{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-4o-2024-11-20","slug":"openai-gpt-4o-2024-11-20","name":"OpenAI: GPT-4o (2024-11-20)","type":"model","url":"https://openrouter.ai/models/openai~gpt-4o-2024-11-20","page_url":"https://unfragile.ai/openai-gpt-4o-2024-11-20","categories":["text-writing"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.50e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-4o-2024-11-20__cap_0","uri":"capability://text.generation.language.multimodal.text.to.text.generation.with.enhanced.creative.writing","name":"multimodal text-to-text generation with enhanced creative writing","description":"Generates natural language text across diverse domains using a transformer-based architecture trained on diverse internet text and proprietary datasets. The 2024-11-20 version incorporates improved instruction-following and creative writing patterns through reinforcement learning from human feedback (RLHF), enabling more contextually relevant and engaging prose with better adherence to stylistic constraints and tone requirements.","intents":["Generate creative fiction, poetry, or marketing copy with natural voice and emotional resonance","Produce technical documentation, explanations, or educational content with clarity and accuracy","Refactor or improve existing text for readability, tone, or audience appropriateness","Brainstorm ideas, outlines, or narrative structures for writing projects"],"best_for":["Content creators and writers seeking AI-assisted composition with nuanced tone control","Product teams building chat interfaces or conversational AI products","Developers integrating LLM capabilities into applications via REST API"],"limitations":["Context window of 128,000 tokens limits handling of extremely long documents or multi-document synthesis","No real-time streaming of token generation in all API configurations — batch processing may introduce latency","Output quality degrades on highly specialized domain knowledge requiring recent training data updates","No built-in fact verification — requires external validation for claims about current events or statistics"],"requires":["OpenAI API key with GPT-4o access enabled","HTTP client capable of REST API calls (curl, Python requests, Node.js fetch, etc.)","Network connectivity to OpenAI's inference endpoints","Billing account with sufficient credits or active subscription"],"input_types":["plain text prompts","structured JSON with system/user/assistant message roles","markdown-formatted text with embedded code blocks"],"output_types":["plain text","markdown-formatted text","JSON-structured responses with tool_calls for function invocation"],"categories":["text-generation-language","creative-writing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-11-20__cap_1","uri":"capability://image.visual.vision.language.understanding.with.document.and.image.analysis","name":"vision-language understanding with document and image analysis","description":"Processes images and documents as input through a vision encoder that extracts spatial and semantic features, integrating them with the text transformer backbone to enable joint reasoning over visual and textual content. Supports multiple image formats and can analyze charts, diagrams, screenshots, and photographs with understanding of layout, text within images (OCR), and visual relationships.","intents":["Extract structured data from screenshots, forms, or documents without manual transcription","Analyze charts, graphs, or infographics to interpret trends and relationships","Describe images, identify objects, and answer questions about visual content","Validate or correct OCR output from document scanning systems"],"best_for":["Document processing teams automating data extraction from PDFs, scans, and forms","Data analysts building pipelines to interpret visual analytics and reports","Accessibility teams generating alt-text or descriptions for images at scale"],"limitations":["Image resolution limits — very high-resolution images (>20 megapixels) may be downsampled, losing fine detail","OCR accuracy degrades on handwritten text, non-Latin scripts, or heavily stylized fonts","No video frame analysis — must process individual frames separately, losing temporal context","Latency increases with image complexity; multi-image requests add ~50-100ms per additional image"],"requires":["OpenAI API key with vision capability enabled","Images in supported formats: JPEG, PNG, GIF, WebP (max 20MB per image)","Base64 encoding or publicly accessible image URLs for API transmission","HTTP/2 capable client for efficient multi-image handling"],"input_types":["JPEG, PNG, GIF, WebP images","base64-encoded image data","image URLs (must be publicly accessible)","mixed text and image prompts in single request"],"output_types":["plain text descriptions and analysis","structured JSON with extracted data fields","markdown with embedded code or tables"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-11-20__cap_2","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.invocation","name":"function calling with schema-based tool invocation","description":"Enables