{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-5","slug":"openai-gpt-5","name":"OpenAI: GPT-5","type":"model","url":"https://openrouter.ai/models/openai~gpt-5","page_url":"https://unfragile.ai/openai-gpt-5","categories":["model-training","testing-quality"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.25e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-5__cap_0","uri":"capability://planning.reasoning.multi.step.reasoning.with.chain.of.thought.decomposition","name":"multi-step reasoning with chain-of-thought decomposition","description":"GPT-5 implements advanced chain-of-thought reasoning that breaks complex problems into intermediate reasoning steps before generating final answers. The model uses transformer-based attention mechanisms to maintain coherence across multi-step logical sequences, enabling it to handle problems requiring sequential inference, mathematical reasoning, and logical deduction without explicit prompt engineering for step-by-step thinking.","intents":["I need to solve a complex math problem that requires multiple reasoning steps","I want the model to show its work and explain how it arrived at a conclusion","I'm building an agent that needs to decompose ambiguous user requests into actionable sub-tasks","I need reliable reasoning for scientific or technical problem-solving"],"best_for":["AI researchers and engineers building reasoning-heavy applications","Teams developing autonomous agents requiring multi-step planning","Educational platforms needing explainable AI outputs","Enterprise applications with complex domain logic"],"limitations":["Reasoning depth is bounded by context window (likely 128K tokens); very long chains may lose coherence","Latency increases with reasoning complexity — multi-step problems may require 5-15 seconds vs <1 second for simple queries","No guaranteed deterministic reasoning paths — same problem may be solved via different logical routes","Reasoning quality degrades on highly specialized domains without domain-specific fine-tuning"],"requires":["OpenAI API key with GPT-5 access","HTTP/REST client or OpenAI SDK (Python 3.8+, Node.js 14+, etc.)","Network connectivity to OpenAI endpoints","Sufficient API quota and billing setup"],"input_types":["natural language text","mathematical expressions","code snippets for analysis","logical puzzles or constraints"],"output_types":["natural language explanation with reasoning steps","structured reasoning traces (if parsed from output)","final answer with confidence indicators"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_1","uri":"capability://code.generation.editing.code.generation.with.multi.language.support.and.context.awareness","name":"code generation with multi-language support and context awareness","description":"GPT-5 generates production-quality code across 40+ programming languages by leveraging transformer-based code understanding trained on diverse codebases. It maintains context awareness of existing code patterns, imports, and architectural conventions within a project, enabling it to generate code that integrates seamlessly with existing implementations rather than producing isolated snippets.","intents":["I need to generate a function that follows my existing codebase's style and patterns","I want to scaffold a new module or service with boilerplate that matches my tech stack","I need to refactor legacy code while maintaining API compatibility","I'm building a code generation pipeline for infrastructure-as-code templates"],"best_for":["Full-stack developers accelerating feature development","DevOps engineers generating infrastructure code (Terraform, CloudFormation, Kubernetes)","Teams migrating between frameworks or languages","Startups with small engineering teams needing rapid prototyping"],"limitations":["Generated code may contain subtle bugs in edge cases — requires human review and testing before production deployment","Context window limits prevent analyzing very large codebases (>100K lines) in a single request","No built-in knowledge of proprietary internal libraries or custom frameworks without explicit documentation in prompts","Security vulnerabilities in generated code are possible — OWASP-class issues like SQL injection or hardcoded credentials require linting and security scanning"],"requires":["OpenAI API key with GPT-5 model access","Programming language runtime/compiler for testing generated code","Code linting and testing infrastructure (pytest, Jest, ESLint, etc.)","Version control system for tracking generated code changes"],"input_types":["natural language specifications or requirements","existing code snippets or file context","architecture diagrams or design documents","test cases or expected behavior descriptions"],"output_types":["source code in target language","complete modules or classes","configuration files (YAML, JSON, HCL)","test cases and documentation"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_10","uri":"capability://text.generation.language.few.shot.learning.with.in.context.examples","name":"few-shot learning with in-context examples","description":"GPT-5 learns from examples provided in the prompt (few-shot learning) without requiring fine-tuning, enabling it to adapt to new tasks by demonstrating desired behavior through examples. The model uses attention mechanisms to identify patterns in examples and apply them to new inputs, enabling rapid task adaptation for custom formats, styles, or domain-specific requirements.","intents":["I need the model to follow a specific format or style that I can demonstrate with examples","I want to adapt the model to a custom task without fine-tuning or retraining","I need to generate outputs in a domain-specific format (e.g., medical notes, legal documents)","I'm building a system where users can customize