{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-3.5-turbo","slug":"openai-gpt-3.5-turbo","name":"OpenAI: GPT-3.5 Turbo","type":"model","url":"https://openrouter.ai/models/openai~gpt-3.5-turbo","page_url":"https://unfragile.ai/openai-gpt-3.5-turbo","categories":["llm-apis"],"tags":["openai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-3.5-turbo__cap_0","uri":"capability://text.generation.language.conversational.chat.completion.with.multi.turn.context","name":"conversational chat completion with multi-turn context","description":"Processes multi-turn conversation histories using a transformer-based architecture optimized for chat interactions. Maintains context across message exchanges by encoding the full conversation thread (system prompt + user/assistant messages) into a single forward pass, enabling coherent dialogue without explicit memory management. Uses token-efficient attention patterns to handle typical chat contexts (up to 4,096 tokens) with minimal computational overhead.","intents":["Build a chatbot that understands conversation history and responds contextually","Create a customer support agent that maintains conversation state across multiple exchanges","Implement a conversational Q&A system where follow-up questions reference prior answers"],"best_for":["Teams building conversational AI products with standard chat UX","Developers prototyping chatbots who need fast iteration and low latency","Non-technical founders building MVP chatbot applications"],"limitations":["Context window limited to 4,096 tokens — long conversations require summarization or pruning","No persistent memory across sessions — each conversation starts fresh without prior interaction history","Training data cutoff at September 2021 — lacks knowledge of events, products, or APIs released after that date","No native support for structured conversation state — requires external session management for complex workflows"],"requires":["OpenAI API key or OpenRouter API key with GPT-3.5 Turbo access","HTTP client library (curl, axios, requests, etc.)","Network connectivity to OpenAI or OpenRouter endpoints"],"input_types":["text (natural language queries)","code snippets (for code explanation or debugging)","structured conversation arrays (system prompt + message history)"],"output_types":["text (natural language responses)","code (generated or explained code blocks)","structured JSON (when prompted with format instructions)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.from.natural.language","name":"code generation and completion from natural language","description":"Generates syntactically valid code in 40+ programming languages from natural language descriptions using transformer-based sequence-to-sequence generation. Trained on large corpora of code repositories and documentation, enabling it to infer intent from English descriptions and produce working implementations. Supports both full-function generation from docstrings and inline completion for partial code snippets, with awareness of common libraries and frameworks.","intents":["Generate boilerplate code or utility functions from English descriptions","Complete partially-written code functions with context-aware suggestions","Translate algorithms or logic described in English into executable code","Generate code examples for documentation or tutorials"],"best_for":["Solo developers and small teams accelerating routine coding tasks","Developers learning new languages or frameworks who need syntax help","Technical writers and educators generating code examples at scale"],"limitations":["Generated code may contain logical errors or security vulnerabilities — requires human review before production use","No awareness of project-specific conventions, internal libraries, or custom APIs — generates generic code that may not integrate seamlessly","Limited to 4,096 token context — cannot generate large multi-file codebases or refactor entire projects","Training data cutoff at September 2021 — unfamiliar with recent language versions, frameworks, or best practices released after that date"],"requires":["OpenAI API key or OpenRouter API key","Programming language runtime or IDE to execute generated code","Basic understanding of the target language syntax to validate output"],"input_types":["natural language descriptions (e.g., 'write a function that sorts an array')","code snippets with comments (e.g., partial functions with docstrings)","pseudocode or algorithm descriptions"],"output_types":["code (single functions, classes, or scripts)","code with explanatory comments","multiple code variants (when prompted to generate alternatives)"],"categories":["code-generation-editing","developer-productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_10","uri":"capability://planning.reasoning.reasoning.and.step.by.step.problem.solving","name":"reasoning and step-by-step problem solving","description":"Solves complex problems by breaking them into steps and reasoning through each step explicitly. Uses chain-of-thought prompting patterns (generating intermediate reasoning steps) to improve accuracy on multi-step problems like math, logic puzzles, or code debugging. Trained on diverse reasoning tasks, enabling it to apply reasoning patterns across domains.","intents":["Solve math problems with step-by-step explanations","Debug code by reasoning through logic and identifying errors","Solve logic puzzles or riddles","Answer complex questions requiring multi-step reasoning"],"best_for":["Educational platforms teaching problem-solving skills","Developers debugging complex code issues","Teams solving analytical or logical problems"],"limitations":["Reasoning is probabilistic — may make logical errors or take incorrect reasoning paths","Limited to 4,096 token context — cannot reason about very large problems or codebases","No access to external tools or computation — cannot verify answers or perform calculations beyond transformer capacity","May produce verbose reasoning that obscures rather than clarifies the solution"],"requires":["OpenAI API key or OpenRouter API key","Problem statement or question"],"input_types":["math problems","logic puzzles","code snippets to debug","complex questions requiring multi-step reasoning"],"output_types":["step-by-step reasoning with final answer","explanation of logic and approach","multiple solution paths (when prompted)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_11","uri":"capability://planning.reasoning.instruction.following.and.task.execution","name":"instruction following and task execution","description":"Follows complex, multi-step instructions and executes tasks as specified. Uses instruction-tuning to interpret natural language commands and adapt behavior to user specifications. Supports conditional logic, parameter variation, and can handle ambiguous or underspecified instructions by asking clarifying questions or making reasonable assumptions.","intents":["Execute complex workflows described in natural language","Adapt responses to specific formatting, tone, or style requirements","Handle conditional logic (e.g., 'if the text is longer than 100 words, summarize it')","Follow domain-specific instructions or protocols"],"best_for":["Teams automating complex workflows with natural language specifications","Non-technical users building custom AI workflows","Developers building instruction-following agents"],"limitations":["Instruction following is probabilistic — may misinterpret complex or ambiguous instructions","No persistent state between requests — cannot maintain instruction context across multiple API calls","Limited to 4,096 token context — cannot process very long instruction sets or task histories","May fail on instructions requiring external knowledge or real-time information"],"requires":["OpenAI API key or OpenRouter API key","Clear, specific instructions"],"input_types":["natural language instructions","conditional logic specifications","formatting or style requirements","task parameters or constraints"],"output_types":["task output formatted per instructions","clarifying questions (if instructions are ambiguous)","structured output (if format is specified)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_2","uri":"capability://code.generation.editing.code.explanation.and.documentation.generation","name":"code explanation and documentation generation","description":"Analyzes provided code snippets and generates human-readable explanations of logic, purpose, and behavior. Uses transformer-based code understanding to parse syntax and semantics, then generates natural language descriptions at varying levels of detail (high-level overview, line-by-line breakdown, or docstring-style summaries). Supports explanation in multiple languages and can generate formal documentation or inline comments.","intents":["Understand legacy code or unfamiliar codebases by generating explanations","Generate docstrings and comments for undocumented code","Create technical documentation or README sections from source code","Learn how code works by requesting detailed walkthroughs"],"best_for":["Teams maintaining legacy codebases with poor documentation","Developers onboarding to new projects and needing rapid code comprehension","Technical writers generating API documentation from source code"],"limitations":["Explanations may be inaccurate for complex algorithms or non-obvious logic — requires domain expertise to validate","Cannot infer business intent or architectural decisions from code alone — explanations are purely technical","Limited to 4,096 token context — cannot explain entire large files or multi-file interactions","May generate verbose or redundant explanations for simple code"],"requires":["OpenAI API key or OpenRouter API key","Code snippet or file to analyze (as text input)"],"input_types":["code snippets (functions, classes, or scripts)","full source files","pseudocode or algorithm descriptions"],"output_types":["natural language explanations","docstrings (Python, JavaScript, etc.)","inline comments","markdown documentation"],"categories":["code-generation-editing","developer-productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_3","uri":"capability://text.generation.language.text.summarization.and.abstraction","name":"text summarization and abstraction","description":"Condenses long-form text (articles, documents, conversations) into concise summaries while preserving key information. Uses transformer-based abstractive summarization (generating new text rather than extracting sentences) to produce coherent, grammatically correct summaries at user-specified lengths. Supports multiple summarization styles (bullet points, paragraphs, executive summaries) and can extract key themes or action items.","intents":["Summarize long articles or research papers into key takeaways","Extract action items and decisions from meeting transcripts or emails","Generate executive summaries of documents for quick review","Condense customer feedback or support tickets into themes"],"best_for":["Knowledge workers processing large volumes of text daily","Teams managing customer feedback or support tickets at scale","Researchers and analysts synthesizing information from multiple sources"],"limitations":["Abstractive summaries may omit nuanced details or misrepresent context — requires human review for critical decisions","Cannot