{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-4o-2024-08-06","slug":"openai-gpt-4o-2024-08-06","name":"OpenAI: GPT-4o (2024-08-06)","type":"model","url":"https://openrouter.ai/models/openai~gpt-4o-2024-08-06","page_url":"https://unfragile.ai/openai-gpt-4o-2024-08-06","categories":["llm-apis"],"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-2024-08-06__cap_0","uri":"capability://image.visual.multimodal.text.and.image.understanding.with.unified.embedding.space","name":"multimodal text and image understanding with unified embedding space","description":"GPT-4o processes both text and image inputs through a shared transformer architecture trained on interleaved text-image data, enabling it to reason across modalities without separate encoding pipelines. The model uses a unified token vocabulary that treats image patches and text tokens equivalently, allowing seamless cross-modal attention and reasoning within a single forward pass.","intents":["analyze images with complex textual context and return nuanced descriptions","extract structured information from documents containing both text and visual elements","answer questions about images that require reading embedded text and understanding visual composition","process screenshots and diagrams with overlaid annotations or labels"],"best_for":["document processing pipelines needing OCR + semantic understanding","multimodal RAG systems requiring unified embeddings across text and images","accessibility tools converting visual content to detailed descriptions"],"limitations":["image resolution limited to ~2000x2000 pixels; larger images are downsampled, potentially losing fine detail","no native video frame extraction — requires pre-processing video into individual frames","cross-modal reasoning latency ~15-20% higher than text-only due to image tokenization overhead"],"requires":["OpenAI API key with GPT-4o access","HTTP/2 client library for streaming image data","Base64 encoding or URL-accessible image hosting for image inputs"],"input_types":["text (UTF-8, up to 128K tokens)","image (JPEG, PNG, GIF, WebP; up to 20MB per image)"],"output_types":["text (UTF-8, up to 4096 tokens default)","structured JSON when using response_format schema"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_1","uri":"capability://data.processing.analysis.json.schema.constrained.structured.output.generation","name":"json schema-constrained structured output generation","description":"GPT-4o implements schema-based output validation through a response_format parameter accepting a JSON Schema Draft 2020-12 specification, which constrains token generation to only produce valid JSON matching the schema. The model uses in-context schema awareness during decoding to prune invalid token sequences in real-time, guaranteeing schema compliance without post-processing.","intents":["extract structured entities from unstructured text with guaranteed JSON validity","generate API payloads or database records with enforced field types and constraints","build deterministic pipelines where downstream systems require strict schema compliance","reduce parsing errors and validation logic in production systems"],"best_for":["data extraction pipelines feeding into databases or APIs","LLM-powered form filling and data collection systems","teams building agentic systems requiring deterministic structured outputs"],"limitations":["schema complexity overhead: deeply nested schemas (>10 levels) add 5-10% latency per request","enum constraints limited to ~1000 distinct values; larger enums degrade performance","no conditional schema validation — cannot express 'if field A is X, then field B must be Y' constraints","schema must be provided at request time; no caching of schema validation rules across requests"],"requires":["OpenAI API key with structured outputs feature enabled (2024-08-06+ model)","JSON Schema Draft 2020-12 compliant schema definition","Client library supporting response_format parameter (openai-python 1.12.0+, openai-node 4.28.0+)"],"input_types":["text prompt (UTF-8)","JSON Schema (application/json)"],"output_types":["JSON object (guaranteed valid per provided schema)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_10","uri":"capability://planning.reasoning.reasoning.aware.chain.of.thought.prompting.with.step.by.step.decomposition","name":"reasoning-aware chain-of-thought prompting with step-by-step decomposition","description":"GPT-4o can be prompted to generate step-by-step reasoning before providing final answers using chain-of-thought (CoT) patterns, where explicit intermediate reasoning steps improve accuracy on complex tasks. The model uses attention mechanisms to maintain reasoning state across steps and can be guided to decompose problems hierarchically, enabling better performance on math, logic, and multi-step reasoning tasks.","intents":["improve accuracy on math problems by requesting step-by-step solutions","debug model reasoning by examining intermediate steps in complex tasks","implement hierarchical problem decomposition for multi-step workflows","reduce hallucination on knowledge-intensive tasks by forcing explicit reasoning"],"best_for":["educational applications where showing work is important","reasoning-heavy tasks (math, logic puzzles, code debugging)","systems requiring explainability or audit trails of model decisions","applications where accuracy is critical and