model behavior through examples"],"best_for":["Teams building customizable AI systems without fine-tuning infrastructure","Developers prototyping new use cases quickly","Applications requiring domain-specific output formats","Systems where users need to adapt model behavior without technical expertise"],"limitations":["Few-shot learning quality depends on example quality — poor examples lead to poor outputs","Limited to 3-10 examples before context window becomes constrained","Learning from examples is less reliable than fine-tuning for complex tasks","Examples consume tokens, increasing API costs and latency"],"requires":["OpenAI API key with GPT-5 access","High-quality examples demonstrating desired behavior","Prompt engineering to present examples clearly","Validation logic to verify outputs match expected format"],"input_types":["examples of desired input-output pairs","new inputs to apply learned patterns to"],"output_types":["outputs following patterns demonstrated in examples","formatted or styled text matching example format"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_11","uri":"capability://data.processing.analysis.semantic.understanding.with.entity.and.relationship.extraction","name":"semantic understanding with entity and relationship extraction","description":"GPT-5 extracts entities (people, places, concepts) and relationships between them from unstructured text, enabling it to build knowledge graphs or structured representations of document content. The model uses transformer-based sequence labeling and relation classification to identify semantic structures without requiring explicit training on domain-specific entity types.","intents":["I need to extract named entities and relationships from documents to build a knowledge graph","I want to identify key concepts and their relationships in research papers or articles","I need to structure unstructured text into a database of entities and relationships","I'm building a system that needs to understand semantic relationships in user queries"],"best_for":["Knowledge graph construction and semantic search systems","Document analysis and information extraction pipelines","Research tools analyzing scientific literature","Enterprise systems building structured data from unstructured documents"],"limitations":["Entity extraction may miss domain-specific entities without explicit guidance","Relationship extraction is approximate and may identify spurious relationships","Coreference resolution (linking pronouns to entities) can fail on complex documents","No guarantee of completeness — some entities or relationships may be missed"],"requires":["OpenAI API key with GPT-5 access","Entity and relationship type definitions (if domain-specific)","Knowledge graph database or structured storage for extracted data","Validation and deduplication logic for extracted entities"],"input_types":["unstructured text documents","entity type definitions (optional)","relationship type definitions (optional)"],"output_types":["list of extracted entities with types","list of relationships between entities","structured knowledge graph representation"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_2","uri":"capability://text.generation.language.instruction.following.with.nuanced.constraint.handling","name":"instruction-following with nuanced constraint handling","description":"GPT-5 implements improved instruction-following through enhanced training on diverse instruction types, enabling it to parse complex, multi-part directives with conditional logic, edge cases, and conflicting constraints. The model uses attention mechanisms to weight different instruction components and resolve ambiguities through contextual reasoning rather than simple pattern matching.","intents":["I need the model to follow a complex set of formatting rules and output constraints simultaneously","I want to specify conditional logic in my prompt (e.g., 'if X then do Y, else do Z')","I'm building a system where users provide detailed specifications that must be followed exactly","I need the model to refuse requests that violate specific safety or business rules I define"],"best_for":["Enterprise teams building custom AI workflows with strict compliance requirements","Content platforms requiring consistent formatting and moderation rules","Researchers studying instruction-following and alignment in LLMs","Developers building domain-specific AI assistants with specialized constraints"],"limitations":["Instruction-following degrades with extremely long or contradictory constraint sets (>20 independent rules may cause conflicts)","No formal verification that constraints are satisfied — requires post-generation validation","Ambiguous or poorly-written instructions may be misinterpreted despite improved instruction-following","Adversarial prompts can still cause instruction violations through jailbreak techniques"],"requires":["OpenAI API key with GPT-5 access","Clear, well-structured prompt engineering with explicit constraints","Output validation logic to verify constraint compliance","Monitoring and logging infrastructure to track instruction-following failures"],"input_types":["natural language instructions with multiple constraints","structured prompt templates with conditional logic","domain-specific rules or policies","examples of desired vs undesired outputs"],"output_types":["text conforming to specified constraints","structured data following defined schemas","refusals or error messages when constraints cannot be met"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_3","uri":"capability://image.visual.image.understanding.and.visual.reasoning","name":"image understanding and visual reasoning","description":"GPT-5 integrates vision capabilities through a multimodal transformer architecture that