{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-4o-2024-05-13","slug":"openai-gpt-4o-2024-05-13","name":"OpenAI: GPT-4o (2024-05-13)","type":"model","url":"https://openrouter.ai/models/openai~gpt-4o-2024-05-13","page_url":"https://unfragile.ai/openai-gpt-4o-2024-05-13","categories":["image-generation"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_0","uri":"capability://image.visual.multimodal.text.and.image.understanding.with.unified.transformer.architecture","name":"multimodal text and image understanding with unified transformer architecture","description":"GPT-4o processes both text and image inputs through a single unified transformer backbone trained on interleaved text-image data, enabling native cross-modal reasoning without separate vision encoders or modality-specific branches. The model uses vision tokens that integrate seamlessly into the standard token stream, allowing the same attention mechanisms to reason across both modalities simultaneously. This architecture enables the model to understand spatial relationships, text within images, charts, diagrams, and visual context with the same semantic depth as pure language understanding.","intents":["I need to analyze screenshots, diagrams, and charts alongside natural language questions in a single API call","I want to extract structured data from documents that contain both text and visual elements without preprocessing","I need to understand context from images (e.g., UI layouts, code screenshots, design mockups) to provide relevant code or text suggestions","I want to perform visual reasoning tasks like comparing images, identifying objects, or reading handwritten text without separate vision APIs"],"best_for":["developers building document processing pipelines that mix text and visual content","teams creating accessibility tools that need to understand and describe images","builders of multimodal RAG systems requiring unified semantic understanding","product teams automating visual QA or design review workflows"],"limitations":["Image inputs must be base64-encoded or provided via URL; no direct file streaming support","Maximum image resolution and token budget constraints limit analysis of very high-resolution or multi-page documents","Vision understanding is optimized for natural images and documents; synthetic or heavily stylized visuals may have degraded performance","No video input support — only static images; temporal reasoning across frames requires frame-by-frame processing"],"requires":["OpenAI API key with GPT-4o access enabled","HTTP client capable of multipart form data or JSON with base64 encoding","Images in JPEG, PNG, GIF, or WebP format","Token budget awareness: vision tokens consume ~85 tokens per 512x512 image tile"],"input_types":["text (UTF-8, any language)","image (JPEG, PNG, GIF, WebP, base64-encoded or URL-referenced)","mixed sequences of text and images in conversation history"],"output_types":["text (UTF-8, any language)","structured JSON when prompted","code snippets with language tags"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_1","uri":"capability://text.generation.language.real.time.text.generation.with.streaming.token.output","name":"real-time text generation with streaming token output","description":"GPT-4o generates text token-by-token with server-sent events (SSE) streaming, allowing clients to receive and display partial responses before generation completes. The streaming implementation uses OpenAI's standard streaming protocol where each token is emitted as a separate JSON event, enabling low-latency user feedback and progressive rendering in applications. The model maintains full context awareness across streamed tokens, ensuring coherent multi-paragraph outputs without degradation from incremental generation.","intents":["I want to display text responses to users in real-time as they're generated, not wait for the full response","I need to build chat interfaces where users see the model 'thinking' token-by-token for better UX","I want to reduce perceived latency in conversational AI by streaming early tokens while later tokens are still being computed","I need to implement cancellation — allow users to stop generation mid-stream without wasting compute"],"best_for":["frontend developers building chat UIs and conversational interfaces","teams building real-time content generation tools (writing assistants, code generators)","developers optimizing for perceived latency in user-facing applications","builders of terminal-based or CLI tools requiring progressive output"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream after partial content is sent","Token-level streaming prevents certain post-processing optimizations (e.g., deduplication, filtering) that require full output visibility","Streaming responses cannot be retried at the token level; partial streams must be discarded and regenerated","Client-side buffering and rendering of high-frequency token streams can cause UI jank on low-end devices"],"requires":["OpenAI API key with streaming enabled","HTTP client with streaming/chunked transfer encoding support (e.g., fetch with ReadableStream, httpx with stream=True)","Server-sent events (SSE) parser or equivalent streaming JSON parser","Handling of connection timeouts and reconnection logic for long-running streams"],"input_types":["text (UTF-8, conversation history with role/content pairs)","images (in multimodal requests with streaming)"],"output_types":["streaming JSON events (one per token, with delta content field)","final completion metadata (stop reason, token