{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-5-chat","slug":"openai-gpt-5-chat","name":"OpenAI: GPT-5 Chat","type":"model","url":"https://openrouter.ai/models/openai~gpt-5-chat","page_url":"https://unfragile.ai/openai-gpt-5-chat","categories":["chatbots-assistants"],"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-chat__cap_0","uri":"capability://text.generation.language.multimodal.context.aware.conversation.with.vision.understanding","name":"multimodal context-aware conversation with vision understanding","description":"Processes both text and image inputs within a single conversation thread, maintaining full context across turns. The model uses a unified transformer architecture that encodes images through a vision encoder and text through a language model, merging representations at intermediate layers to enable cross-modal reasoning. This allows the model to reference visual elements in follow-up text queries and vice versa without losing conversation history.","intents":["I need to ask questions about an image and have follow-up conversations that reference both the image and previous text exchanges","I want to build a chatbot that understands screenshots, diagrams, and charts alongside natural language queries","I need to analyze documents with mixed text and visual content in a single conversation flow"],"best_for":["enterprise teams building document analysis and knowledge work applications","developers creating customer support bots that handle visual issues (UI bugs, product photos)","teams building internal tools for data interpretation and visual reasoning"],"limitations":["Image resolution and aspect ratio constraints may require preprocessing; very high-resolution images are downsampled internally","Context window limits mean very long conversations with many images may require summarization or context pruning","No real-time video processing — only static image frames supported per turn"],"requires":["OpenAI API key with GPT-5 Chat model access","HTTP client capable of multipart form data (for image uploads)","Images in JPEG, PNG, WebP, or GIF format"],"input_types":["text","image (JPEG, PNG, WebP, GIF)"],"output_types":["text","structured reasoning with citations to visual elements"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-chat__cap_1","uri":"capability://text.generation.language.enterprise.grade.conversation.with.extended.context.window","name":"enterprise-grade conversation with extended context window","description":"Supports extended context windows (128K+ tokens) enabling multi-turn conversations with substantial document analysis, code review, or knowledge base integration. The model uses sliding window attention with KV-cache optimization to manage memory efficiently across long sequences, allowing developers to maintain conversation state without explicit summarization or context management overhead.","intents":["I need to have long conversations that reference entire codebases, documents, or knowledge bases without losing context","I want to build a chatbot that can handle complex, multi-step reasoning across dozens of turns","I need to analyze large documents or logs in a single conversation without chunking or summarization"],"best_for":["enterprise applications requiring sustained multi-turn reasoning (legal review, technical documentation analysis)","developers building code analysis tools that need to reference entire repositories in context","teams implementing RAG systems where the model needs to reason over large retrieved document sets"],"limitations":["Latency increases with context window size; 128K token conversations may have 2-5x higher response times than short contexts","Cost scales linearly with input tokens, making very long conversations expensive at scale","Attention computation is O(n²) even with optimizations, creating practical limits around 128K tokens"],"requires":["OpenAI API key with GPT-5 Chat model access","HTTP client with support for large request payloads (>10MB)","Timeout configuration of 60+ seconds for long-context requests"],"input_types":["text","image"],"output_types":["text","structured analysis with cross-references to input context"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-chat__cap_2","uri":"capability://text.generation.language.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Generates responses constrained to user-defined JSON schemas, ensuring outputs conform to expected structure without post-processing. The model uses constrained decoding (token-level masking during generation) to enforce schema compliance at generation time, preventing invalid outputs and eliminating the need for retry loops or validation layers.","intents":["I need the model to always return valid JSON matching my application's data model","I want to extract structured data from unstructured text without writing parsing logic","I need to build reliable APIs where the model output directly feeds into downstream systems"],"best_for":["developers building data extraction pipelines that feed into databases or APIs","teams implementing LLM-powered form filling or data entry automation","enterprises requiring deterministic output formats for compliance or integration"],"limitations":["Schema complexity affects generation speed; deeply nested schemas with many conditional fields may reduce throughput by 20-40%","Constrained decoding prevents the model from expressing uncertainty or edge cases that don't fit the schema","Very large schemas (>500 fields) may cause token overhead and slower generation"],"requires":["OpenAI API key with GPT-5 Chat model access","JSON Schema definition provided in API request","Understanding of JSON Schema specification (draft 2020-12)"],"input_types":["text","image"],"output_types":["JSON (validated against provided schema)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-chat__cap_3","uri":"capability://tool.use.integration.function.calling.with.multi.provider.schema.registry","name":"function calling with multi-provider schema registry","description":"Enables the model to invoke external tools and APIs through a standardized function-calling interface. The model receives a list of available functions with parameter schemas, decides when to call them based on user intent, and