processes both image and text tokens, enabling it to analyze images, answer questions about visual content, perform OCR, and reason about spatial relationships. The model uses cross-modal attention mechanisms to ground language understanding in visual features extracted from images.","intents":["I need to extract text from images or documents (OCR with context understanding)","I want to analyze charts, diagrams, or screenshots and extract insights","I need to answer questions about the content or composition of images","I'm building a system that needs to understand visual layouts or spatial relationships in UI screenshots"],"best_for":["Document processing and data extraction workflows","Accessibility tools converting visual content to text descriptions","Quality assurance teams analyzing UI screenshots and design mockups","Research teams studying multimodal reasoning and vision-language models"],"limitations":["Image resolution is limited by token budget — very high-resolution images (>4K) may lose detail or require downsampling","OCR accuracy degrades on handwritten text, non-Latin scripts, or heavily stylized fonts","No real-time video processing — only static image analysis per frame","Spatial reasoning is approximate and may fail on complex 3D scenes or ambiguous perspectives"],"requires":["OpenAI API key with GPT-5 vision capabilities enabled","Image input in supported formats (JPEG, PNG, WebP, GIF)","Image preprocessing pipeline for format conversion and optimization","HTTP client supporting multipart form data or base64 image encoding"],"input_types":["JPEG, PNG, WebP, GIF images","screenshots and UI mockups","charts, graphs, and diagrams","documents and scanned pages","natural language questions about images"],"output_types":["text descriptions and captions","extracted text (OCR output)","structured data extracted from charts or tables","answers to visual questions","spatial relationship descriptions"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_4","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.orchestration","name":"function calling with schema-based tool orchestration","description":"GPT-5 implements function calling through a schema-based interface where developers define tool signatures as JSON schemas, and the model generates structured function calls that can be executed by external systems. The model uses attention mechanisms to select appropriate tools based on user intent and generate valid arguments that conform to the schema, enabling integration with APIs, databases, and custom business logic.","intents":["I need the model to call external APIs or functions to fetch real-time data or perform actions","I want to build an agent that can use a set of predefined tools to accomplish complex tasks","I need to integrate GPT-5 with my existing backend services and databases","I'm building a system where the model should decide which tools to use based on user requests"],"best_for":["Teams building AI agents with access to external APIs and databases","Enterprise applications requiring integration with legacy systems","Developers creating specialized AI assistants for domain-specific tasks","Startups building AI-powered automation workflows"],"limitations":["Function calling is non-deterministic — the model may choose suboptimal tools or generate invalid arguments requiring retry logic","Schema complexity is bounded by context window — very large tool sets (>50 tools) may degrade selection accuracy","No built-in transaction semantics — failed function calls require explicit error handling and recovery logic","Tool execution latency is additive — each function call adds network round-trip time, making multi-step workflows slow"],"requires":["OpenAI API key with function calling support","JSON schema definitions for all available tools","Backend service or API to execute generated function calls","Error handling and retry logic for failed function executions","Logging and monitoring to track tool usage and failures"],"input_types":["natural language user requests","JSON schema definitions for available tools","context about available functions and their parameters"],"output_types":["structured function calls with arguments","tool selection decisions","function execution results (if integrated with execution layer)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_5","uri":"capability://memory.knowledge.long.context.understanding.with.128k.token.window","name":"long-context understanding with 128k token window","description":"GPT-5 processes extended context windows up to 128,000 tokens, enabling it to analyze entire documents, codebases, or conversation histories without summarization or chunking. The model uses efficient attention mechanisms (likely sparse or hierarchical attention) to maintain performance while processing long sequences, allowing it to maintain coherence and reference information across large documents.","intents":["I need to analyze a full research paper or technical specification without losing context","I want to process an entire codebase or multiple files in a single request","I need to maintain conversation history across many turns without losing early context","I'm building a system that needs to search and reason over large document collections"],"best_for":["Legal and compliance teams analyzing lengthy contracts or regulations","Researchers processing full academic papers and datasets","Developers refactoring large codebases or understanding complex systems","Customer support teams handling detailed multi-turn conversations"],"limitations":["API latency increases with context length — 128K token requests may take 10-30 seconds vs <1 second for short prompts","Cost scales linearly with input tokens — processing large documents is significantly more expensive than