counts)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_10","uri":"capability://text.generation.language.system.prompt.injection.and.role.based.behavior.customization","name":"system prompt injection and role-based behavior customization","description":"GPT-4o accepts a 'system' message that defines the model's behavior, role, tone, and constraints for the entire conversation. The system prompt is processed before user messages and influences all subsequent responses, enabling developers to customize the model's personality, expertise level, output format, and safety guardrails. System prompts can define specific roles (e.g., 'You are a Python expert'), output formats (e.g., 'Always respond in JSON'), or behavioral constraints (e.g., 'Do not provide medical advice').","intents":["I want to create specialized AI assistants with specific expertise or personality (e.g., a code reviewer, a creative writer, a customer support agent)","I need to enforce output format constraints (e.g., 'Always respond in JSON', 'Use markdown formatting')","I want to define safety guardrails or behavioral constraints (e.g., 'Do not engage with requests for illegal content')","I need to adapt the model's tone and communication style to match my brand or application"],"best_for":["developers building specialized AI assistants or chatbots","teams creating white-label AI products with custom behavior","applications requiring consistent tone and style across interactions","systems implementing custom safety policies or compliance requirements"],"limitations":["System prompts are suggestions, not hard constraints; the model may ignore or partially follow system instructions if user input conflicts","Very long or complex system prompts consume token budget; system tokens count toward usage limits","System prompt injection attacks are possible if user input is concatenated with system prompts; developers must sanitize user input","The model's base training may override system prompts in some cases (e.g., safety guidelines); system prompts cannot fully override core model behavior"],"requires":["OpenAI API key","System prompt text (typically 100-1000 characters, but can be longer)","Input sanitization if user input is combined with system prompts","Testing to verify the model follows system prompt instructions"],"input_types":["system message (role definition, constraints, format instructions)","user messages (queries or instructions)"],"output_types":["text (response following system prompt instructions)","structured data (if system prompt specifies format)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_11","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation.for.api.requests","name":"token counting and cost estimation for api requests","description":"GPT-4o provides token usage information in API responses, including prompt tokens, completion tokens, and total tokens consumed. Developers can use this information to estimate costs, monitor usage, and optimize token efficiency. OpenAI provides the tiktoken library for client-side token counting, enabling developers to estimate costs before making API calls. Token counts vary by language and content type (text vs images), requiring careful tracking for accurate cost prediction.","intents":["I want to estimate API costs before submitting requests to avoid unexpected bills","I need to monitor token usage across my application and set usage alerts or limits","I want to optimize prompts and responses to reduce token consumption and costs","I need to track per-user or per-feature token usage for billing or analytics"],"best_for":["developers building cost-sensitive applications with variable workloads","teams implementing usage-based billing or metering","applications with strict budget constraints","analytics and monitoring systems tracking API costs"],"limitations":["Token counting is approximate; actual token counts may differ slightly from estimates due to tokenization edge cases","Image token counts are estimated based on image size; actual counts depend on image content and compression","Streaming responses don't provide token counts until completion; real-time cost tracking requires buffering or estimation","Token prices change with model updates; cost calculations must be updated when pricing changes"],"requires":["OpenAI API key","tiktoken library (Python) or equivalent token counter for other languages","Awareness of current token prices for GPT-4o (input: $5/1M tokens, output: $15/1M tokens as of May 2024)","Logging and analytics infrastructure to track token usage"],"input_types":["text (for token counting)","images (for image token estimation)"],"output_types":["token count (integer)","cost estimate (float, in USD)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_2","uri":"capability://text.generation.language.context.aware.conversation.management.with.multi.turn.memory","name":"context-aware conversation management with multi-turn memory","description":"GPT-4o maintains conversation state through explicit message history passed in each API request, where each message includes a role (system/user/assistant) and content. The model uses this conversation history to maintain context across turns, enabling it to reference previous statements, build on prior reasoning, and adapt tone/style based on established patterns. The architecture requires clients to manage and persist conversation state; the model itself is stateless and re-processes the full history on each turn, ensuring consistency but requiring careful token budget management for