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The model applies attention mechanisms across both modalities, enabling it to reason about image content, answer questions about visual elements, and generate text responses grounded in visual context. Vision inputs are converted to image tokens through a learned visual encoder before being fed into the main language model backbone.","intents":["I need to analyze screenshots, diagrams, or photos and get detailed explanations of what's in them","I want to ask questions about images and get contextual answers that reference specific visual elements","I need to extract text from images (OCR) and reason about the extracted content","I want to build an AI assistant that can understand both user text queries and attached images in the same request"],"best_for":["developers building document analysis tools","teams creating accessibility features for visual content","builders of multimodal chatbots and assistants","researchers prototyping vision-language applications"],"limitations":["Image resolution and aspect ratio constraints limit detail extraction from very high-resolution or unusual aspect ratio images","Vision processing adds ~500-800ms latency compared to text-only requests","Cannot process video or animated content — only static images","Image token budget is shared with text tokens, reducing available context for very large image inputs"],"requires":["OpenAI API key or OpenRouter API key","Images in JPEG, PNG, GIF, or WebP format","HTTP client library (Python requests, Node.js fetch, etc.)","Base64 encoding capability for image transmission"],"input_types":["text (natural language queries)","image (JPEG, PNG, GIF, WebP)","mixed text + image in single request"],"output_types":["text (natural language responses)","structured text (markdown, JSON if requested)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-1106-preview__cap_1","uri":"capability://data.processing.analysis.structured.json.output.generation.with.schema.validation","name":"structured json output generation with schema validation","description":"Constrains model output to valid JSON matching a developer-provided schema, using a decoding-time constraint mechanism that prevents invalid JSON generation at the token level. The model's output is validated against the schema before being returned, ensuring type correctness, required field presence, and enum constraints. This works by modifying the sampling distribution at each token position to only allow tokens that keep the output valid JSON.","intents":["I need to extract structured data from unstructured text and guarantee the output is valid JSON","I want to build a data pipeline where the model output directly feeds into downstream JSON parsers without error handling","I need to ensure API responses conform to a specific schema for client-side type safety","I want to reduce parsing errors and validation logic in my application by having the model enforce schema compliance"],"best_for":["backend developers building data extraction pipelines","teams implementing type-safe LLM integrations","builders of structured data generation workflows","developers reducing post-processing validation overhead"],"limitations":["Schema complexity adds 10-15% latency overhead due to constraint checking at each token","Nested objects with deep nesting (>5 levels) may reduce output quality as model optimizes for schema compliance","Cannot enforce semantic constraints (e.g., 'email must be valid format') — only structural JSON schema constraints","Schema must be provided in JSON Schema format; other schema languages require conversion"],"requires":["OpenAI API key or OpenRouter API key","JSON Schema definition for output structure","API client supporting response_format parameter (Python 1.0+, Node.js 4.0+)","Understanding of JSON Schema specification"],"input_types":["text (natural language prompt)","JSON Schema (schema definition)"],"output_types":["JSON (valid, schema-conformant structured data)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-1106-preview__cap_2","uri":"capability://tool.use.integration.function.calling.with.multi.tool.orchestration","name":"function calling with multi-tool orchestration","description":"Accepts a list of tool/function definitions with parameters, and the model learns to emit structured function calls in response to user queries. The model outputs function names and arguments as JSON, which the developer's application then executes and feeds back to the model for continued reasoning. This enables agentic workflows where the model decides which tools to invoke, in what order, and how to interpret results. The model is trained to understand function signatures, parameter types, and return values.","intents":["I want to build an AI agent that can call APIs, databases, or local functions to answer user questions","I need the model to decide which of multiple available tools to use based on the user's intent","I want to create a multi-step workflow where the model calls a tool, sees the result, and decides on the next action","I need to integrate the model with existing backend services without writing custom orchestration logic"],"best_for":["developers building AI agents and autonomous systems","teams creating chatbots with backend integration","builders of LLM-powered automation workflows","engineers implementing tool-use patterns in production systems"],"limitations":["Model may hallucinate function calls that don't exist or misunderstand parameter types if tool definitions are ambiguous","No built-in error recovery — if a function call fails, the developer must handle retry logic and feed errors back to the model","Function call latency is additive: model inference + function execution + model inference again for interpretation","Limited to ~128 concurrent tool definitions before performance degrades; requires tool selection/filtering for larger tool sets"],"requires":["OpenAI API key or OpenRouter API key","Tool definitions in OpenAI function calling format (name, description, parameters as JSON Schema)","Application code to execute functions and return results to the model","HTTP client capable of streaming or polling for function call responses"],"input_types":["text (user query)","tool definitions (JSON with function signatures)","function execution results (text, JSON, or structured data)"],"output_types":["function calls (JSON with function name and arguments)","text (natural language responses after tool execution)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-1106-preview__cap_3","uri":"capability://text.generation.language.extended.context.window.reasoning.with.128k.token.capacity","name":"extended