the model to request execution of external functions by generating structured JSON payloads conforming to developer-defined schemas. The model learns to map natural language requests to appropriate function calls through training on function definitions, parameter types, and usage examples, supporting parallel function calls and error recovery through multi-turn conversations.","intents":["Build AI agents that can invoke APIs, databases, or custom business logic based on user requests","Create conversational interfaces that execute actions (scheduling, payments, data queries) without manual routing","Implement multi-step workflows where the model decides which tools to call and in what sequence","Enable structured data extraction by mapping model outputs to predefined function schemas"],"best_for":["AI agent developers building autonomous systems with tool orchestration","Teams implementing conversational APIs that need to execute backend operations","Developers building no-code automation platforms with LLM-driven decision logic"],"limitations":["Schema complexity limits — deeply nested or circular schemas may confuse the model's function selection","No built-in error handling — failed function calls require explicit error messages in follow-up turns to recover","Parallel function calls are generated but must be executed sequentially by the client (no native async orchestration)","Function selection accuracy degrades when multiple functions have overlapping purposes or similar parameter names"],"requires":["OpenAI API key with function calling enabled","JSON Schema definitions for each tool (OpenAPI 3.0 compatible format)","Client-side function registry mapping function names to actual implementations","Ability to handle multi-turn conversations and parse tool_calls from response objects"],"input_types":["JSON Schema function definitions","natural language requests describing desired actions","tool execution results (for error recovery and multi-step workflows)"],"output_types":["tool_calls array with function name, arguments, and call ID","text responses explaining reasoning or next steps","structured JSON with extracted parameters"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-11-20__cap_3","uri":"capability://text.generation.language.instruction.following.with.system.prompt.customization","name":"instruction-following with system prompt customization","description":"Accepts system-level instructions that define the model's behavior, tone, constraints, and role within a conversation. The system prompt is processed separately from user messages through a specialized attention mechanism that weights system instructions more heavily during token generation, enabling consistent personality and behavioral constraints across multi-turn conversations.","intents":["Create specialized AI personas (customer service agent, technical expert, creative writing assistant) with consistent behavior","Enforce safety constraints or domain-specific rules (e.g., 'never provide medical advice', 'respond only in JSON')","Control output format and structure through declarative instructions rather than prompt engineering","Build multi-tenant applications where each user or organization has custom system instructions"],"best_for":["SaaS platforms building white-label AI features with customizable behavior per customer","Teams implementing specialized chatbots with consistent brand voice and operational constraints","Developers building AI applications requiring strict output formatting or safety guardrails"],"limitations":["System prompt conflicts with user requests — model may prioritize user intent over system constraints in ambiguous cases","Extremely long system prompts (>10,000 tokens) reduce effective context window for user messages","System prompt changes require new API calls — no in-conversation system prompt updates without breaking conversation state","Instruction clarity matters — vague or contradictory system prompts degrade instruction-following reliability"],"requires":["OpenAI API key","Well-crafted system prompt (typically 50-500 tokens for optimal performance)","Understanding of prompt injection risks when system prompts are user-configurable"],"input_types":["plain text system prompts","markdown-formatted instructions with examples","structured role definitions (e.g., 'You are a Python expert')"],"output_types":["text responses adhering to system constraints","structured output (JSON, markdown, code) as specified in system prompt","responses with enforced tone or style"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-11-20__cap_4","uri":"capability://automation.workflow.batch.processing.for.cost.optimized.inference","name":"batch processing for cost-optimized inference","description":"Accepts multiple requests bundled into a single batch file (JSONL format) and processes them asynchronously with lower per-token pricing (50% discount vs. real-time API). Requests are queued and processed during off-peak hours, with results returned via webhook or polling, enabling cost-effective processing of non-time-sensitive workloads at scale.","intents":["Process