{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gpt-4o","slug":"gpt-4o","name":"GPT-4o","type":"model","url":"https://openai.com/index/hello-gpt-4o/","page_url":"https://unfragile.ai/gpt-4o","categories":["llm-apis","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gpt-4o__cap_0","uri":"capability://image.visual.multimodal.text.image.audio.understanding.with.unified.embedding.space","name":"multimodal text-image-audio understanding with unified embedding space","description":"GPT-4o processes text, images, and audio through a single transformer architecture with shared token representations, eliminating separate modality encoders. Images are tokenized into visual patches and embedded into the same vector space as text tokens, enabling seamless cross-modal reasoning without explicit fusion layers. Audio is converted to mel-spectrogram tokens and processed identically to text, allowing the model to reason about speech content, speaker characteristics, and emotional tone in a single forward pass.","intents":["I need to analyze documents with mixed text and images without separate API calls","I want to extract structured data from screenshots and PDFs in one request","I need to transcribe and understand audio context alongside text queries","I want to build a chatbot that can see, read, and listen to user input simultaneously"],"best_for":["teams building document intelligence systems (invoices, contracts, forms)","developers creating accessibility tools that need to understand multimodal content","product teams building AI assistants that accept mixed input types"],"limitations":["Image resolution is internally downsampled; fine details in high-resolution images may be lost","Audio processing requires pre-conversion to supported formats (MP3, WAV, M4A); real-time streaming not supported","Cross-modal reasoning quality degrades with extremely long documents (>100 pages) due to context window constraints","No explicit control over which modality receives more attention during inference"],"requires":["OpenAI API key with GPT-4o access","Images in JPEG, PNG, GIF, or WebP format (max 20MB per image)","Audio files in MP3, MP4, MPEG, MPGA, M4A, WAV, or WebM format (max 25MB)","HTTP/2 capable client for streaming responses"],"input_types":["text (UTF-8, up to 128K tokens)","images (JPEG, PNG, GIF, WebP, multiple per request)","audio (MP3, WAV, M4A, WebM, MPEG, MP4, MPGA)"],"output_types":["text (streaming or complete)","structured JSON (via JSON mode)","function call arguments (via tool use)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_1","uri":"capability://memory.knowledge.128k.context.window.with.efficient.attention.mechanism","name":"128k context window with efficient attention mechanism","description":"GPT-4o implements a 128,000-token context window using optimized attention patterns (likely sparse or grouped-query attention variants) that reduce memory complexity from O(n²) to near-linear scaling. This enables processing of entire codebases, long documents, or multi-turn conversations without truncation. The model maintains coherence across the full context through learned positional embeddings that generalize beyond training sequence lengths.","intents":["I need to analyze an entire codebase file structure and dependencies in one request","I want to maintain conversation history across 50+ turns without losing context","I need to process a 100-page legal document and cross-reference sections","I want to summarize multiple research papers together with their relationships"],"best_for":["developers building code analysis and refactoring tools","teams creating long-form content generation systems (books, reports)","enterprises processing large document collections with semantic understanding"],"limitations":["Latency increases linearly with context size; 128K tokens may add 2-5 seconds vs 8K context","Cost scales with input tokens; long contexts significantly increase per-request pricing","Retrieval quality degrades in middle sections of very long contexts (lost-in-the-middle effect still present)","No explicit control over attention allocation; model may not weight early context appropriately for some tasks"],"requires":["OpenAI API key with GPT-4o access","Client capable of batching/streaming large token sequences","Sufficient API rate limits for large context requests"],"input_types":["text (up to 128,000 tokens)","images (processed as tokens, counted against context limit)","mixed text + image sequences"],"output_types":["text (up to 4,096 tokens per response)","structured JSON","function call arguments"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_10","uri":"capability://safety.moderation.safety.filtering.and.content.moderation.with.configurable.policies","name":"safety filtering and content moderation with configurable policies","description":"GPT-4o includes built-in safety mechanisms that filter harmful content, refuse unsafe requests, and provide explanations for refusals. The model is trained to decline requests for illegal activities, violence, abuse, and other harmful content. Safety filtering operates at inference time without requiring external moderation APIs. Applications can configure safety levels or override defaults for specific