returns structured function calls that applications can execute. This is implemented via a dedicated token stream for function calls, allowing parallel function invocation and native integration with OpenAI's function-calling API.","intents":["I need the model to decide when and how to call external APIs or tools to fulfill user requests","I want to build an AI agent that can search the web, query databases, or trigger workflows","I need to create a chatbot that can perform actions (send emails, create tickets, update records) based on conversation"],"best_for":["developers building AI agents that orchestrate multiple tools and APIs","teams implementing autonomous workflows triggered by natural language","enterprises building customer-facing bots that need to take actions (booking, payments, support)"],"limitations":["Function calling adds latency (~100-200ms) due to additional model inference for decision-making","The model may hallucinate function calls or parameters if schemas are ambiguous or poorly documented","Parallel function calls are limited to ~10 concurrent calls per request; more requires batching"],"requires":["OpenAI API key with GPT-5 Chat model access","Function definitions in OpenAI's function schema format (JSON Schema subset)","Application-level handler to execute returned function calls and return results"],"input_types":["text","image"],"output_types":["function calls (structured JSON with function name and parameters)","text (if model chooses not to call functions)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-chat__cap_4","uri":"capability://text.generation.language.few.shot.learning.with.in.context.examples","name":"few-shot learning with in-context examples","description":"Adapts model behavior through examples provided in the conversation context without fine-tuning. The model uses in-context learning to recognize patterns from provided examples and apply them to new inputs, enabling rapid customization for domain-specific tasks, writing styles, or output formats. This is implemented through standard conversation turns where examples are provided as user-assistant pairs.","intents":["I need the model to adopt a specific writing style or tone based on examples I provide","I want to teach the model domain-specific terminology or classification schemes through examples","I need to customize the model's behavior for a specific task without training or fine-tuning"],"best_for":["developers prototyping custom behaviors before committing to fine-tuning","teams building domain-specific applications (legal, medical, technical) that need specialized language","enterprises requiring rapid customization without ML infrastructure"],"limitations":["In-context learning is less reliable than fine-tuning for complex patterns; typically requires 3-10 high-quality examples","Examples consume tokens from the context window, reducing space for actual user queries","Performance degrades with very long or complex examples; optimal example length is 50-200 tokens each"],"requires":["OpenAI API key with GPT-5 Chat model access","Well-crafted examples demonstrating desired behavior (3-10 examples recommended)","Understanding of prompt engineering principles for effective example selection"],"input_types":["text","image"],"output_types":["text (adapted to example patterns)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-chat__cap_5","uri":"capability://planning.reasoning.natural.language.reasoning.with.chain.of.thought.decomposition","name":"natural language reasoning with chain-of-thought decomposition","description":"Generates step-by-step reasoning chains that break down complex problems into intermediate steps before arriving at conclusions. The model uses extended token generation to produce verbose reasoning traces, enabling transparency into decision-making and improving accuracy on multi-step logical problems. This is implemented through standard text generation with longer output sequences and explicit reasoning prompts.","intents":["I need the model to show its reasoning for complex decisions so I can verify correctness","I want to improve accuracy on math, logic, or multi-step reasoning tasks","I need to debug why the model arrived at a particular conclusion"],"best_for":["developers building systems that require explainable AI (compliance, healthcare, finance)","teams solving complex reasoning problems (math, logic puzzles, code analysis)","enterprises needing audit trails for AI-driven decisions"],"limitations":["Chain-of-thought reasoning increases token consumption by 3-5x, raising costs proportionally","Longer reasoning chains increase latency by 2-3x compared to direct answers","The model may produce plausible-sounding but incorrect reasoning; reasoning transparency doesn't guarantee correctness"],"requires":["OpenAI API key with GPT-5 Chat model access","Explicit prompting for chain-of-thought (e.g., 'Let's think step by step')","Increased timeout and token limits for longer responses"],"input_types":["text","image"],"output_types":["text (with intermediate reasoning steps followed by final answer)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-chat__cap_6","uri":"capability://memory.knowledge.conversation.memory.management.with.system.prompts.and.context.control","name":"conversation memory management with system prompts and context control","description":"Manages conversation state through system prompts that define model behavior and explicit context windows that control which previous turns are included in each request. The model uses a standard conversation format (system, user, assistant turns) where developers control context retention through explicit message history management, enabling stateless API design with client-side or external state management.","intents":["I need to maintain conversation state across multiple API calls without server-side session storage","I want to control what the model remembers from previous turns (e.g., forget sensitive information)","I need to inject system-level instructions that persist across the entire conversation"],"best_for":["developers building stateless chatbot APIs that scale horizontally","teams implementing privacy-conscious systems that don't store conversation history server-side","enterprises