reasoning transparency is valued"],"limitations":["chain-of-thought adds 2-5x token overhead — reasoning steps consume significant context window","reasoning quality depends on prompt structure; poorly formatted CoT prompts may degrade performance","no guarantee of correct reasoning — model can generate plausible-sounding but incorrect intermediate steps","reasoning steps are not formally verified; downstream systems must validate correctness independently"],"requires":["OpenAI API key","Prompt engineering to structure CoT requests (e.g., 'Let's think step by step...')","Validation logic to verify correctness of reasoning steps"],"input_types":["text prompt requesting step-by-step reasoning"],"output_types":["text with intermediate reasoning steps followed by final answer"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_11","uri":"capability://automation.workflow.batch.processing.api.for.cost.optimized.asynchronous.inference","name":"batch processing api for cost-optimized asynchronous inference","description":"GPT-4o supports batch processing through the OpenAI Batch API, where multiple requests are submitted together and processed asynchronously with 50% cost reduction compared to standard API calls. The implementation queues requests and processes them in optimized batches during off-peak hours, trading latency (12-24 hour turnaround) for significant cost savings on non-time-sensitive workloads.","intents":["process large volumes of data (1000+ documents) with reduced per-request cost","run nightly batch jobs for content generation, classification, or extraction","optimize costs for non-real-time applications like report generation or data enrichment","implement cost-conscious data pipelines where latency is acceptable"],"best_for":["data processing pipelines with flexible latency requirements (hours to days)","cost-sensitive applications processing large document volumes","batch analytics or reporting systems","organizations with variable workloads seeking to optimize cloud costs"],"limitations":["latency: batch requests processed with 12-24 hour turnaround; unsuitable for real-time applications","no streaming support — batch API returns full responses only, no token-level streaming","batch size limits: maximum 100,000 requests per batch; larger workloads require multiple batches","no priority queuing — all batches processed in FIFO order without expedited processing options"],"requires":["OpenAI API key with batch API access","JSONL file format for batch requests (one JSON request per line)","Asynchronous job tracking to poll for batch completion status"],"input_types":["JSONL file containing multiple API requests"],"output_types":["JSONL file with corresponding responses (one per line)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_2","uri":"capability://memory.knowledge.long.context.reasoning.with.128k.token.window","name":"long-context reasoning with 128k token window","description":"GPT-4o maintains a 128,000 token context window using a sliding-window attention mechanism with sparse attention patterns, enabling it to process entire documents, codebases, or conversation histories without truncation. The model uses rotary position embeddings (RoPE) to maintain positional awareness across the full window while reducing memory overhead through selective attention to recent and relevant tokens.","intents":["analyze entire source code files or multi-file projects without splitting into chunks","process full research papers or technical documentation in a single request","maintain conversation history over 50+ turns without losing early context","perform codebase-wide refactoring with full dependency visibility"],"best_for":["code review and refactoring tools processing large files (>50KB)","document analysis systems handling full PDFs or long-form content","conversational AI systems requiring persistent multi-turn memory","RAG systems where full document context improves answer quality"],"limitations":["token counting overhead: processing 128K tokens adds ~2-3 seconds latency vs. 8K context models","cost scales linearly with context size — 128K context costs 16x more than 8K context for same output","attention mechanism becomes less precise at extreme context lengths (>100K tokens); relevance ranking degrades","no native sliding-window persistence — context is lost between API calls unless explicitly managed by client"],"requires":["OpenAI API key with GPT-4o access","Token counting library to estimate context size before API calls (openai-python includes tiktoken)","Sufficient API rate limits to handle 128K token requests (typically requires paid tier)"],"input_types":["text (up to 128,000 tokens total including system prompt and conversation history)"],"output_types":["text (up to 4,096 tokens default, configurable up to 4,096)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_3","uri":"capability://code.generation.editing.vision.based.code.understanding.and.generation","name":"vision-based code understanding and generation","description":"GPT-4o can analyze screenshots, diagrams, and visual representations of code (e.g., flowcharts, architecture diagrams, whiteboard sketches) and generate or refactor code based on visual intent. The model uses its unified multimodal architecture to extract semantic meaning from visual layouts and convert them into executable code, supporting diagram-to-code workflows without