use cases.","intents":["I need to ensure LLM responses don't contain harmful content","I want to understand why the model refused a request","I need to build applications with appropriate safety guardrails","I want to handle edge cases where safety filtering may be too restrictive"],"best_for":["teams building consumer-facing LLM applications","enterprises with compliance requirements (healthcare, finance)","product teams that need to balance safety and utility"],"limitations":["Safety filtering may be overly restrictive for legitimate use cases (e.g., discussing violence in fiction)","No fine-grained control over which content categories are filtered","Refusals are sometimes vague; applications cannot always determine exact reason","Safety filtering is not foolproof; adversarial prompts may bypass guardrails","No built-in audit logging for safety decisions; requires external monitoring"],"requires":["OpenAI API key with GPT-4o access","Awareness of safety policies and refusal patterns","Error handling for refusal responses"],"input_types":["any text input (safety filtering applied)"],"output_types":["filtered responses","refusal messages with explanations","metadata indicating safety filtering applied"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_11","uri":"capability://automation.workflow.batch.processing.api.for.cost.optimized.inference","name":"batch processing api for cost-optimized inference","description":"GPT-4o supports batch processing through OpenAI's Batch API, where multiple requests are submitted together and processed asynchronously at lower cost (50% discount). Batches are processed in the background and results are retrieved via polling or webhooks. Ideal for non-time-sensitive workloads like data processing, content generation, and analysis at scale.","intents":["I need to process thousands of documents at lower cost","I want to generate content in bulk without real-time latency requirements","I need to analyze large datasets with LLM-based extraction","I want to optimize costs for non-urgent processing tasks"],"best_for":["teams processing large document collections","data engineering teams building ETL pipelines with LLM steps","enterprises with non-real-time processing needs"],"limitations":["Batch processing is asynchronous; results may take hours or days to complete","No real-time feedback or streaming responses in batch mode","Batch API has lower priority than standard API; may be delayed during high load","Minimum batch size requirements may apply; small batches don't benefit from discounts","No ability to cancel or modify batches after submission"],"requires":["OpenAI API key with batch API access","Batch requests formatted in JSONL (JSON Lines) format","Polling mechanism or webhook handler for result retrieval","Patience for asynchronous processing (hours to days)"],"input_types":["JSONL-formatted batch requests (multiple requests per file)"],"output_types":["JSONL-formatted batch results (asynchronous)","metadata including batch status and completion time"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_12","uri":"capability://code.generation.editing.vision.based.code.understanding.and.generation.from.screenshots","name":"vision-based code understanding and generation from screenshots","description":"GPT-4o can analyze screenshots of code, whiteboards, and diagrams to understand intent and generate corresponding code. The model extracts code from images, understands handwritten pseudocode, and generates implementation from visual designs. Enables workflows where developers can sketch ideas visually and have them converted to working code.","intents":["I want to convert a whiteboard sketch into working code","I need to understand and refactor code from a screenshot","I want to generate code from a UI mockup or design","I need to extract code from a PDF or scanned document"],"best_for":["developers using visual design tools and wanting code generation","teams with legacy codebases in image format (scans, PDFs)","educators teaching programming through visual examples"],"limitations":["Code extraction from images is less accurate than text input; formatting may be lost","Handwritten pseudocode recognition is approximate; complex notation may be misinterpreted","Generated code from visual designs may not match exact specifications; requires review","Image quality significantly affects accuracy; low-resolution or blurry images fail","No ability to preserve exact formatting or comments from original code"],"requires":["OpenAI API key with GPT-4o access","Images in JPEG, PNG, GIF, or WebP format (max 20MB)","High-quality images (>300 DPI for scanned documents)"],"input_types":["screenshots of code","whiteboard photos","UI mockups and designs","scanned documents with code"],"output_types":["extracted code","generated code from designs","code explanations"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_13","uri":"capability://text.generation.language.multi.turn.conversation.with.context.preservation.and.coherence","name":"multi-turn conversation with context preservation and coherence","description":"GPT-4o maintains conversation state across multiple turns, preserving context and building coherent narratives. The model tracks conversation