{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-4-0314","slug":"openai-gpt-4-0314","name":"OpenAI: GPT-4 (older v0314)","type":"model","url":"https://openrouter.ai/models/openai~gpt-4-0314","page_url":"https://unfragile.ai/openai-gpt-4-0314","categories":["chatbots-assistants"],"tags":["openai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-5 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-4-0314__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.8k.token.context","name":"multi-turn conversational reasoning with 8k token context","description":"Processes multi-turn conversations using transformer-based attention mechanisms with an 8,192 token context window, enabling coherent dialogue across multiple exchanges. The model maintains conversation history within the context window and applies causal masking to prevent attending to future tokens, allowing it to generate contextually appropriate responses based on prior turns. Architecture uses decoder-only transformer with rotary positional embeddings to handle sequential dependencies in dialogue.","intents":["build a chatbot that maintains conversation state across multiple user messages","create an interactive assistant that references earlier parts of a conversation","implement a multi-turn Q&A system where context from previous exchanges informs answers"],"best_for":["developers building conversational AI applications","teams creating customer support chatbots","builders prototyping interactive agents with limited context needs"],"limitations":["8,192 token context window limits conversation length before history must be summarized or truncated","no native memory persistence across sessions — conversation state must be managed externally","training data cutoff at September 2021 means no knowledge of events after that date","single-turn latency ~500ms-2s depending on response length and API load"],"requires":["OpenAI API key with GPT-4 access","HTTP client capable of streaming responses","token counter to manage context window (e.g., tiktoken library)","network connectivity to OpenAI endpoints"],"input_types":["text (natural language)","code snippets (in conversation)","structured prompts with system/user/assistant roles"],"output_types":["text (natural language response)","code (when asked to generate)","structured reasoning traces (with appropriate prompting)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-0314__cap_1","uri":"capability://code.generation.editing.code.generation.and.explanation.with.programming.language.support","name":"code generation and explanation with programming language support","description":"Generates syntactically valid code across 50+ programming languages by leveraging transformer patterns trained on public code repositories and documentation. The model applies language-specific formatting rules learned during training and can generate complete functions, classes, or multi-file solutions based on natural language descriptions. Uses in-context learning to adapt to coding style and patterns provided in the prompt.","intents":["generate boilerplate code for common patterns (API endpoints, database queries, UI components)","explain existing code snippets and suggest refactoring improvements","translate code between programming languages while preserving logic","debug code by analyzing error messages and suggesting fixes"],"best_for":["individual developers accelerating routine coding tasks","teams using code generation to reduce boilerplate in new projects","educators explaining programming concepts through generated examples"],"limitations":["generated code may contain subtle bugs or security vulnerabilities — requires human review before production use","struggles with domain-specific languages and proprietary frameworks not well-represented in training data","cannot access external libraries or package documentation beyond training cutoff (Sep 2021)","context window limits to ~3,000 tokens of code before quality degrades"],"requires":["OpenAI API key","ability to parse and execute generated code in target language","code linter or formatter to normalize output (optional but recommended)"],"input_types":["natural language descriptions of desired code behavior","existing code snippets to refactor or explain","error messages and stack traces for debugging"],"output_types":["code in target programming language","explanations of code logic","refactoring suggestions with rationale"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-0314__cap_2","uri":"capability://text.generation.language.instruction.following.with.system.prompt.control","name":"instruction-following with system prompt control","description":"Accepts a system prompt parameter that establishes role, tone, and behavioral constraints for the model, enabling fine-grained control over response style without retraining. The system prompt is prepended to the conversation context and influences token generation probabilities across all subsequent user messages through learned associations between instructions and output patterns. This is implemented via the OpenAI Chat Completions API's system role parameter.","intents":["create a domain-specific assistant (e.g., SQL expert, creative writer, technical documentation generator)","enforce consistent tone and style across multiple API calls","implement guardrails by instructing the model to refuse certain requests","adapt the same model to multiple use cases without fine-tuning"],"best_for":["product teams building specialized assistants for specific domains","developers prototyping different personas or roles quickly","organizations needing consistent brand voice across AI interactions"],"limitations":["system prompt effectiveness varies — complex behavioral constraints may be ignored or partially followed","no guarantee that system prompt will override user input if user explicitly contradicts it","system prompt tokens count against the 8,192 token limit, reducing available context for conversation","no audit trail of which system prompt was used for a given response (must log separately)"],"requires":["OpenAI API key with Chat Completions endpoint access","understanding of prompt engineering best practices","testing framework to validate that system prompt produces desired behavior"],"input_types":["system prompt (text instruction)","user messages (text)"],"output_types":["text responses adhering to system prompt constraints"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-0314__cap_3","uri":"capability://planning.reasoning.logical.reasoning.and.multi.step.problem.decomposition","name":"logical reasoning and multi-step problem decomposition","description":"Performs chain-of-thought reasoning by generating intermediate reasoning steps before producing final