intermediate textual specifications.","intents":["convert hand-drawn or whiteboard sketches into functional code","generate code from architecture diagrams or system design mockups","analyze screenshots of legacy systems and suggest modernization","extract data models from visual entity-relationship diagrams"],"best_for":["low-code/no-code platforms accepting visual input for code generation","design-to-code tools for UI/UX prototyping","documentation systems converting diagrams to executable specifications","accessibility tools helping non-programmers describe code intent visually"],"limitations":["accuracy degrades for hand-drawn sketches with poor image quality or ambiguous notation","no support for animated or interactive diagrams — requires static image input","generated code from visual input requires manual review; no formal verification of correctness","limited to common programming languages and frameworks; niche or domain-specific languages may be misinterpreted"],"requires":["OpenAI API key with GPT-4o vision capability","Image preprocessing pipeline for sketch/diagram normalization (contrast enhancement, rotation correction)","Code validation framework to test generated code before deployment"],"input_types":["image (screenshot, diagram, sketch; JPEG, PNG, WebP)","text prompt describing code intent or constraints"],"output_types":["code (Python, JavaScript, SQL, etc.)","structured JSON schema for data models"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_4","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.binding","name":"function calling with schema-based tool binding","description":"GPT-4o supports tool_use via a function calling interface where developers define functions as JSON schemas, and the model generates function calls with arguments matching the schema. The model uses constrained decoding to ensure generated function calls are valid JSON and match the provided schema signature, enabling deterministic tool orchestration without parsing errors.","intents":["build agentic systems where the model decides which tools to invoke and with what arguments","integrate LLMs with external APIs or databases through structured function calls","create multi-step workflows where the model chains function calls to accomplish complex tasks","implement tool-use patterns for code execution, web search, or database queries"],"best_for":["autonomous agent frameworks (LangChain, LlamaIndex, AutoGPT-style systems)","API orchestration layers where LLMs route requests to backend services","chatbots with access to external tools (calculators, web search, CRM systems)","workflow automation systems where LLMs decide task sequencing"],"limitations":["no native parallel function calling — model generates one function call at a time, requiring sequential execution","function schema complexity limited to ~50 parameters per function; deeply nested parameter objects degrade performance","no built-in error handling or retry logic — client must implement fallback strategies if function execution fails","tool context window cost: each function definition consumes ~100-200 tokens, limiting practical tool count to ~10-15 before context bloat"],"requires":["OpenAI API key with function calling support","Client library supporting tools parameter (openai-python 1.0+, openai-node 4.0+)","JSON Schema definitions for each function","Execution environment for invoking actual functions and returning results to the model"],"input_types":["text prompt","JSON Schema array defining available functions"],"output_types":["function call objects with name and arguments (JSON)","text response if model chooses not to call a function"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_5","uri":"capability://text.generation.language.real.time.streaming.text.generation.with.token.level.control","name":"real-time streaming text generation with token-level control","description":"GPT-4o supports server-sent events (SSE) streaming where tokens are emitted incrementally as they are generated, enabling real-time display of model output without waiting for full completion. The implementation uses chunked HTTP transfer encoding with delta objects containing individual tokens, allowing clients to render text progressively and implement token-level callbacks for monitoring or interruption.","intents":["build responsive chat interfaces with real-time token streaming for perceived latency reduction","implement token-counting or cost estimation during generation without waiting for completion","create interrupt-able generation workflows where users can stop generation mid-stream","monitor token generation patterns for debugging or analytics"],"best_for":["chat applications and conversational interfaces requiring perceived responsiveness","real-time code generation tools where users see code appearing line-by-line","streaming analytics dashboards consuming LLM output incrementally","cost-conscious applications needing to estimate token usage before full generation"],"limitations":["streaming adds ~50-100ms latency overhead per request due to SSE handshake and chunking","token-level callbacks cannot modify generation mid-stream — interruption requires connection termination","no native backpressure handling — fast clients may overwhelm with token processing if not rate-limited","streaming incompatible with structured output schemas — response_format requires full