history, remembers user preferences and constraints mentioned earlier, and generates responses that are consistent with prior exchanges. Supports up to 128K tokens of conversation history without losing coherence.","intents":["I need to build a chatbot that remembers conversation context across turns","I want to have a multi-turn dialogue where the model understands references to earlier statements","I need to maintain user preferences and constraints throughout a conversation","I want to build interactive applications where context accumulates naturally"],"best_for":["teams building conversational AI and chatbot applications","developers creating interactive tutoring or customer support systems","product teams building assistants with persistent context"],"limitations":["Context window is finite (128K tokens); very long conversations may exceed limits","Model may lose focus on early context in very long conversations (lost-in-the-middle effect)","No built-in persistence; conversation history must be managed by the application","Context is stateless per request; no server-side session management","Token costs accumulate with conversation length; long conversations become expensive"],"requires":["OpenAI API key with GPT-4o access","Application-level conversation history management","Message format: array of {role, content} objects"],"input_types":["conversation history (array of messages)","new user message","system context and instructions"],"output_types":["assistant response","token usage (input + output tokens)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_2","uri":"capability://tool.use.integration.native.function.calling.with.schema.based.argument.binding","name":"native function calling with schema-based argument binding","description":"GPT-4o includes built-in function calling via OpenAI's function schema format, where developers define tool signatures as JSON schemas and the model outputs structured function calls with validated arguments. The model learns to map natural language requests to appropriate functions and generate correctly-typed arguments without additional prompting. Supports parallel function calls (multiple tools invoked in single response) and automatic retry logic for invalid schemas.","intents":["I want to build an agent that calls APIs based on user requests without manual prompt engineering","I need to extract structured data (entities, relationships) from unstructured text reliably","I want to create a chatbot that can perform actions (send emails, create tickets, query databases)","I need to ensure function arguments are always valid JSON matching my schema"],"best_for":["teams building LLM agents and autonomous systems","developers creating data extraction pipelines with guaranteed schema compliance","product teams integrating LLMs into existing tool ecosystems"],"limitations":["Function calling works best with 2-10 tools; performance degrades with >20 function definitions","Model may hallucinate function calls not present in schema if prompt is ambiguous","No built-in error handling for failed function execution; requires external retry logic","Argument validation is schema-based only; semantic validation (e.g., 'email must be valid format') requires post-processing"],"requires":["OpenAI API key with GPT-4o access","Function schemas defined in OpenAI's format (JSON Schema subset)","Client library (Python, Node.js, etc.) supporting tools parameter"],"input_types":["text (natural language request)","function schema definitions (JSON)","images (can be analyzed to determine which function to call)"],"output_types":["function call objects with tool_use_id, function name, and arguments","text (if model chooses not to call a function)","parallel function calls (array of function objects)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_3","uri":"capability://data.processing.analysis.json.mode.with.guaranteed.schema.compliance","name":"json mode with guaranteed schema compliance","description":"GPT-4o's JSON mode constrains the output to valid JSON matching a provided schema, using constrained decoding (token-level filtering during generation) to ensure every output is parseable and schema-compliant. The model generates JSON directly without intermediate text, eliminating parsing errors and hallucinated fields. Supports nested objects, arrays, enums, and type constraints (string, number, boolean, null).","intents":["I need to extract structured data from text and guarantee valid JSON output","I want to generate configuration files or API payloads without post-processing","I need to ensure LLM output integrates directly into my database without validation","I want to create a data pipeline where LLM output is immediately consumable by downstream systems"],"best_for":["data engineering teams building ETL pipelines with LLM extraction","developers creating APIs that return LLM-generated structured data","teams building form-filling or document generation systems"],"limitations":["JSON mode may reduce output quality/creativity compared to unconstrained generation for some tasks","Schema must be provided upfront; dynamic schema generation not supported","Complex nested schemas (>5 levels deep) may cause the model to truncate or simplify