short queries","Attention mechanisms may still lose information from very early context (first 10K tokens) due to attention distribution","Model performance may degrade on tasks requiring precise recall of information from the middle of very long contexts"],"requires":["OpenAI API key with access to GPT-5's extended context window","Sufficient API quota and billing budget for high token usage","Text preprocessing to convert documents into token-compatible formats","Monitoring of token usage to manage costs and latency"],"input_types":["full documents (PDFs, markdown, plain text)","multiple code files or entire repositories","long conversation histories","large datasets or knowledge bases"],"output_types":["analysis and summaries of long documents","code refactoring suggestions across multiple files","answers to questions about document content","structured extraction from long documents"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_6","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"GPT-5 can generate structured outputs that conform to specified JSON schemas, enabling it to produce machine-readable data suitable for downstream processing. The model uses constrained decoding or guided generation to ensure output conforms to the schema, preventing invalid JSON or missing required fields that would require post-processing or error handling.","intents":["I need the model to extract structured data from unstructured text and return it as JSON","I want to generate API responses or database records with guaranteed schema compliance","I need to build a pipeline where model outputs feed directly into downstream systems without parsing","I'm building a system that needs to extract entities, relationships, or metadata in a structured format"],"best_for":["Data extraction and ETL pipeline builders","Teams building API backends powered by LLMs","Developers creating knowledge graph construction systems","Enterprise applications requiring structured data generation"],"limitations":["Schema complexity is bounded — very large or deeply nested schemas may cause generation failures","Constrained decoding adds latency (5-15% overhead) compared to unconstrained generation","Model may refuse to generate output if it cannot satisfy all required fields, requiring fallback logic","Complex validation rules (e.g., cross-field dependencies) cannot be expressed in JSON schema and require post-processing"],"requires":["OpenAI API key with structured output support","JSON schema definition for desired output format","Schema validation library to verify compliance","Error handling for cases where model cannot generate valid output"],"input_types":["natural language text to extract from","JSON schema definitions","examples of desired output format"],"output_types":["JSON objects conforming to specified schema","arrays of structured records","nested objects with validated field types"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_7","uri":"capability://planning.reasoning.knowledge.cutoff.awareness.and.temporal.reasoning","name":"knowledge cutoff awareness and temporal reasoning","description":"GPT-5 maintains awareness of its knowledge cutoff date and can reason about temporal information, enabling it to acknowledge when information may be outdated and distinguish between facts from its training data versus current events. The model uses temporal tokens and positional embeddings to understand time-relative concepts and can reason about causality and temporal sequences.","intents":["I need the model to acknowledge when it doesn't have current information about recent events","I want to build a system that can reason about historical timelines and temporal relationships","I need the model to understand that some information may be outdated and suggest verification","I'm building an application where temporal context matters (e.g., stock prices, news, regulations)"],"best_for":["News and media applications requiring current information","Financial and investment platforms where timeliness is critical","Research tools where distinguishing old vs new information matters","Systems requiring temporal reasoning or historical analysis"],"limitations":["Knowledge cutoff is fixed (likely April 2024 or later) — no automatic updates as new information becomes available","Temporal reasoning is approximate and may fail on complex time-dependent logic","Model cannot access real-time data without integration with external APIs or search tools","Reasoning about future events is speculative and should not be relied upon for predictions"],"requires":["OpenAI API key with GPT-5 access","Integration with external data sources for current information (web search, APIs, databases)","Prompt engineering to explicitly request temporal awareness","Fact-checking and verification infrastructure for time-sensitive claims"],"input_types":["questions about current events or recent information","historical timelines or temporal sequences","time-dependent reasoning problems"],"output_types":["acknowledgments of knowledge cutoff limitations","temporal reasoning and timeline analysis","suggestions for verification of time-sensitive information"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_8","uri":"capability://text.generation.language.multilingual.generation.and.translation.with.cultural.context","name":"multilingual generation and translation with cultural context","description":"GPT-5 generates and translates text across 100+ languages while maintaining cultural context, idioms, and nuance. The model uses language-specific tokenization and attention mechanisms to preserve meaning across linguistic boundaries, enabling it to adapt tone, formality, and cultural references appropriately for target audiences rather than producing literal word-for-word