long conversations.","intents":["I want to build multi-turn chatbots where the model remembers previous exchanges and builds on them","I need to maintain conversation context across API calls without external state storage","I want to inject system prompts or role definitions that persist across the entire conversation","I need to implement conversation branching or editing — modify earlier messages and regenerate responses from that point"],"best_for":["developers building chatbot backends with stateless API architectures","teams creating conversational AI without dedicated session storage infrastructure","builders of multi-turn reasoning systems (e.g., tutoring, debugging, creative writing)","applications requiring conversation portability (export/import conversation JSON)"],"limitations":["Full conversation history is re-processed on each turn, causing linear token cost growth with conversation length; 100-turn conversations consume ~10x tokens of single-turn requests","No built-in conversation persistence — clients must implement database storage, serialization, and recovery logic","Context window limits (128K tokens for GPT-4o) constrain maximum conversation length; very long conversations require summarization or pruning strategies","No native support for parallel conversation branches; implementing conversation trees requires client-side logic"],"requires":["OpenAI API key","Client-side conversation state management (array of message objects with role and content)","Token counting logic or library (e.g., tiktoken) to monitor context window usage","Persistent storage for conversation history if durability is required (database, file system, etc.)"],"input_types":["message array with role (system/user/assistant) and content (text or multimodal)","system prompt (optional, typically first message with role='system')"],"output_types":["assistant message (text response)","usage metadata (prompt tokens, completion tokens, total tokens)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_3","uri":"capability://tool.use.integration.function.calling.with.structured.schema.based.tool.invocation","name":"function calling with structured schema-based tool invocation","description":"GPT-4o can be instructed to output structured function calls by providing a JSON schema describing available tools, their parameters, and return types. When the model determines a tool is needed, it outputs a special function_call message containing the tool name and arguments as JSON. The client then executes the tool, returns results in a new message, and the model continues reasoning with the tool output. This enables agentic workflows where the model acts as a planner/reasoner and external tools provide grounded information or actions.","intents":["I want to build AI agents that can call APIs, databases, or custom functions to retrieve real-time data or perform actions","I need the model to decide when and how to use tools based on user intent, not just execute predefined sequences","I want to implement multi-step workflows where tool outputs inform subsequent tool calls or reasoning","I need structured, validated tool invocations — ensure the model only calls tools with correct parameter types and required fields"],"best_for":["developers building AI agents with external tool integration (API calls, database queries, code execution)","teams implementing retrieval-augmented generation (RAG) where the model decides when to search","builders of autonomous workflows that require grounded decision-making","applications needing structured data extraction with validation"],"limitations":["Schema validation is client-side responsibility; the model may output invalid JSON or parameters that don't match the schema, requiring error handling and retry logic","No native support for streaming function calls — entire function call must be generated before client can invoke the tool","Tool execution latency is additive; each tool call adds network round-trip time, making multi-step workflows slower than single-pass solutions","Model may hallucinate tool names or parameters not in the schema; robust implementations require strict validation and error recovery"],"requires":["OpenAI API key","JSON schema definitions for each tool (name, description, parameters with types)","Client-side tool registry and execution logic","Error handling for invalid function calls (malformed JSON, missing required parameters, unknown tool names)","Conversation history management to track tool calls and results"],"input_types":["text (user query or instruction)","tools array (JSON schema definitions with name, description, parameters)","conversation history with function_call and function messages"],"output_types":["function_call message (tool name + arguments as JSON)","text response (model reasoning or final answer)","mixed output (text with embedded function calls)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_4","uri":"capability://image.visual.vision.based.code.understanding.and.generation.from.screenshots","name":"vision-based code understanding and generation from screenshots","description":"GPT-4o can analyze code screenshots, UI mockups, and development environment screenshots to understand code structure, identify bugs, or generate code based on visual specifications. The model processes the image through its unified vision-language architecture, extracting text from code, understanding layout and syntax highlighting, and reasoning about the code's purpose. This enables workflows where developers provide screenshots instead of copy-pasting