thousands of documents or records through the model at significantly reduced cost","Generate training data, synthetic examples, or augmented datasets for machine learning pipelines","Perform bulk content moderation, classification, or tagging across large corpora","Run nightly or scheduled analysis jobs without real-time latency requirements"],"best_for":["Data teams processing large datasets with flexible latency requirements","ML engineers generating synthetic training data at scale with cost constraints","Content platforms performing bulk moderation or categorization"],"limitations":["Latency is unpredictable — requests may take hours to days to complete depending on queue load","No real-time feedback — cannot adjust requests mid-batch or cancel individual items","Minimum batch size requirements may not be cost-effective for small workloads (<100 requests)","Requires JSONL formatting and proper error handling for malformed requests in the batch"],"requires":["OpenAI API key with batch processing enabled","Requests formatted as JSONL (JSON Lines) with proper message structure","Webhook endpoint or polling mechanism to retrieve results","Ability to handle asynchronous result delivery and potential request failures"],"input_types":["JSONL file with multiple API requests","each line containing a complete chat completion request (messages, model, parameters)"],"output_types":["JSONL file with results matching input request order","each line containing the model's response with request ID for correlation"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-11-20__cap_5","uri":"capability://memory.knowledge.context.window.management.with.128k.token.capacity","name":"context window management with 128k token capacity","description":"Maintains a 128,000-token context window that can accommodate approximately 100,000 words of conversation history, documents, or code. The model uses sliding-window attention patterns and efficient tokenization to process long contexts without quadratic memory growth, enabling analysis of entire codebases, long documents, or extended multi-turn conversations within a single request.","intents":["Analyze entire source code files or small codebases for refactoring, security issues, or optimization opportunities","Process long documents (research papers, books, legal contracts) and answer questions about specific sections","Maintain extended conversation history without losing context or requiring summarization","Compare multiple documents or code files to identify patterns, inconsistencies, or relationships"],"best_for":["Software developers working with large codebases or performing comprehensive code review","Researchers and analysts processing long-form documents and extracting insights","Teams building conversational AI with extended interaction histories"],"limitations":["Token counting is approximate — actual token usage may vary by 5-10% depending on tokenization edge cases","Latency increases linearly with context size — 128K token requests take ~2-3x longer than 10K token requests","Attention patterns over very long contexts may dilute focus on specific sections (needle-in-haystack problem)","Cost scales with context size — longer contexts consume more tokens and increase API costs proportionally"],"requires":["OpenAI API key with GPT-4o access","Proper token counting using OpenAI's tokenizer (tiktoken library) to avoid exceeding limits","Understanding of token consumption: system prompt + input + output all count against the 128K limit"],"input_types":["plain text documents","source code files","markdown-formatted content","conversation history with multiple turns"],"output_types":["text analysis and insights","code suggestions or refactoring recommendations","structured summaries or extracted information"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-11-20__cap_6","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"Constrains model output to conform to developer-provided JSON schemas, ensuring responses are valid JSON matching specified field types, required properties, and nested structures. The model generates tokens that are guaranteed to produce valid JSON without post-processing, using constrained decoding that prunes invalid token sequences during generation.","intents":["Extract structured data from unstructured text with guaranteed valid JSON output","Generate API responses or database records with schema compliance","Build data pipelines where model outputs feed directly into downstream systems without validation","Create form-filling or data collection workflows with type-safe outputs"],"best_for":["Data engineering teams building ETL pipelines with LLM-powered extraction","API developers generating structured responses that must conform to OpenAPI schemas","Teams building no-code automation where model outputs must be type-safe"],"limitations":["Schema complexity limits — very deeply nested or circular schemas may cause generation failures","Constrained decoding adds ~10-20% latency overhead compared to unconstrained generation","Schema must be JSON Schema compatible — some advanced schema features may not be