output","Enum constraints are strict; model cannot generate values outside the provided set"],"requires":["OpenAI API key with GPT-4o access","JSON schema definition (JSON Schema format or simplified OpenAI format)","Client library supporting response_format parameter"],"input_types":["text (natural language request or document to extract from)","images (analyzed and converted to JSON-structured output)"],"output_types":["valid JSON matching provided schema","nested objects and arrays","typed primitives (string, number, boolean, null)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_4","uri":"capability://image.visual.vision.understanding.with.spatial.reasoning.and.ocr","name":"vision understanding with spatial reasoning and ocr","description":"GPT-4o processes images through a vision transformer backbone that extracts spatial features, object relationships, and text content. The model performs optical character recognition (OCR) natively without separate APIs, understanding text layout, tables, diagrams, and handwriting. Spatial reasoning enables the model to answer questions about object positions, sizes, and relationships within images. Supports multiple images per request with cross-image reasoning.","intents":["I need to extract text from screenshots, scans, and documents without a separate OCR service","I want to analyze charts, diagrams, and infographics to extract insights","I need to identify objects in images and answer questions about their spatial relationships","I want to process multiple images together and reason about relationships between them"],"best_for":["teams building document processing systems (invoices, receipts, forms)","developers creating accessibility tools that describe images","product teams analyzing user-generated content (screenshots, photos)"],"limitations":["OCR quality degrades on low-resolution images (<300 DPI) or unusual fonts","Spatial reasoning is approximate; precise measurements or pixel-level accuracy not guaranteed","Handwriting recognition works for printed/cursive text but fails on highly stylized writing","Image understanding is 2D; cannot reason about 3D structure from single images","Processing multiple images increases latency and token usage linearly"],"requires":["OpenAI API key with GPT-4o access","Images in JPEG, PNG, GIF, or WebP format (max 20MB per image)","Image resolution between 100x100 and 20,000x20,000 pixels"],"input_types":["images (JPEG, PNG, GIF, WebP)","text queries about images","multiple images per request"],"output_types":["text descriptions","extracted text (OCR)","structured data (via JSON mode)","spatial relationships and object locations"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_5","uri":"capability://text.generation.language.audio.transcription.and.understanding.with.speaker.identification","name":"audio transcription and understanding with speaker identification","description":"GPT-4o transcribes audio files to text while preserving speaker information, tone, and emotional context. The model identifies speaker changes, extracts dialogue, and understands speech content without requiring separate speech-to-text APIs. Supports multiple speakers and can answer questions about audio content (e.g., 'What did speaker 2 say about pricing?'). Audio is tokenized similarly to text, enabling efficient processing of long recordings.","intents":["I need to transcribe meeting recordings and extract action items automatically","I want to analyze customer support calls to identify sentiment and issues","I need to extract quotes from interviews while preserving speaker attribution","I want to build a system that understands audio content without separate transcription services"],"best_for":["teams building meeting intelligence and note-taking tools","customer success teams analyzing support call recordings","researchers transcribing interviews and focus groups"],"limitations":["Speaker identification works for 2-3 speakers clearly; degrades with >5 speakers or overlapping speech","Transcription accuracy depends on audio quality; background noise significantly reduces accuracy","Emotional tone detection is approximate; sarcasm and subtle emotions may be misinterpreted","Audio files must be pre-converted to supported formats; real-time streaming not supported","Long recordings (>1 hour) may exceed context window or incur high token costs"],"requires":["OpenAI API key with GPT-4o access","Audio files in MP3, WAV, M4A, WebM, MPEG, MP4, or MPGA format (max 25MB)","Audio sample rate between 8kHz and 48kHz"],"input_types":["audio files (MP3, WAV, M4A, WebM, MPEG, MP4, MPGA)","text queries about audio content"],"output_types":["transcribed text with speaker labels","structured data (JSON with speaker turns)","summaries and extracted insights","answers to questions about audio content"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"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 and completes code across 40+ programming languages using patterns learned from massive code corpora. The model understands syntax, semantics, and common idioms for each language, generating contextually appropriate code that follows language conventions. Supports generating entire functions, classes, or scripts from