependent on X platform availability and API stability","Cannot access private/protected tweets or accounts without appropriate authentication","Historical data retrieval limited to X API's standard lookback window (typically 7 days for standard endpoints)","Latency increases with context length (estimated 2-5x slower for 128K tokens vs 4K tokens)","API costs scale with token usage (both input and output tokens counted)","Attention mechanism efficiency degrades at extreme lengths despite optimizations","Not all use cases benefit from 128K context — many tasks plateau in quality at 16K-32K tokens","Instruction-following quality degrades with very long or ambiguous instructions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"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":"2026-05-24T12:16:22.066Z","last_scraped_at":null,"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=grok-2","compare_url":"https://unfragile.ai/compare?artifact=grok-2"}},"signature":"F4QSnreVdHqm6Doznz35O5gZeB5rMpxgxrqs0QfafQB2s3VLTS4QnSLA+O114M5e9JIYISjcUQLhGPIAeqj8AA==","signedAt":"2026-06-22T10:43:25.638Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/grok-2","artifact":"https://unfragile.ai/grok-2","verify":"https://unfragile.ai/api/v1/verify?slug=grok-2","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"}}