supported","Model may struggle to fill all required fields if the input doesn't contain sufficient information"],"requires":["OpenAI API key with structured output support enabled","JSON Schema definition for the desired output structure (JSON Schema Draft 2020-12 compatible)","Understanding that the schema constrains what the model can output, not what it understands"],"input_types":["JSON Schema definitions","unstructured text or documents to extract from","natural language requests for data extraction"],"output_types":["valid JSON conforming to provided schema","guaranteed to parse without errors","all required fields populated (or null if unavailable)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-11-20__cap_7","uri":"capability://planning.reasoning.reasoning.focused.inference.with.extended.thinking","name":"reasoning-focused inference with extended thinking","description":"Allocates additional computational resources to internal reasoning steps before generating final responses, using a chain-of-thought pattern that explores multiple solution paths and validates reasoning before committing to an answer. This mode trades latency for accuracy on complex reasoning tasks by enabling the model to 'think through' problems more thoroughly.","intents":["Solve complex math problems, logic puzzles, or multi-step reasoning tasks with higher accuracy","Debug code by exploring multiple potential issues and validating hypotheses","Analyze complex scenarios with multiple variables and interdependencies","Generate well-reasoned explanations that show working and justify conclusions"],"best_for":["Teams solving complex technical problems requiring rigorous reasoning","Educational applications where showing reasoning steps is valuable","Research or analysis tasks where accuracy is more important than speed"],"limitations":["Significantly higher latency — reasoning mode requests may take 10-30 seconds vs. 1-2 seconds for standard mode","Higher token consumption — reasoning steps consume tokens that count against usage limits","Not beneficial for simple tasks — overhead of reasoning mode wastes resources on straightforward requests","Reasoning process is opaque — internal thinking steps are not exposed to the user, only final answer"],"requires":["OpenAI API key with extended thinking capability enabled","Acceptance of higher latency and token costs for improved accuracy","Tasks that genuinely benefit from deeper reasoning (not simple retrieval or generation)"],"input_types":["complex math problems","logic puzzles or reasoning tasks","code debugging requests","multi-step analysis prompts"],"output_types":["detailed explanations with reasoning shown","step-by-step solutions","validated conclusions with justification"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with GPT-4o access enabled","HTTP client capable of REST API calls (curl, Python requests, Node.js fetch, etc.)","Network connectivity to OpenAI's inference endpoints","Billing account with sufficient credits or active subscription","OpenAI API key with vision capability enabled","Images in supported formats: JPEG, PNG, GIF, WebP (max 20MB per image)","Base64 encoding or publicly accessible image URLs for API transmission","HTTP/2 capable client for efficient multi-image handling","OpenAI API key with function calling enabled","JSON Schema definitions for each tool (OpenAPI 3.0 compatible format)"],"failure_modes":["Context window of 128,000 tokens limits handling of extremely long documents or multi-document synthesis","No real-time streaming of token generation in all API configurations — batch processing may introduce latency","Output quality degrades on highly specialized domain knowledge requiring recent training data updates","No built-in fact verification — requires external validation for claims about current events or statistics","Image resolution limits — very high-resolution images (>20 megapixels) may be downsampled, losing fine detail","OCR accuracy degrades on handwritten text, non-Latin scripts, or heavily stylized fonts","No video frame analysis — must process individual frames separately, losing temporal context","Latency increases with image complexity; multi-image requests add ~50-100ms per additional image","Schema complexity limits — deeply nested or circular schemas may confuse the model's function selection","No built-in error handling — failed function calls require explicit error messages in follow-up turns to recover","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"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":"2026-05-24T12:16:24.485Z","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=openai-gpt-4o-2024-11-20","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4o-2024-11-20"}},"signature":"+NBQ69058wuv5oR7wVmK68SyaE42PRxM5Lqr1JIg2iXtP4zkHnQFsZhHAqFcsLbvCz+O8HpgIJ6twxUeSc/LBA==","signedAt":"2026-06-21T02:00:49.380Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4o-2024-11-20","artifact":"https://unfragile.ai/openai-gpt-4o-2024-11-20","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4o-2024-11-20","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"}}