code, or where designers provide mockups for implementation.","intents":["I want to paste a screenshot of code and ask the model to refactor, debug, or explain it without manual copy-paste","I need to generate code from design mockups or wireframes — provide a screenshot and get implementation","I want to analyze UI screenshots to identify accessibility issues, responsive design problems, or styling inconsistencies","I need to extract code from documentation, tutorials, or Stack Overflow screenshots and adapt it"],"best_for":["developers using screen-sharing or screenshot-based workflows","teams with design-to-code pipelines where mockups are primary artifacts","educators and technical writers creating visual code examples","developers debugging issues by sharing screenshots of error messages or code"],"limitations":["OCR accuracy degrades with poor image quality, small fonts, or unusual syntax highlighting; code with non-standard fonts may be misread","The model cannot execute or validate the code it extracts from screenshots; logical errors or context-dependent issues may be missed","Very large code files (>500 lines) in a single screenshot become difficult to read; the model may miss details or misunderstand structure","Syntax highlighting and formatting are lost in the model's understanding; the model sees text, not semantic code structure"],"requires":["OpenAI API key with vision capability","Screenshots in JPEG, PNG, GIF, or WebP format with readable font size (minimum ~12pt)","Clear, well-lit screenshots with sufficient contrast for OCR","Acceptance that extracted code may require manual verification and correction"],"input_types":["image (screenshot of code, UI, or documentation)","text (question or instruction about the image)"],"output_types":["text (explanation, refactored code, bug analysis)","code (extracted or generated based on visual input)","structured data (identified issues, suggestions)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_5","uri":"capability://image.visual.document.analysis.and.structured.data.extraction.from.images","name":"document analysis and structured data extraction from images","description":"GPT-4o can extract structured data from documents, forms, invoices, receipts, and tables by analyzing their visual representation. The model identifies document type, locates relevant fields, extracts text and numbers, and can output results as JSON, CSV, or other structured formats. This enables document processing workflows without OCR preprocessing or manual field mapping, leveraging the model's ability to understand document layout and semantics simultaneously.","intents":["I want to extract invoice data (vendor, amount, date, line items) from a scanned PDF or image without manual data entry","I need to parse form submissions where users upload photos of filled-out forms and extract field values","I want to analyze tables in documents and convert them to structured data (CSV, JSON, database records)","I need to identify and extract key information from receipts, contracts, or other semi-structured documents"],"best_for":["teams automating document processing and data entry workflows","fintech applications handling invoice and receipt processing","form-based services accepting image uploads instead of typed input","businesses digitizing paper documents or legacy data"],"limitations":["Extraction accuracy depends on document quality, legibility, and standardization; handwritten or heavily damaged documents may have high error rates","The model may miss fields or misinterpret ambiguous data; validation and human review are recommended for high-stakes applications","Complex multi-page documents require separate image processing per page; the model cannot process PDF files directly","No built-in confidence scores or uncertainty quantification; developers must implement validation logic to detect extraction errors"],"requires":["OpenAI API key with vision capability","Document images in JPEG, PNG, GIF, or WebP format","Clear, readable documents with sufficient contrast and resolution (minimum 150 DPI recommended)","JSON schema or format specification for desired output structure","Validation logic to verify extracted data matches expected types and constraints"],"input_types":["image (document, form, invoice, receipt, table)","text (instructions for extraction, desired output format)"],"output_types":["JSON (structured extracted data)","CSV (tabular data)","text (formatted extraction results)","code (Python dict, JavaScript object, etc.)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_6","uri":"capability://planning.reasoning.reasoning.focused.response.generation.with.extended.thinking.patterns","name":"reasoning-focused response generation with extended thinking patterns","description":"GPT-4o can be prompted to engage in explicit reasoning chains, step-by-step problem decomposition, and multi-stage analysis before generating final responses. While the model doesn't have a dedicated 'chain-of-thought' mode like some alternatives, it responds well to prompts that request detailed reasoning, intermediate steps, and explicit justification. The model's training enables it to naturally produce reasoning-heavy outputs when prompted, supporting workflows where explanation and justification are as important as the final answer.","intents":["I want the model to show its work — explain reasoning steps before providing a final answer","I need to debug complex problems by having the model think through multiple approaches before choosing one","I want to generate