generation before validation"],"requires":["OpenAI API key with streaming support","HTTP/2 client with SSE support (most modern libraries include this)","stream=true parameter in API request","Event listener implementation to handle token deltas"],"input_types":["text prompt"],"output_types":["server-sent events (SSE) with delta objects containing individual tokens"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_6","uri":"capability://text.generation.language.multilingual.text.generation.and.understanding.across.100.languages","name":"multilingual text generation and understanding across 100+ languages","description":"GPT-4o was trained on text from 100+ languages with balanced representation, enabling it to generate and understand content across diverse language families (Indo-European, Sino-Tibetan, Afro-Asiatic, etc.). The model uses a shared vocabulary and unified transformer weights across all languages, allowing cross-lingual reasoning and translation without language-specific fine-tuning or separate models.","intents":["build multilingual chatbots serving users in their native language without language detection","translate content between language pairs with context-aware semantic preservation","analyze sentiment or extract entities from non-English text in a single model call","generate content in low-resource languages where specialized models are unavailable"],"best_for":["global applications serving diverse linguistic markets","content moderation systems handling multilingual user input","translation pipelines where context preservation is critical","multilingual search and retrieval systems"],"limitations":["performance varies significantly by language — high-resource languages (English, Spanish, Mandarin) achieve 95%+ accuracy; low-resource languages (Icelandic, Swahili) may drop to 70-80%","code-switching (mixing multiple languages in single text) can confuse the model; performance degrades with >20% code-switching","no native language detection — client must specify language or model may misidentify language family","transliteration and script handling imperfect for languages with complex orthography (Arabic, Devanagari)"],"requires":["OpenAI API key","UTF-8 text encoding support in client","Optional: language detection library (langdetect, textblob) for automatic language identification"],"input_types":["text in any of 100+ supported languages (UTF-8 encoded)"],"output_types":["text in any supported language"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_7","uri":"capability://image.visual.vision.based.document.analysis.and.ocr.with.layout.understanding","name":"vision-based document analysis and ocr with layout understanding","description":"GPT-4o can process images of documents (PDFs rendered as images, scanned papers, forms) and extract text, structure, and semantic meaning while preserving layout information. The model uses spatial reasoning to understand document hierarchy (headers, tables, footnotes) and can extract structured data from forms or tables without explicit coordinate-based parsing, enabling end-to-end document understanding from image input.","intents":["extract structured data from scanned forms or invoices without manual data entry","convert PDF documents to markdown or structured JSON preserving original layout","analyze tables in images and convert to CSV or database records","read handwritten notes or annotations in document images"],"best_for":["document processing pipelines for invoicing, expense management, or compliance","accessibility tools converting document images to machine-readable formats","form automation systems extracting data from paper or digital forms","knowledge extraction from historical documents or archives"],"limitations":["handwriting recognition accuracy varies by handwriting quality — printed text >95% accurate, cursive handwriting 70-85% accurate","table extraction limited to ~50 rows; larger tables may lose row/column alignment","no native PDF parsing — requires converting PDF pages to images first (adds preprocessing step)","layout preservation imperfect for complex multi-column documents or documents with overlapping text"],"requires":["OpenAI API key with vision capability","PDF-to-image conversion tool (PyPDF2, pdf2image, pdfplumber) for PDF input","Image preprocessing for document normalization (deskewing, contrast enhancement)"],"input_types":["image (scanned document, form, invoice; JPEG, PNG, WebP)"],"output_types":["text (extracted content)","JSON (structured data from forms or tables)","markdown (document with preserved layout)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_8","uri":"capability://planning.reasoning.few.shot.learning.with.in.context.examples.for.task.adaptation","name":"few-shot learning with in-context examples for task adaptation","description":"GPT-4o can adapt to new tasks by including examples in the prompt (few-shot learning), where the model learns task patterns from 1-10 examples without fine-tuning. The implementation uses attention mechanisms to identify patterns in examples and apply them to new inputs, enabling rapid task adaptation for classification, extraction, or generation tasks without model updates.","intents":["adapt the model to domain-specific classification tasks (e.g., sentiment analysis for financial news) with 3-5 examples","teach the model custom output formats or naming conventions through