natural language descriptions. Achieves 90.2% on HumanEval benchmark, indicating strong code correctness.","intents":["I need to generate boilerplate code or scaffolding for a new project","I want to complete a function based on its signature and docstring","I need to convert code between languages or refactor existing code","I want to generate test cases or documentation for my code"],"best_for":["developers building code generation tools or IDE plugins","teams automating code scaffolding and boilerplate generation","educators creating coding tutorials and examples"],"limitations":["Generated code may have subtle bugs or security vulnerabilities; requires human review","Code generation quality varies by language; less common languages (Rust, Go) have lower accuracy than Python/JavaScript","Model may generate code that works but doesn't follow project-specific conventions or style guides","Large codebases (>100K lines) may exceed context window; requires chunking or summarization","No built-in linting or type checking; generated code may not pass CI/CD pipelines without modification"],"requires":["OpenAI API key with GPT-4o access","Code context (existing code, function signatures, docstrings) for better results","Client library supporting streaming for real-time code generation"],"input_types":["natural language descriptions of desired code","code snippets or function signatures","docstrings and comments","images of code (via vision capability)"],"output_types":["code in 40+ programming languages","complete functions, classes, or scripts","code explanations and documentation"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_7","uri":"capability://text.generation.language.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"GPT-4o demonstrates strong mathematical reasoning across algebra, calculus, statistics, and logic problems. The model can solve multi-step math problems, explain reasoning, and generate symbolic expressions. Achieves 88.7% on MMLU benchmark, indicating broad knowledge across domains. Supports generating LaTeX expressions and mathematical notation for precise communication.","intents":["I need to solve math problems and get step-by-step explanations","I want to generate mathematical expressions or formulas from descriptions","I need to verify mathematical correctness of student work or research","I want to build a tutoring system that explains mathematical concepts"],"best_for":["educational technology teams building tutoring platforms","researchers verifying mathematical correctness in papers","teams building scientific computing tools with natural language interfaces"],"limitations":["Mathematical reasoning is probabilistic; complex proofs may have logical gaps or errors","Symbolic computation is limited; cannot perform arbitrary algebraic manipulations like Mathematica/SymPy","Numerical precision is limited to floating-point accuracy; high-precision arithmetic not supported","Model may struggle with novel or highly specialized mathematical domains","No built-in verification; generated proofs require human review"],"requires":["OpenAI API key with GPT-4o access","Mathematical expressions in text, LaTeX, or image format"],"input_types":["natural language math problems","LaTeX expressions","images of handwritten or printed math","numerical data for statistical analysis"],"output_types":["step-by-step solutions","LaTeX expressions","explanations and reasoning","numerical results"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_8","uri":"capability://text.generation.language.real.time.streaming.responses.with.token.level.control","name":"real-time streaming responses with token-level control","description":"GPT-4o supports streaming responses where tokens are sent to the client as they are generated, enabling real-time feedback and lower perceived latency. The API streams both text tokens and function calls, allowing clients to process partial results immediately. Streaming reduces time-to-first-token (TTFT) and enables interactive applications like chatbots and live code generation.","intents":["I need to build a chatbot with real-time response streaming for better UX","I want to display code generation results as they are produced","I need to reduce perceived latency in interactive applications","I want to process partial results from long-running requests"],"best_for":["teams building interactive chatbot interfaces","developers creating IDE plugins or code generation tools","product teams optimizing perceived latency in LLM applications"],"limitations":["Streaming adds complexity to client-side code (handling partial tokens, buffering)","Function calls are streamed as JSON fragments; requires client-side parsing and buffering","Token-level streaming may expose model reasoning or intermediate thoughts (privacy consideration)","Streaming responses cannot be retried mid-stream; requires full request restart","Some operations (e.g., JSON mode validation) may require buffering entire response"],"requires":["OpenAI API key with GPT-4o access","HTTP/2 or HTTP/1.1 with chunked transfer encoding support","Client library supporting streaming (Python, Node.js, etc.)","Event handling for stream events (token, function_call, done)"],"input_types":["text (same as non-streaming)","images (same as non-streaming)"],"output_types":["streamed text tokens (delta format)","streamed function calls (partial JSON)","metadata (finish_reason, usage)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__cap_9","uri":"capability://text.generation.language.knowledge.cutoff.and.temporal.reasoning.with.date.awareness","name":"knowledge cutoff and temporal reasoning with date awareness","description":"GPT-4o has a knowledge cutoff date (April 2024) and is aware of the current date during inference, enabling it to reason about temporal relationships and provide time-aware responses. The model can calculate time differences, understand historical context, and avoid providing outdated information. Date awareness is passed via system context, allowing applications to control temporal reasoning.","intents":["I need the model to understand current date and avoid outdated information","I want to ask questions about events relative to today (e.g., 'What happened 5 years ago?')","I need to generate time-aware content (e.g., 'What are current best practices for X?')","I want to build applications that adapt responses based on temporal context"],"best_for":["teams building news aggregation or current events applications","developers creating time-sensitive applications (scheduling, planning)","product teams that need to acknowledge knowledge limitations"],"limitations":["Knowledge cutoff (April 2024) means no information about events after that date","Temporal reasoning is approximate; complex time calculations may have errors","Model may confuse historical dates or provide outdated information for rapidly evolving fields","Date awareness requires explicit system context; not automatic without configuration","No access to real-time information; cannot fetch current news or data"],"requires":["OpenAI API key with GPT-4o access","System message including current date (e.g., 'Today is December 2024')","Client responsible for providing accurate current date"],"input_types":["text queries with temporal references","system context with current date"],"output_types":["time-aware responses","temporal reasoning and calculations","acknowledgment of knowledge limitations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4o__headline","uri":"capability://llm.apis.multimodal.ai.model.for.production.applications","name":"multimodal ai model for production applications","description":"OpenAI's GPT-4o is a cutting-edge multimodal AI model that integrates text, vision, and audio capabilities, designed for high-performance production use with a vast context window and rapid inference speeds.","intents":["best multimodal AI model","multimodal model for production applications","fast AI model for text and vision","top-performing AI model for coding and analysis","GPT-4o vs other multimodal models"],"best_for":["production applications","high-performance tasks"],"limitations":[],"requires":[],"input_types":["text","image","audio"],"output_types":["text","structured data"],"categories":["llm-apis","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":81,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-4o access","Images in JPEG, PNG, GIF, or WebP format (max 20MB per image)","Audio files in MP3, MP4, MPEG, MPGA, M4A, WAV, or WebM format (max 25MB)","HTTP/2 capable client for streaming responses","Client capable of batching/streaming large token sequences","Sufficient API rate limits for large context requests","Awareness of safety policies and refusal patterns","Error handling for refusal responses","OpenAI API key with batch API access","Batch requests formatted in JSONL (JSON Lines) format"],"failure_modes":["Image resolution is internally downsampled; fine details in high-resolution images may be lost","Audio processing requires pre-conversion to supported formats (MP3, WAV, M4A); real-time streaming not supported","Cross-modal reasoning quality degrades with extremely long documents (>100 pages) due to context window constraints","No explicit control over which modality receives more attention during inference","Latency increases linearly with context size; 128K tokens may add 2-5 seconds vs 8K context","Cost scales with input tokens; long contexts significantly increase per-request pricing","Retrieval quality degrades in middle sections of very long contexts (lost-in-the-middle effect still present)","No explicit control over attention allocation; model may not weight early context appropriately for some tasks","Safety filtering may be overly restrictive for legitimate use cases (e.g., discussing violence in fiction)","No fine-grained control over which content categories are filtered","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"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":"2026-05-24T12:16:22.066Z","last_scraped_at":null,"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=gpt-4o","compare_url":"https://unfragile.ai/compare?artifact=gpt-4o"}},"signature":"ubnjFYrHDTDfWFGpVALlipDTKOYr5FZWci1SPJ0jdsqIWZEj2sFPp1GPaT67a1YMpJ4+HfPFhlxck07cfr9jDw==","signedAt":"2026-06-22T18:00:29.456Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gpt-4o","artifact":"https://unfragile.ai/gpt-4o","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}