educational content where the reasoning process is as valuable as the answer","I need to verify the model's logic by inspecting intermediate reasoning steps"],"best_for":["educational applications where reasoning transparency is critical","debugging and troubleshooting workflows requiring step-by-step analysis","research and analysis tasks where methodology matters as much as conclusions","teams building explainable AI systems with audit trails"],"limitations":["Reasoning-focused responses are longer and consume more tokens; a detailed reasoning chain may be 3-5x longer than a direct answer","The model may produce verbose or redundant reasoning; not all intermediate steps are equally valuable","No guarantee of correctness — detailed reasoning can be confidently wrong; reasoning transparency doesn't ensure accuracy","Reasoning patterns are learned from training data; the model may not reason about novel or highly specialized domains effectively"],"requires":["OpenAI API key","Prompts that explicitly request reasoning (e.g., 'Think step-by-step', 'Show your work', 'Explain your reasoning')","Token budget awareness for longer responses","Parsing logic to extract reasoning steps from free-form text if structured output is needed"],"input_types":["text (question or problem requiring analysis)","images (for visual reasoning tasks)"],"output_types":["text (reasoning steps + final answer)","structured reasoning (if prompted to format as JSON or markdown)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_7","uri":"capability://text.generation.language.multilingual.text.generation.and.translation.across.50.languages","name":"multilingual text generation and translation across 50+ languages","description":"GPT-4o supports input and output in 50+ languages including English, Spanish, French, German, Chinese, Japanese, Arabic, Hindi, and many others. The model handles language detection automatically, maintains semantic meaning across language boundaries, and can translate, summarize, or generate content in any supported language. The unified transformer architecture processes all languages through the same token space, enabling cross-lingual reasoning and code-switching (mixing languages in a single response).","intents":["I want to build applications that serve global users in their native languages without separate language-specific models","I need to translate content between languages while preserving tone, context, and technical accuracy","I want to generate content in multiple languages from a single prompt (e.g., multilingual documentation)","I need to handle user input in any language and respond appropriately without explicit language selection"],"best_for":["global SaaS applications serving users in multiple countries","content creation teams producing multilingual materials","localization workflows requiring high-quality translation","international customer support systems"],"limitations":["Translation quality varies by language pair; less common languages or specialized terminology may have lower accuracy","The model may not preserve formatting, special characters, or language-specific conventions (e.g., date formats, number separators)","Cultural context and idioms may not translate perfectly; human review is recommended for marketing or sensitive content","Token efficiency varies by language; some languages (e.g., Chinese, Japanese) use more tokens per character than English"],"requires":["OpenAI API key","UTF-8 encoding support for input and output","Awareness of token budget differences across languages","Optional: language specification in prompts for disambiguation (e.g., 'Respond in French')"],"input_types":["text in any of 50+ supported languages","mixed-language input (code-switching)"],"output_types":["text in any supported language","mixed-language output (if requested)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_8","uri":"capability://code.generation.editing.code.generation.and.completion.across.50.programming.languages","name":"code generation and completion across 50+ programming languages","description":"GPT-4o can generate, complete, and refactor code in 50+ programming languages including Python, JavaScript, Java, C++, Go, Rust, SQL, and many others. The model understands language-specific syntax, idioms, libraries, and best practices, enabling it to generate production-quality code or complete partial implementations. The unified architecture processes code as text, enabling the model to reason about code structure, dependencies, and logic alongside natural language explanations.","intents":["I want to generate boilerplate or utility code without manually typing it","I need to complete partial code implementations — provide a function signature and get the body","I want to refactor or optimize code while maintaining functionality","I need to generate code in languages I'm less familiar with, relying on the model's knowledge of idioms and best practices"],"best_for":["developers using AI-assisted coding tools and IDEs","teams automating code generation from specifications or templates","polyglot teams working across multiple programming languages","educational contexts where students learn by reading AI-generated examples"],"limitations":["Generated code may contain logical errors, security vulnerabilities, or performance issues; code review and testing are essential","The model may not understand project-specific conventions, internal libraries, or custom frameworks without explicit context","Very large codebases exceed context window limits; the model cannot