answers, leveraging transformer attention patterns to maintain logical consistency across multiple reasoning hops. The model can decompose complex problems into sub-problems, track variable states across steps, and validate intermediate conclusions. This emerges from training on mathematical proofs, scientific papers, and structured reasoning examples.","intents":["solve multi-step math problems with shown work","analyze complex scenarios and provide reasoned recommendations","debug logical errors in arguments or code by tracing through assumptions","generate structured analysis with clear justification for conclusions"],"best_for":["developers building AI-powered analysis or decision-support tools","educators creating interactive problem-solving tutors","teams needing explainable AI decisions with reasoning traces"],"limitations":["reasoning quality degrades on problems requiring >10-15 logical steps","may produce confident-sounding but incorrect reasoning (hallucination of logic)","no formal verification — reasoning traces are plausible but not guaranteed correct","longer reasoning outputs consume more tokens, reducing available context"],"requires":["OpenAI API key","prompt engineering to explicitly request step-by-step reasoning (e.g., 'Let's think step by step')","parsing logic to extract reasoning steps from response if needed for downstream processing"],"input_types":["natural language problem statements","mathematical expressions","code with logical errors"],"output_types":["step-by-step reasoning traces","final answers with justification","identified logical fallacies or errors"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-0314__cap_4","uri":"capability://text.generation.language.knowledge.synthesis.and.summarization","name":"knowledge synthesis and summarization","description":"Synthesizes information from multiple sources or long documents by identifying key concepts, extracting relevant details, and generating coherent summaries that preserve essential information. The model uses attention mechanisms to weight important tokens and generate abstractive summaries (not just extractive) that reorganize information for clarity. Trained on news articles, academic papers, and web content with human-written summaries.","intents":["summarize long documents or articles into key points","extract and synthesize information from multiple sources into a unified narrative","generate executive summaries for reports or research papers","condense meeting notes or conversation transcripts into action items"],"best_for":["knowledge workers processing large volumes of information","teams building document analysis or research tools","organizations automating report generation"],"limitations":["summaries may omit important nuances or context from original source","struggles with highly technical or domain-specific jargon not well-represented in training data","cannot verify factual accuracy of summaries — may hallucinate details not in source","summary length must be specified in prompt; no automatic optimization for ideal length"],"requires":["OpenAI API key","source documents or text to summarize","clear instructions on desired summary length and focus areas"],"input_types":["long-form text (articles, reports, transcripts)","multiple documents or sources","structured data with descriptions"],"output_types":["abstractive summaries (text)","bullet-point key takeaways","structured summaries (with appropriate prompting)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-0314__cap_5","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.style.control","name":"creative writing and content generation with style control","description":"Generates original creative content (stories, poetry, marketing copy, dialogue) by sampling from learned distributions of language patterns associated with different genres and styles. The model uses temperature and top-p sampling parameters to control output diversity, and can adapt to specified tones, genres, and narrative constraints provided in the prompt. Trained on diverse creative writing from the internet and published works.","intents":["generate marketing copy, product descriptions, or advertising headlines","create fictional narratives, character backstories, or dialogue","write poetry or creative prose in specified styles or meters","brainstorm creative ideas and variations on themes"],"best_for":["content creators and copywriters accelerating ideation","game developers generating NPC dialogue or narrative content","marketing teams prototyping ad copy variations","educators creating writing prompts and examples"],"limitations":["generated content may infringe on copyrighted material if training data contained similar works","creative output quality is subjective and inconsistent — requires human curation","struggles with very long narratives (>2,000 tokens) where plot coherence degrades","may produce clichéd or derivative content if not prompted with specific constraints"],"requires":["OpenAI API key","clear creative brief or prompt with style/tone specifications","human editorial review before publishing generated content"],"input_types":["creative briefs or prompts","style references or examples","narrative constraints or character descriptions"],"output_types":["creative text (stories, poetry, copy)","multiple variations or alternatives","structured creative content (e.g., JSON with character attributes)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-0314__cap_6","uri":"capability://text.generation.language.translation.and.cross.lingual.understanding","name":"translation and cross-lingual understanding","description":"Translates text between 100+ languages and understands semantic meaning across linguistic boundaries by leveraging multilingual token embeddings and cross-lingual attention patterns learned during training. The model can preserve tone, formality, and cultural context in translations, and can answer questions about text in languages different from the query language. Supports both direct translation and back-translation for quality validation.","intents":["translate documents or user-generated content between languages","build multilingual chatbots that respond in the user's language","extract meaning from non-English text for analysis or classification","validate translation quality by back-translating and comparing to original"],"best_for":["global teams building multilingual products","content platforms serving international audiences","organizations processing documents in multiple languages","developers building translation APIs"],"limitations":["translation quality varies significantly by language pair — low-resource languages produce lower quality","cultural idioms and wordplay often don't translate