example prompts","implement zero-shot to few-shot fallback patterns where complex tasks benefit from examples","reduce hallucination in specialized domains by grounding the model with relevant examples"],"best_for":["rapid prototyping of NLP tasks without fine-tuning infrastructure","domain-specific applications where labeled data is limited but examples are available","multi-tenant systems where different customers need different task behaviors","A/B testing task approaches without retraining models"],"limitations":["few-shot learning adds example tokens to context window — 10 examples can consume 1-2K tokens, reducing available context for actual input","performance plateaus after ~10 examples; adding more examples doesn't improve accuracy and wastes tokens","example quality is critical — poor or contradictory examples degrade performance more than zero-shot","no native example selection or active learning — client must manually curate examples"],"requires":["OpenAI API key","Curated examples relevant to the target task","Prompt engineering to structure examples clearly (e.g., 'Example 1: Input: ... Output: ...')"],"input_types":["text prompt with embedded examples"],"output_types":["text (following patterns established by examples)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-2024-08-06__cap_9","uri":"capability://safety.moderation.safety.aware.content.generation.with.built.in.guardrails","name":"safety-aware content generation with built-in guardrails","description":"GPT-4o includes trained safety mechanisms that reduce generation of harmful, illegal, or unethical content through reinforcement learning from human feedback (RLHF) and constitutional AI principles. The model uses learned safety classifiers during generation to suppress tokens associated with harmful outputs, without requiring explicit content filters or external moderation APIs.","intents":["build customer-facing applications without external content moderation infrastructure","reduce risk of generating illegal content (malware, exploits, instructions for harm)","implement responsible AI practices with minimal additional overhead","comply with content policies without manual review workflows"],"best_for":["public-facing chat applications and customer service bots","content generation platforms requiring automated safety guardrails","regulated industries (finance, healthcare) with compliance requirements","applications serving minors or vulnerable populations"],"limitations":["safety mechanisms are probabilistic — edge cases and adversarial prompts can still elicit unsafe content","safety guardrails may over-filter legitimate content (e.g., refusing to discuss cybersecurity topics)","no transparency into safety decision-making — difficult to debug why specific outputs were rejected","safety mechanisms add ~5-10% latency overhead due to safety classifier inference"],"requires":["OpenAI API key","Awareness of model limitations and potential for jailbreaking","Optional: external content moderation (OpenAI Moderation API) for additional safety layer"],"input_types":["text prompt"],"output_types":["text (with safety filtering applied)"],"categories":["safety-moderation"],"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","HTTP/2 client library for streaming image data","Base64 encoding or URL-accessible image hosting for image inputs","OpenAI API key with structured outputs feature enabled (2024-08-06+ model)","JSON Schema Draft 2020-12 compliant schema definition","Client library supporting response_format parameter (openai-python 1.12.0+, openai-node 4.28.0+)","OpenAI API key","Prompt engineering to structure CoT requests (e.g., 'Let's think step by step...')","Validation logic to verify correctness of reasoning steps","OpenAI API key with batch API access"],"failure_modes":["image resolution limited to ~2000x2000 pixels; larger images are downsampled, potentially losing fine detail","no native video frame extraction — requires pre-processing video into individual frames","cross-modal reasoning latency ~15-20% higher than text-only due to image tokenization overhead","schema complexity overhead: deeply nested schemas (>10 levels) add 5-10% latency per request","enum constraints limited to ~1000 distinct values; larger enums degrade performance","no conditional schema validation — cannot express 'if field A is X, then field B must be Y' constraints","schema must be provided at request time; no caching of schema validation rules across requests","chain-of-thought adds 2-5x token overhead — reasoning steps consume significant context window","reasoning quality depends on prompt structure; poorly formatted CoT prompts may degrade performance","no guarantee of correct reasoning — model can generate plausible-sounding but incorrect intermediate steps","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":"2026-05-03T15:20:45.776Z","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-2024-08-06","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4o-2024-08-06"}},"signature":"3nHLg8GIguhfISmTjARNk5LLTLRYGKjqJYNl4K9WKnmjzBQe1rdMn6Rv0oSziR3b8l6J9jdZ+KODZKm4A8RNAw==","signedAt":"2026-06-21T11:48:37.522Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4o-2024-08-06","artifact":"https://unfragile.ai/openai-gpt-4o-2024-08-06","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4o-2024-08-06","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"}}