translations.","intents":["I need to translate content while preserving cultural context and local idioms","I want to generate content in multiple languages from a single prompt","I need to localize applications or content for different markets and cultures","I'm building a system that serves users in multiple languages with culturally appropriate responses"],"best_for":["Global companies localizing products and content","Translation agencies augmenting human translators","Multilingual customer support systems","International research teams collaborating across languages"],"limitations":["Translation quality varies by language pair — low-resource languages (e.g., Swahili, Icelandic) have lower accuracy than high-resource pairs (English-Spanish)","Cultural context is inferred from training data and may not reflect current cultural norms or sensitivities","Idioms and wordplay often cannot be translated without losing meaning","Specialized terminology in non-English languages may be mistranslated without domain-specific training"],"requires":["OpenAI API key with multilingual support","Language identification logic to detect input language","Cultural context or style guides for target languages","Human review for high-stakes translations (legal, medical, marketing)"],"input_types":["text in any supported language","translation requests with target language specification","localization briefs with cultural guidelines"],"output_types":["translated text in target language","culturally adapted content","multilingual responses"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5__cap_9","uri":"capability://safety.moderation.safety.filtering.and.harmful.content.detection","name":"safety filtering and harmful content detection","description":"GPT-5 implements multiple layers of safety mechanisms including input filtering, output moderation, and refusal logic to prevent generation of harmful content. The model uses classifiers trained on harmful content categories to detect and refuse requests for illegal activities, violence, hate speech, sexual content involving minors, and other policy violations, with transparent explanations of why requests are refused.","intents":["I need to ensure the model refuses to generate illegal or harmful content","I want to understand why the model refused a particular request","I need to build a system with configurable safety policies for different use cases","I'm building a platform where user safety is critical and I need to prevent abuse"],"best_for":["Consumer-facing applications with broad user bases","Platforms with strict content policies and moderation requirements","Organizations with compliance obligations (GDPR, CCPA, etc.)","Educational and child-safe applications"],"limitations":["Safety filtering may refuse legitimate requests (false positives) — e.g., refusing to discuss historical atrocities or medical topics","Adversarial prompts and jailbreak attempts can sometimes bypass safety mechanisms","Safety policies are fixed by OpenAI and cannot be customized per application","Refusal explanations may leak information about safety mechanisms that could be exploited"],"requires":["OpenAI API key with safety features enabled","Monitoring and logging of refusals to identify patterns","User communication strategy for explaining refusals","Feedback mechanisms to report false positives or safety issues"],"input_types":["any user input (text, code, images)","requests that may violate content policies"],"output_types":["refusal messages with explanations","filtered or modified outputs","safety violation classifications"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-5 access","HTTP/REST client or OpenAI SDK (Python 3.8+, Node.js 14+, etc.)","Network connectivity to OpenAI endpoints","Sufficient API quota and billing setup","OpenAI API key with GPT-5 model access","Programming language runtime/compiler for testing generated code","Code linting and testing infrastructure (pytest, Jest, ESLint, etc.)","Version control system for tracking generated code changes","High-quality examples demonstrating desired behavior","Prompt engineering to present examples clearly"],"failure_modes":["Reasoning depth is bounded by context window (likely 128K tokens); very long chains may lose coherence","Latency increases with reasoning complexity — multi-step problems may require 5-15 seconds vs <1 second for simple queries","No guaranteed deterministic reasoning paths — same problem may be solved via different logical routes","Reasoning quality degrades on highly specialized domains without domain-specific fine-tuning","Generated code may contain subtle bugs in edge cases — requires human review and testing before production deployment","Context window limits prevent analyzing very large codebases (>100K lines) in a single request","No built-in knowledge of proprietary internal libraries or custom frameworks without explicit documentation in prompts","Security vulnerabilities in generated code are possible — OWASP-class issues like SQL injection or hardcoded credentials require linting and security scanning","Few-shot learning quality depends on example quality — poor examples lead to poor outputs","Limited to 3-10 examples before context window becomes constrained","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.37,"match_graph":0.25,"freshness":0.9,"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-5","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-5"}},"signature":"BBhhGAWhikMD2nDbjMjcRX214wSjsbdNw5lU504trAPtih3vYv/rmXaT5nnJ3cLBdJmmyKhHZ/jOruB26H5aAg==","signedAt":"2026-06-16T01:51:43.541Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-5","artifact":"https://unfragile.ai/openai-gpt-5","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-5","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"}}