well, requiring human post-editing","formal terminology in specialized domains may be mistranslated if not in training data","no guarantee of consistency across multiple translations of the same phrase"],"requires":["OpenAI API key","source text in supported language","specification of target language (ISO 639-1 code recommended)"],"input_types":["text in any supported language","code-mixed text (multiple languages in one document)","specialized terminology with context"],"output_types":["translated text in target language","confidence scores or quality assessments (with appropriate prompting)","multiple translation alternatives"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-0314__cap_7","uri":"capability://text.generation.language.question.answering.with.knowledge.cutoff.awareness","name":"question-answering with knowledge cutoff awareness","description":"Answers factual and conceptual questions by retrieving relevant knowledge from training data and generating coherent responses. The model explicitly acknowledges its knowledge cutoff (September 2021) and can indicate uncertainty when asked about events or developments after that date. Uses attention mechanisms to identify relevant context within the question and generate targeted answers rather than generic summaries.","intents":["build FAQ systems or knowledge bases that answer user questions","create educational tools that explain concepts and answer follow-up questions","implement search-augmented systems where the model answers based on retrieved documents","provide technical support by answering questions about products or processes"],"best_for":["teams building customer support or help desk systems","educators creating interactive learning tools","organizations building internal knowledge bases","developers prototyping question-answering features"],"limitations":["knowledge cutoff at September 2021 — cannot answer questions about recent events or developments","may hallucinate plausible-sounding answers to questions outside training data","struggles with questions requiring real-time information (stock prices, weather, current news)","no built-in fact-checking — answers should be verified against authoritative sources"],"requires":["OpenAI API key","clear, well-formed questions","optional: retrieval system to provide context documents (for RAG-style QA)"],"input_types":["natural language questions","context documents (optional, for improved accuracy)","follow-up questions in multi-turn conversations"],"output_types":["natural language answers","structured answers with confidence levels (with appropriate prompting)","citations or references to source material (if provided in context)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-0314__cap_8","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.parsing","name":"structured data extraction and schema-based parsing","description":"Extracts structured information from unstructured text by mapping natural language content to predefined schemas or JSON structures. The model uses instruction-following to generate valid JSON or structured output that conforms to specified field definitions and data types. Leverages learned associations between natural language patterns and structured representations from training data.","intents":["extract entities (names, dates, amounts) from documents or user input","parse natural language into structured data for database insertion","convert free-form text descriptions into standardized forms or templates","generate structured API requests from natural language specifications"],"best_for":["teams building data extraction pipelines for document processing","developers automating form-filling or data entry tasks","organizations converting unstructured data into structured databases","builders creating natural language interfaces to structured systems"],"limitations":["extraction accuracy depends on clarity of schema definition and examples in prompt","may hallucinate missing fields or generate plausible but incorrect values","struggles with ambiguous or incomplete source text","no validation that extracted data is semantically correct — requires post-processing validation"],"requires":["OpenAI API key","clear schema definition (JSON schema, TypeScript interface, or example)","JSON parsing library to validate and process output","validation logic to check extracted data against business rules"],"input_types":["unstructured text (documents, emails, user input)","schema definitions (JSON schema, examples, or descriptions)"],"output_types":["JSON objects conforming to specified schema","CSV or structured text formats","validation errors or confidence scores (with appropriate prompting)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-4 access","HTTP client capable of streaming responses","token counter to manage context window (e.g., tiktoken library)","network connectivity to OpenAI endpoints","OpenAI API key","ability to parse and execute generated code in target language","code linter or formatter to normalize output (optional but recommended)","OpenAI API key with Chat Completions endpoint access","understanding of prompt engineering best practices","testing framework to validate that system prompt produces desired behavior"],"failure_modes":["8,192 token context window limits conversation length before history must be summarized or truncated","no native memory persistence across sessions — conversation state must be managed externally","training data cutoff at September 2021 means no knowledge of events after that date","single-turn latency ~500ms-2s depending on response length and API load","generated code may contain subtle bugs or security vulnerabilities — requires human review before production use","struggles with domain-specific languages and proprietary frameworks not well-represented in training data","cannot access external libraries or package documentation beyond training cutoff (Sep 2021)","context window limits to ~3,000 tokens of code before quality degrades","system prompt effectiveness varies — complex behavioral constraints may be ignored or partially followed","no guarantee that system prompt will override user input if user explicitly contradicts it","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.24,"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-4-0314","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4-0314"}},"signature":"O2dpT2qebSnULKTz5KLjPdAjdxlYRPT0Cd2t9xfbisxiNgYGeqgBtvGhvllbdh/7toNG+GMmKP/ncZvYtEIuAw==","signedAt":"2026-06-22T01:21:34.533Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4-0314","artifact":"https://unfragile.ai/openai-gpt-4-0314","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4-0314","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"}}