{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-4o","slug":"openai-gpt-4o","name":"OpenAI: GPT-4o","type":"model","url":"https://openrouter.ai/models/openai~gpt-4o","page_url":"https://unfragile.ai/openai-gpt-4o","categories":["image-generation"],"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__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, eliminating separate vision and language encoders. Images are tokenized into visual patches and embedded into the same token sequence as text, allowing the model to reason jointly over mixed modalities without explicit fusion layers. This architecture enables pixel-level image understanding (OCR, spatial reasoning, object detection) while maintaining full language comprehension in a single forward pass.","intents":["I need to analyze screenshots, diagrams, or photos and extract structured information from them","I want to ask questions about images that require understanding both visual content and textual context","I need to perform document analysis on PDFs or scanned images with handwritten or printed text","I want to build a chatbot that can understand user-uploaded images without separate vision API calls"],"best_for":["developers building document processing pipelines","teams creating multimodal chatbots or assistants","builders needing unified vision+language reasoning without orchestrating multiple models"],"limitations":["Image input limited to ~2,000 tokens per image; high-resolution images are downsampled, reducing fine detail capture","No image generation capability — only analysis and understanding","Batch processing of images incurs per-image token costs; no bulk discount for image-heavy workloads","Image understanding quality degrades for very small text (<8pt) or heavily compressed images"],"requires":["OpenAI API key with GPT-4o model access","Images in JPEG, PNG, GIF, or WebP format","HTTP client library to call OpenAI REST API","Base64 encoding or URL-accessible image hosting for API submission"],"input_types":["text (prompts)","image (JPEG, PNG, GIF, WebP, up to 20MB per image)","mixed text+image sequences"],"output_types":["text (natural language responses)","structured text (JSON, markdown, code)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_1","uri":"capability://text.generation.language.long.context.text.generation.with.128k.token.window","name":"long-context text generation with 128k token window","description":"GPT-4o maintains a 128,000-token context window, allowing it to process and generate responses based on very long documents, codebases, or conversation histories in a single request. The model uses rotary positional embeddings (RoPE) and efficient attention mechanisms to handle this extended context without quadratic memory explosion. Developers can submit entire books, API documentation, or multi-file code repositories and ask questions that require reasoning across the full context.","intents":["I need to analyze a large codebase (50+ files) and ask questions about architecture or find bugs across the entire project","I want to summarize a 100-page document or research paper in a single API call","I need to maintain a long conversation history (500+ turns) without losing context or requiring conversation management","I want to perform code review on a large pull request with multiple interdependent files"],"best_for":["developers working with large codebases or documentation","teams building document analysis tools without chunking/RAG complexity","researchers processing long-form content in a single pass"],"limitations":["Token cost scales linearly with context length; a 100K-token request costs ~100x more than a 1K-token request","Attention quality may degrade for information in the middle of very long contexts (lost-in-the-middle effect), though GPT-4o mitigates this better than earlier models","No built-in caching of repeated context across requests; each request reprocesses the full context window","Latency increases with context size; 128K tokens may take 10-30 seconds to process depending on output length"],"requires":["OpenAI API key with GPT-4o access","Text content pre-tokenized or submitted as raw text (OpenAI handles tokenization)","Sufficient API quota to handle high token consumption","HTTP client with timeout tolerance for longer inference times"],"input_types":["text (up to 128,000 tokens)","code (any programming language)","markdown documents","mixed text+image (images count toward token limit)"],"output_types":["text (natural language)","code","structured data (JSON, YAML)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_10","uri":"capability://code.generation.editing.fine.tuning.with.custom.training.data.for.domain.specific.adaptation","name":"fine-tuning with custom training data for domain-specific adaptation","description":"GPT-4o can be fine-tuned on custom training data to adapt the model to specific domains, writing styles, or task-specific behaviors. Fine-tuning uses supervised learning to update model weights based on provided examples, allowing developers to create specialized