context window reasoning with 128k token capacity","description":"Processes input sequences up to 128,000 tokens (approximately 96,000 words or 400+ pages of text) in a single request, enabling the model to maintain coherent reasoning across very long documents, codebases, or conversation histories. The model uses a modified attention mechanism (likely sparse or hierarchical attention) to handle the extended context efficiently without quadratic memory scaling. This allows developers to pass entire books, code repositories, or long conversation threads without truncation.","intents":["I need to analyze a full codebase (thousands of files) and get refactoring suggestions that consider the entire architecture","I want to summarize a 100+ page document while maintaining consistency and cross-referencing between sections","I need to maintain a long conversation history (100+ turns) without losing context from earlier messages","I want to build a code review tool that understands the full context of a large pull request with multiple files"],"best_for":["developers working with large codebases and needing architectural understanding","teams analyzing long documents or research papers","builders of long-running conversational agents","engineers implementing code analysis and refactoring tools"],"limitations":["Latency increases with context length — 128K token requests take 3-5x longer than 4K token requests","Cost scales linearly with input tokens; 128K token requests are 32x more expensive than 4K requests","Model attention may dilute across very long contexts, reducing quality of reasoning about specific details in the middle of the context","Requires careful prompt engineering to ensure the model focuses on relevant sections rather than averaging across all context"],"requires":["OpenAI API key or OpenRouter API key","HTTP client with support for large request bodies (typically 10-20MB)","Token counting library to stay within 128K limit","Sufficient API rate limits and quota for large token volumes"],"input_types":["text (up to 128,000 tokens)","code (full files or repositories)","documents (markdown, plain text, or extracted text from PDFs)"],"output_types":["text (analysis, summaries, suggestions)","code (refactored code, explanations)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-1106-preview__cap_4","uri":"capability://text.generation.language.instruction.following.with.few.shot.and.zero.shot.prompting","name":"instruction-following with few-shot and zero-shot prompting","description":"Interprets natural language instructions and system prompts to adapt behavior without fine-tuning, using in-context learning to understand task specifications from examples (few-shot) or descriptions (zero-shot). The model's training includes extensive instruction-following data, enabling it to understand complex, multi-step tasks described in plain English and execute them consistently. This works through the model's learned ability to parse instructions, extract intent, and apply that intent to new inputs.","intents":["I want to give the model a detailed instruction about tone, format, or style and have it follow that instruction consistently","I need to show the model 2-3 examples of a task and have it generalize to new inputs without fine-tuning","I want to build a system prompt that defines the model's role and have it stay in character throughout a conversation","I need the model to follow specific output formatting rules (e.g., 'respond in JSON', 'use markdown', 'limit to 100 words')"],"best_for":["developers building customizable AI assistants","teams prototyping new use cases without fine-tuning infrastructure","builders of prompt-based applications","non-technical users creating AI workflows through prompt engineering"],"limitations":["Instruction-following quality degrades with very complex or contradictory instructions; model may prioritize earlier instructions over later ones","Few-shot learning requires careful example selection — poor examples can degrade performance more than zero-shot","No guarantee of instruction adherence — model may ignore or misinterpret instructions, especially if they conflict with training data","System prompts are not truly 'locked in' — user input can override system instructions if phrased persuasively"],"requires":["OpenAI API key or OpenRouter API key","Well-written natural language instructions","Understanding of prompt engineering best practices","Examples (for few-shot) or detailed descriptions (for zero-shot)"],"input_types":["text (system prompt)","text (user query)","text (examples for few-shot learning)"],"output_types":["text (response following specified instructions)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-1106-preview__cap_5","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"Generates syntactically correct code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) based on natural language descriptions, comments, or partial code. The model understands language-specific idioms, standard libraries, and best practices for each language. Code generation works through transformer-based sequence-to-sequence prediction, where the model learns patterns from billions of tokens of code in its training data and predicts the most likely next tokens that form valid code.","intents":["I want to describe a function in English and have the model write the implementation in my target language","I need to complete a partially-written function or fill in boilerplate code","I want to generate unit tests for existing code","I need to translate code from one language to another while maintaining functionality"],"best_for":["developers accelerating coding tasks","teams building code generation tools","educators teaching programming concepts","developers working across multiple languages"],"limitations":["Generated code may contain logical errors or security vulnerabilities — requires human review before production use","Performance is poor for very domain-specific languages or niche frameworks not well-represented in training data","Code generation quality degrades for complex algorithms requiring deep algorithmic knowledge (e.g., graph algorithms, dynamic programming)","No awareness of project-specific conventions or internal libraries — generated code may not follow team standards"],"requires":["OpenAI API key or OpenRouter API key","Clear description of desired code or partial code to complete","Target programming language specified in prompt","Code review process before deploying generated code"],"input_types":["text (natural language description of desired code)","code (partial code to complete)","comments (docstrings or inline comments describing intent)"],"output_types":["code (generated or completed code in target language)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-1106-preview__cap_6","uri":"capability://text.generation.language.knowledge.cutoff.aware.reasoning.with.temporal.grounding","name":"knowledge cutoff-aware reasoning with temporal grounding","description":"Explicitly acknowledges its training data cutoff (April 2023) and can reason about what information it may not have access to, enabling developers to build systems that know when to query external data sources. The model understands temporal references in queries and can indicate uncertainty about recent events or developments. This is implemented through training data that includes explicit temporal markers and examples of the model declining to answer about post-cutoff events.","intents":["I want the model to tell me when it doesn't have information about recent events or developments","I need to build a system that knows when to fall back to web search or a knowledge base for current information","I want to ask about historical events and have the model indicate whether it has training data about them","I need to understand the limitations of the model's knowledge for compliance or accuracy reasons"],"best_for":["developers building hybrid systems combining LLMs with knowledge bases","teams building search-augmented applications","builders of fact-checking or verification tools","organizations requiring transparency about model limitations"],"limitations":["Model may still hallucinate about post-cutoff events if the query is phrased ambiguously","Knowledge cutoff awareness is probabilistic — model may incorrectly claim knowledge of post-cutoff events or vice versa","Temporal reasoning is imperfect; model may confuse dates or incorrectly estimate when events occurred relative to cutoff","No automatic integration with external knowledge sources — developers must implement fallback logic"],"requires":["OpenAI API key or OpenRouter API key","Prompts that explicitly ask about knowledge cutoff or temporal information","Optional: external knowledge source or web search API for fallback queries"],"input_types":["text (query with temporal references)"],"output_types":["text (response with explicit uncertainty about post-cutoff information)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-1106-preview__cap_7","uri":"capability://text.generation.language.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Solves mathematical problems including algebra, calculus, geometry, and logic through step-by-step reasoning, using chain-of-thought patterns learned during training. The model can work through multi-step problems, show intermediate steps, and explain reasoning. This works by training the model on mathematical problem-solving datasets and using reinforcement learning to reward correct final answers and clear reasoning paths. The model learns to recognize mathematical patterns and apply appropriate solution strategies.","intents":["I want the model to solve math problems and show its work step-by-step","I need to verify mathematical reasoning or check homework","I want to generate math problems with solutions for educational purposes","I need the model to explain mathematical concepts and their applications"],"best_for":["educators building tutoring systems","developers creating educational tools","teams building homework help applications","researchers exploring mathematical reasoning in LLMs"],"limitations":["Performance degrades on very complex proofs or problems requiring deep mathematical insight beyond pattern matching","Model may make arithmetic errors in long calculations — not suitable as a replacement for symbolic math engines","Struggles with novel problem types not well-represented in training data","Cannot perform symbolic computation (e.g., simplifying complex algebraic expressions) as reliably as dedicated math software"],"requires":["OpenAI API key or OpenRouter API key","Mathematical problem statement in text or LaTeX format","Optional: symbolic math library (SymPy, Mathematica) for verification"],"input_types":["text (mathematical problem description)","LaTeX (mathematical notation)"],"output_types":["text (step-by-step solution)","LaTeX (mathematical notation in response)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key or OpenRouter API key","Images in JPEG, PNG, GIF, or WebP format","HTTP client library (Python requests, Node.js fetch, etc.)","Base64 encoding capability for image transmission","JSON Schema definition for output structure","API client supporting response_format parameter (Python 1.0+, Node.js 4.0+)","Understanding of JSON Schema specification","Tool definitions in OpenAI function calling format (name, description, parameters as JSON Schema)","Application code to execute functions and return results to the model","HTTP client capable of streaming or polling for function call responses"],"failure_modes":["Image resolution and aspect ratio constraints limit detail extraction from very high-resolution or unusual aspect ratio images","Vision processing adds ~500-800ms latency compared to text-only requests","Cannot process video or animated content — only static images","Image token budget is shared with text tokens, reducing available context for very large image inputs","Schema complexity adds 10-15% latency overhead due to constraint checking at each token","Nested objects with deep nesting (>5 levels) may reduce output quality as model optimizes for schema compliance","Cannot enforce semantic constraints (e.g., 'email must be valid format') — only structural JSON schema constraints","Schema must be provided in JSON Schema format; other schema languages require conversion","Model may hallucinate function calls that don't exist or misunderstand parameter types if tool definitions are ambiguous","No built-in error recovery — if a function call fails, the developer must handle retry logic and feed errors back to the model","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.777Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openai-gpt-4-1106-preview","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4-1106-preview"}},"signature":"KRAIH6rYTmZw7YX9TBn9cpzAxpkc0L+yyUWrDK5ul6Clk5OlIGzIfBXaa7lGTxctWRRL5GKtwgoFjRYUc2jDDA==","signedAt":"2026-06-20T03:45:53.304Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4-1106-preview","artifact":"https://unfragile.ai/openai-gpt-4-1106-preview","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4-1106-preview","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"}}