reason about entire projects, only code snippets","Generated code may be outdated if training data predates recent language versions or library updates"],"requires":["OpenAI API key","Code context (existing code, function signatures, requirements) for better generation quality","Testing and validation infrastructure to verify generated code","Language-specific linters and formatters to ensure code quality"],"input_types":["text (code snippets, function signatures, requirements)","images (screenshots of code for analysis)"],"output_types":["code (generated or completed implementation)","text (explanation of generated code)","mixed (code with inline comments)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-05-13__cap_9","uri":"capability://automation.workflow.batch.processing.with.asynchronous.job.submission.and.result.retrieval","name":"batch processing with asynchronous job submission and result retrieval","description":"GPT-4o supports batch processing through OpenAI's Batch API, enabling developers to submit multiple requests in a single batch job and retrieve results asynchronously. Batch processing is optimized for cost efficiency (50% discount vs real-time API) and throughput, making it suitable for non-time-sensitive workloads like data processing, content generation, or analysis at scale. Requests are queued and processed in parallel, with results available for retrieval once processing completes (typically within 24 hours).","intents":["I want to process thousands of documents or data points cost-effectively without real-time latency requirements","I need to generate content in bulk (e.g., product descriptions, email templates) and retrieve results later","I want to analyze a large dataset with the model and get results back in a batch, not one-by-one","I need to optimize costs for non-urgent workloads by using batch processing discounts"],"best_for":["data processing pipelines handling large datasets","content generation workflows (bulk writing, summarization, translation)","cost-sensitive applications where latency is not critical","batch analysis and reporting systems"],"limitations":["Results are not available immediately; typical latency is 1-24 hours depending on queue depth and batch size","No streaming support in batch mode; responses are returned in full once processing completes","Batch API has different error handling semantics; individual request failures don't stop the batch, requiring per-request error checking","Batch jobs cannot be cancelled once submitted; failed requests must be resubmitted in a new batch"],"requires":["OpenAI API key with batch processing enabled","JSONL file format for batch requests (one JSON object per line)","Polling or webhook logic to check batch status and retrieve results","Storage for batch job IDs and result mapping"],"input_types":["JSONL file (newline-delimited JSON with request objects)","each request object contains: custom_id, method, url, body (same as standard API)"],"output_types":["JSONL file (results with custom_id, response, error fields)","batch metadata (status, request_counts, processing_times)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-4o access enabled","HTTP client capable of multipart form data or JSON with base64 encoding","Images in JPEG, PNG, GIF, or WebP format","Token budget awareness: vision tokens consume ~85 tokens per 512x512 image tile","OpenAI API key with streaming enabled","HTTP client with streaming/chunked transfer encoding support (e.g., fetch with ReadableStream, httpx with stream=True)","Server-sent events (SSE) parser or equivalent streaming JSON parser","Handling of connection timeouts and reconnection logic for long-running streams","OpenAI API key","System prompt text (typically 100-1000 characters, but can be longer)"],"failure_modes":["Image inputs must be base64-encoded or provided via URL; no direct file streaming support","Maximum image resolution and token budget constraints limit analysis of very high-resolution or multi-page documents","Vision understanding is optimized for natural images and documents; synthetic or heavily stylized visuals may have degraded performance","No video input support — only static images; temporal reasoning across frames requires frame-by-frame processing","Streaming adds complexity to error handling — errors may occur mid-stream after partial content is sent","Token-level streaming prevents certain post-processing optimizations (e.g., deduplication, filtering) that require full output visibility","Streaming responses cannot be retried at the token level; partial streams must be discarded and regenerated","Client-side buffering and rendering of high-frequency token streams can cause UI jank on low-end devices","System prompts are suggestions, not hard constraints; the model may ignore or partially follow system instructions if user input conflicts","Very long or complex system prompts consume token budget; system tokens count toward usage limits","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":"2026-05-24T12:16:24.485Z","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-4o-2024-05-13","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4o-2024-05-13"}},"signature":"8RS8MK7Ts+ksrYEgsT4njB7uDtHpO72jTPHJU2d2ql5uRhK+eh/FsNVEf/LeI8AAEx8e1mXlunzQpiU/ym7ECg==","signedAt":"2026-06-21T21:38:48.875Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4o-2024-05-13","artifact":"https://unfragile.ai/openai-gpt-4o-2024-05-13","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4o-2024-05-13","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"}}