versions of GPT-4o. The fine-tuning process is managed via the OpenAI API, with training data provided as JSONL files containing prompt-completion pairs.","intents":["I need to adapt GPT-4o to my company's writing style, terminology, or domain-specific knowledge","I want to improve performance on a specific task by providing labeled examples","I need to reduce token consumption by fine-tuning the model to be more concise","I want to create a specialized version of GPT-4o for a particular industry or use case"],"best_for":["teams with domain-specific use cases and labeled training data","developers needing to optimize model behavior for specific tasks","builders creating specialized AI products for particular industries"],"limitations":["Fine-tuning requires high-quality labeled training data (typically 100+ examples); poor data quality degrades performance","Fine-tuning adds cost and latency to the training process; training a model may take hours or days","Fine-tuned models are not automatically updated when OpenAI releases new base model versions","Fine-tuning may reduce generalization; models may overfit to training data and perform poorly on out-of-distribution inputs"],"requires":["OpenAI API key with fine-tuning access","Training data in JSONL format (prompt-completion pairs)","Minimum 10-20 examples per task; 100+ examples recommended for best results","Validation data to evaluate fine-tuned model performance"],"input_types":["JSONL file (training data with prompt-completion pairs)","text (prompts for evaluation)"],"output_types":["fine-tuned model (accessible via API)","training metrics (loss, accuracy)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_2","uri":"capability://text.generation.language.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"GPT-4o supports constrained generation via JSON schema specification, ensuring output strictly adheres to a provided schema without post-processing or validation. The model uses grammar-constrained decoding (similar to outlines.ai or llama.cpp's approach) to enforce token-level constraints during generation, guaranteeing valid JSON that matches the schema. Developers specify a JSON schema in the API request, and the model generates only tokens that produce valid schema-compliant output.","intents":["I need to extract structured data (entities, relationships, fields) from unstructured text and guarantee valid JSON output","I want to generate API responses that must conform to a specific OpenAPI schema without manual validation","I need to build a form-filling agent that produces guaranteed-valid structured data for database insertion","I want to reduce post-processing overhead by having the model enforce output structure at generation time"],"best_for":["developers building data extraction pipelines","teams integrating LLM outputs directly into databases or APIs","builders needing deterministic output formats without validation layers"],"limitations":["Schema complexity is limited; deeply nested or highly complex schemas may reduce generation quality or increase latency","No support for arbitrary regex constraints or custom validation logic — only JSON schema validation","Constrained decoding adds ~10-20% latency overhead compared to unconstrained generation","Schema must be provided in JSON Schema format; other schema languages (Pydantic, TypeScript interfaces) require conversion"],"requires":["OpenAI API key with GPT-4o access","JSON schema definition (JSON Schema draft 2020-12 compatible)","API client library supporting the structured output parameter (OpenAI Python SDK 1.0+, Node.js SDK 4.0+)"],"input_types":["text (natural language prompt)","JSON schema (as string or object)"],"output_types":["JSON (guaranteed schema-compliant)","structured text"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_3","uri":"capability://text.generation.language.real.time.streaming.text.generation.with.token.level.granularity","name":"real-time streaming text generation with token-level granularity","description":"GPT-4o supports server-sent events (SSE) streaming, delivering generated tokens to the client as they are produced rather than waiting for the full response. The API streams tokens individually, allowing developers to display text progressively, implement real-time chat interfaces, or cancel requests mid-generation. Streaming uses HTTP chunked transfer encoding with JSON-formatted token events, enabling low-latency user feedback.","intents":["I want to build a chat interface that shows the model's response appearing in real-time as it's generated","I need to implement a cancellable long-running generation task that can be stopped by the user","I want to reduce perceived latency by showing partial results while the model is still thinking","I need to process token-by-token output for real-time analytics or filtering"],"best_for":["developers building interactive chat applications","teams creating real-time AI assistants or copilots","builders needing low-latency user feedback on long generations"],"limitations":["Streaming adds ~50-100ms overhead