needing fine-grained control over what context the model sees"],"limitations":["Conversation history must be managed client-side or in external storage; no built-in persistence","Selective context pruning requires manual implementation; the model doesn't automatically summarize old turns","System prompts are not truly persistent — they must be included in every request, consuming tokens"],"requires":["OpenAI API key with GPT-5 Chat model access","Client-side conversation history management (array of message objects)","External storage (database, cache) if persistence across sessions is needed"],"input_types":["text","image"],"output_types":["text"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-chat__cap_7","uri":"capability://safety.moderation.content.moderation.and.safety.filtering","name":"content moderation and safety filtering","description":"Applies content filtering to both input and output to detect and prevent harmful content. The model uses built-in safety classifiers that evaluate requests for policy violations (hate speech, violence, sexual content, etc.) and can refuse to engage with prohibited topics. This is implemented through pre-generation filtering of inputs and post-generation filtering of outputs, with configurable safety levels.","intents":["I need to ensure the model doesn't generate harmful content in a public-facing application","I want to prevent the model from engaging with requests that violate my content policy","I need to audit what content the model is refusing and why"],"best_for":["teams building consumer-facing applications with content moderation requirements","enterprises subject to regulatory compliance (COPPA, GDPR, industry-specific standards)","developers implementing trust and safety systems"],"limitations":["Safety filtering may refuse legitimate requests if they contain keywords associated with prohibited content","False positives require manual review and can degrade user experience","Safety levels are not granular — limited to preset configurations rather than custom policies"],"requires":["OpenAI API key with GPT-5 Chat model access","Understanding of OpenAI's usage policies and safety guidelines","Logging infrastructure to audit refusals and safety events"],"input_types":["text","image"],"output_types":["text (or refusal message if content violates policy)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-chat__cap_8","uri":"capability://automation.workflow.rate.limiting.and.quota.management.via.api.tier","name":"rate limiting and quota management via api tier","description":"Enforces usage limits based on API tier and account configuration, managing request rates and token consumption. The model operates within OpenAI's tiered API system where different subscription levels have different rate limits (requests per minute, tokens per minute) and quota allocations. This is implemented through server-side request throttling and quota tracking.","intents":["I need to understand and manage my API usage costs and rate limits","I want to implement backoff strategies when I hit rate limits","I need to allocate quota across multiple applications or teams"],"best_for":["developers building production applications that need predictable API costs","teams managing multiple applications sharing a single API account","enterprises with usage-based budgeting requirements"],"limitations":["Rate limits are account-level, not per-application; requires client-side quota management for multi-app scenarios","No built-in quota allocation or cost tracking — requires external billing integration","Rate limit headers provide limited visibility; detailed usage analytics require separate API calls"],"requires":["OpenAI API key with appropriate tier/subscription","HTTP client capable of handling 429 (rate limit) responses","Backoff/retry logic to handle rate limiting gracefully"],"input_types":["API requests (text, image)"],"output_types":["rate limit headers in HTTP response"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-5 Chat model access","HTTP client capable of multipart form data (for image uploads)","Images in JPEG, PNG, WebP, or GIF format","HTTP client with support for large request payloads (>10MB)","Timeout configuration of 60+ seconds for long-context requests","JSON Schema definition provided in API request","Understanding of JSON Schema specification (draft 2020-12)","Function definitions in OpenAI's function schema format (JSON Schema subset)","Application-level handler to execute returned function calls and return results","Well-crafted examples demonstrating desired behavior (3-10 examples recommended)"],"failure_modes":["Image resolution and aspect ratio constraints may require preprocessing; very high-resolution images are downsampled internally","Context window limits mean very long conversations with many images may require summarization or context pruning","No real-time video processing — only static image frames supported per turn","Latency increases with context window size; 128K token conversations may have 2-5x higher response times than short contexts","Cost scales linearly with input tokens, making very long conversations expensive at scale","Attention computation is O(n²) even with optimizations, creating practical limits around 128K tokens","Schema complexity affects generation speed; deeply nested schemas with many conditional fields may reduce throughput by 20-40%","Constrained decoding prevents the model from expressing uncertainty or edge cases that don't fit the schema","Very large schemas (>500 fields) may cause token overhead and slower generation","Function calling adds latency (~100-200ms) due to additional model inference for decision-making","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.27,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openai-gpt-5-chat","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-5-chat"}},"signature":"11F6HqMHmluEm8jIiFqbKUtFAtvmvgko+PFdjfMMpIbSYoycqfWCnGjU4T6gY8dRC1hDQhv31x/zKPBkgvR5BQ==","signedAt":"2026-06-22T04:16:13.589Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-5-chat","artifact":"https://unfragile.ai/openai-gpt-5-chat","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-5-chat","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"}}