per request due to HTTP connection setup and chunking","Token-level granularity means high-frequency network events; high-latency connections may see degraded UX","No built-in buffering or batching of tokens; client must handle individual token events","Streaming responses cannot be retried mid-stream; connection loss requires restarting the entire request"],"requires":["OpenAI API key with GPT-4o access","HTTP client with SSE support (fetch API, axios, requests library with stream=True)","Client-side event handling for stream events","Timeout handling for long-running streams"],"input_types":["text (prompt)"],"output_types":["text (streamed tokens)","structured events (JSON with token, finish_reason, usage)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_4","uri":"capability://tool.use.integration.function.calling.with.multi.tool.orchestration.and.parallel.execution","name":"function calling with multi-tool orchestration and parallel execution","description":"GPT-4o supports function calling via a schema-based tool registry, where developers define functions as JSON schemas and the model decides which tools to invoke and with what arguments. The model can call multiple functions in parallel within a single response, and the API supports automatic tool result injection for multi-turn tool use. The implementation uses a special token vocabulary for function calls, allowing the model to reason about tool use without generating raw function names.","intents":["I want to build an agent that can call APIs, databases, or custom functions based on user requests","I need to implement a multi-step workflow where the model decides which tools to use and in what order","I want to enable the model to call multiple independent functions in parallel to speed up task completion","I need to build a chatbot that can perform actions (send emails, create calendar events, fetch data) based on user intent"],"best_for":["developers building AI agents and autonomous systems","teams creating tool-augmented chatbots","builders implementing multi-step workflows with LLM decision-making"],"limitations":["Function schemas must be JSON Schema compatible; complex or recursive schemas may confuse the model","No built-in error handling or retry logic; developers must implement tool result validation and error messages","Parallel function calls are limited to ~10 concurrent calls per response; very large tool sets may require filtering","Tool use adds ~100-200ms latency per tool invocation due to additional reasoning and token generation"],"requires":["OpenAI API key with GPT-4o access","Function definitions as JSON schemas","Backend implementation of the actual functions","API client library supporting tool_choice and tool_use parameters"],"input_types":["text (user prompt)","JSON schemas (function definitions)","tool results (as text or structured data)"],"output_types":["function calls (tool_calls with function name and arguments)","text (natural language response)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_5","uri":"capability://image.visual.vision.based.reasoning.with.spatial.understanding.and.object.detection","name":"vision-based reasoning with spatial understanding and object detection","description":"GPT-4o performs spatial reasoning over images, understanding object locations, relationships, and hierarchies without explicit bounding box annotations. The model can identify objects, read text at various scales, understand diagrams and charts, and reason about spatial relationships (above, below, inside, overlapping). This capability is built into the unified multimodal architecture, allowing the model to ground language understanding in visual context.","intents":["I need to analyze a screenshot and identify UI elements, their positions, and how to interact with them","I want to extract data from charts, graphs, or infographics and convert them to structured format","I need to read and understand handwritten notes, diagrams, or sketches","I want to perform visual question-answering on images (e.g., 'What color is the car?' or 'How many people are in this photo?')"],"best_for":["developers building document processing or form automation tools","teams creating visual search or image understanding applications","builders needing OCR and semantic understanding in a single model"],"limitations":["Spatial reasoning quality degrades for very small objects (<50 pixels) or cluttered scenes with many overlapping elements","No explicit bounding box output; spatial understanding is implicit in text responses","Performance on specialized domains (medical imaging, satellite imagery) may be lower than domain-specific models","Image understanding is approximate; exact pixel-level precision is not guaranteed"],"requires":["OpenAI API key with GPT-4o access","Images in supported formats (JPEG, PNG, GIF, WebP)","Prompts that clearly specify the spatial or visual task"],"input_types":["image (JPEG, PNG, GIF, WebP)","text (natural language question or instruction)"],"output_types":["text (natural language description)","structured data (JSON with extracted information)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_6","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"GPT-4o generates code across 40+ programming languages, supporting both full function generation and inline completion. The model understands language-specific syntax, idioms, and best practices, and can generate code that integrates with existing codebases when provided with sufficient context. Code generation uses the same transformer backbone as text generation, allowing the model to reason about code structure and dependencies.","intents":["I need to generate boilerplate code or implement a function based on a description","I want to complete a partially-written function or code snippet","I need to generate code that integrates with an existing codebase (e.g., implement a missing method)","I want to generate test cases or documentation for existing code"],"best_for":["developers using GPT-4o as a coding assistant","teams automating code generation for repetitive tasks","builders creating code-generation tools or IDEs"],"limitations":["Generated code may contain subtle bugs or security vulnerabilities; always review and test generated code","Code quality degrades for very complex algorithms or domain-specific code (e.g., cryptography, systems programming)","No built-in code execution or testing; developers must validate generated code","Context window limits mean very large codebases may require chunking or summarization"],"requires":["OpenAI API key with GPT-4o access","Clear code generation prompts with examples or specifications","Existing code context (if generating code that must integrate with existing code)"],"input_types":["text (code generation prompt)","code (existing code for context or completion)","structured specifications (function signatures, requirements)"],"output_types":["code (generated source code)","text (explanations or comments)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_7","uri":"capability://planning.reasoning.reasoning.focused.response.generation.with.chain.of.thought.patterns","name":"reasoning-focused response generation with chain-of-thought patterns","description":"GPT-4o can be prompted to generate detailed reasoning chains before providing final answers, using explicit chain-of-thought (CoT) patterns. The model breaks down complex problems into steps, shows intermediate reasoning, and arrives at conclusions through explicit logical progression. This capability is enabled through prompt engineering rather than architectural changes, but the model's training makes it particularly effective at following CoT instructions.","intents":["I need the model to show its reasoning process for complex questions, not just provide answers","I want to debug the model's decision-making by seeing intermediate steps","I need to generate explanations that are easy for users to follow and verify","I want to improve accuracy on reasoning-heavy tasks by forcing the model to think step-by-step"],"best_for":["developers building explainable AI systems","teams needing transparent reasoning for high-stakes decisions","builders creating educational or tutoring applications"],"limitations":["Chain-of-thought reasoning increases token consumption by 2-5x due to verbose intermediate steps","Longer reasoning chains increase latency proportionally; complex problems may take 30+ seconds","Model may generate plausible-sounding but incorrect reasoning; CoT does not guarantee correctness","Reasoning quality depends heavily on prompt engineering; poorly-structured prompts may produce incoherent chains"],"requires":["OpenAI API key with GPT-4o access","Prompts that explicitly request step-by-step reasoning (e.g., 'Think step by step...')","Tolerance for higher token consumption and latency"],"input_types":["text (complex questions or problems)","structured prompts (with explicit CoT instructions)"],"output_types":["text (reasoning chain + final answer)","structured reasoning (numbered steps, intermediate conclusions)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_8","uri":"capability://safety.moderation.content.moderation.and.safety.filtering.with.configurable.guardrails","name":"content moderation and safety filtering with configurable guardrails","description":"GPT-4o includes built-in content moderation that filters harmful outputs (violence, hate speech, sexual content, etc.) based on OpenAI's usage policies. The moderation is applied at the output level, preventing the model from generating prohibited content. Developers can also use OpenAI's Moderation API to classify user inputs and filter requests before sending them to GPT-4o, creating a two-layer safety approach.","intents":["I need to ensure the model doesn't generate harmful, illegal, or offensive content","I want to filter user inputs before they reach the model to prevent jailbreak attempts","I need to classify user messages for safety compliance or content policy enforcement","I want to build a safe chatbot that refuses harmful requests gracefully"],"best_for":["teams building public-facing AI applications","developers in regulated industries (healthcare, finance) needing compliance","builders creating content moderation systems"],"limitations":["Moderation is not perfect; some harmful content may slip through, and some benign content may be over-filtered","Moderation rules are set by OpenAI and cannot be customized per application","False positives may reject legitimate requests (e.g., discussing violence in historical or educational contexts)","Moderation adds latency (~50-100ms) if using the separate Moderation API for input filtering"],"requires":["OpenAI API key with GPT-4o access","Awareness of OpenAI's usage policies and content guidelines","Optional: OpenAI Moderation API key for input-level filtering"],"input_types":["text (user prompts or messages)","structured content (for classification)"],"output_types":["moderation flags (categories, scores)","filtered responses (with harmful content removed)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o__cap_9","uri":"capability://automation.workflow.batch.processing.api.for.cost.optimized.bulk.inference","name":"batch processing api for cost-optimized bulk inference","description":"GPT-4o supports batch processing via the OpenAI Batch API, allowing developers to submit hundreds or thousands of requests in a single batch and receive results asynchronously. Batch requests are processed at off-peak times and cost 50% less than standard API calls, making them ideal for non-time-sensitive workloads. Requests are submitted as JSONL files, processed in parallel, and results are returned in a single output file.","intents":["I need to process thousands of documents or records with GPT-4o at lower cost","I want to run overnight batch jobs that don't require real-time responses","I need to analyze a large dataset (e.g., customer feedback, survey responses) without paying full API rates","I want to generate training data or synthetic examples at scale with cost optimization"],"best_for":["teams processing large datasets with non-urgent deadlines","developers building cost-sensitive data processing pipelines","builders creating training data generation systems"],"limitations":["Batch processing is asynchronous; results are not available immediately (typically 1-24 hours)","No streaming support in batch mode; responses are returned as complete text","Batch requests cannot be cancelled once submitted; developers must wait for processing to complete","Batch API has lower priority than standard API; processing time may vary based on OpenAI's load"],"requires":["OpenAI API key with Batch API access","Requests formatted as JSONL (JSON Lines) with specific structure","Ability to handle asynchronous processing and polling for results","Minimum batch size of 10,000 requests to achieve significant cost savings"],"input_types":["JSONL file (batch requests)","text (prompts for each request)"],"output_types":["JSONL file (batch results)","text (responses for each request)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with GPT-4o model access","Images in JPEG, PNG, GIF, or WebP format","HTTP client library to call OpenAI REST API","Base64 encoding or URL-accessible image hosting for API submission","OpenAI API key with GPT-4o access","Text content pre-tokenized or submitted as raw text (OpenAI handles tokenization)","Sufficient API quota to handle high token consumption","HTTP client with timeout tolerance for longer inference times","OpenAI API key with fine-tuning access","Training data in JSONL format (prompt-completion pairs)"],"failure_modes":["Image input limited to ~2,000 tokens per image; high-resolution images are downsampled, reducing fine detail capture","No image generation capability — only analysis and understanding","Batch processing of images incurs per-image token costs; no bulk discount for image-heavy workloads","Image understanding quality degrades for very small text (<8pt) or heavily compressed images","Token cost scales linearly with context length; a 100K-token request costs ~100x more than a 1K-token request","Attention quality may degrade for information in the middle of very long contexts (lost-in-the-middle effect), though GPT-4o mitigates this better than earlier models","No built-in caching of repeated context across requests; each request reprocesses the full context window","Latency increases with context size; 128K tokens may take 10-30 seconds to process depending on output length","Fine-tuning requires high-quality labeled training data (typically 100+ examples); poor data quality degrades performance","Fine-tuning adds cost and latency to the training process; training a model may take hours or days","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4o"}},"signature":"R1acAayHMQv6C45ylhuO7LSGAl40f2/sg9TyF9JOHR1LBqJOU33cLMfJVpSv/B52yGTyFsZ2fc0bd/iHHOcXAg==","signedAt":"2026-06-20T02:24:35.025Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4o","artifact":"https://unfragile.ai/openai-gpt-4o","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4o","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"}}