preserve exact quotes or citations — generated text is paraphrased","Limited to 4,096 token context — cannot summarize very long documents without chunking","May hallucinate details not present in source text, especially for unfamiliar topics"],"requires":["OpenAI API key or OpenRouter API key","Text content to summarize (as string input)"],"input_types":["long-form text (articles, documents, emails)","conversation transcripts","meeting notes","customer feedback or reviews"],"output_types":["paragraph summaries","bullet-point summaries","key takeaways or action items","structured JSON (themes, sentiment, priorities)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_4","uri":"capability://text.generation.language.translation.between.natural.languages","name":"translation between natural languages","description":"Translates text between 100+ language pairs using transformer-based neural machine translation. Trained on multilingual corpora and instruction-tuned for translation tasks, enabling it to handle idiomatic expressions, cultural context, and domain-specific terminology. Supports preservation of formatting, handling of code or technical terms, and can translate at varying formality levels.","intents":["Translate user-generated content or customer communications across languages","Localize product documentation or help content for international audiences","Translate code comments or technical documentation","Enable multilingual customer support by translating support tickets"],"best_for":["Global teams managing multilingual content or communications","SaaS products expanding to international markets","Customer support teams handling multilingual inquiries"],"limitations":["Translations may lose nuance, idioms, or cultural context — requires native speaker review for marketing or sensitive content","No awareness of domain-specific terminology or company-specific jargon — may produce generic translations","Limited to 4,096 token context — cannot translate very long documents without chunking","May struggle with rare languages or language pairs with limited training data"],"requires":["OpenAI API key or OpenRouter API key","Source language and target language specification","Text content to translate"],"input_types":["natural language text","code with comments","technical documentation","customer communications"],"output_types":["translated text","translated text with formatting preserved","multiple translation variants (when prompted)"],"categories":["text-generation-language","localization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_5","uri":"capability://text.generation.language.sentiment.analysis.and.emotional.tone.detection","name":"sentiment analysis and emotional tone detection","description":"Analyzes text to identify emotional tone, sentiment polarity (positive/negative/neutral), and emotional intensity. Uses transformer-based classification trained on sentiment-labeled datasets to infer emotional content from language patterns. Can detect multiple sentiments in a single text, identify sarcasm or irony, and provide confidence scores for classifications.","intents":["Analyze customer feedback or reviews to identify satisfaction levels","Monitor social media mentions or brand sentiment at scale","Detect emotional tone in customer support tickets to prioritize urgent issues","Analyze survey responses or feedback forms for sentiment trends"],"best_for":["Customer success and support teams monitoring satisfaction","Product managers analyzing user feedback and feature requests","Marketing teams tracking brand sentiment across channels"],"limitations":["Sentiment detection is probabilistic — may misclassify sarcasm, irony, or context-dependent emotions","No awareness of domain-specific sentiment (e.g., 'bug' is negative in product feedback but neutral in biology)","Limited to 4,096 token context — cannot analyze very long documents without chunking","May be biased toward training data demographics — may perform poorly on underrepresented languages or dialects"],"requires":["OpenAI API key or OpenRouter API key","Text content to analyze"],"input_types":["customer reviews or feedback","social media posts or comments","support tickets or emails","survey responses"],"output_types":["sentiment label (positive/negative/neutral)","sentiment score (0-1 confidence)","emotion categories (joy, anger, frustration, etc.)","structured JSON with multiple sentiment dimensions"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_6","uri":"capability://text.generation.language.content.classification.and.categorization","name":"content classification and categorization","description":"Classifies text into predefined categories or custom labels using transformer-based sequence classification. Trained on diverse text types, enabling zero-shot classification (assigning labels without examples) or few-shot classification (with minimal examples). Supports hierarchical categorization, multi-label classification (assigning multiple categories per text), and confidence scoring.","intents":["Automatically categorize support tickets or emails into routing queues","Classify product feedback into feature requests, bug reports, or general comments","Tag content for content management systems or knowledge bases","Detect spam, abuse, or policy violations in user-generated content"],"best_for":["Support teams automating ticket routing and triage","Content platforms moderating or organizing user-generated content","Product teams analyzing feedback at scale"],"limitations":["Zero-shot classification may be inaccurate for ambiguous or novel categories — benefits from examples or fine-tuning","No awareness of context-specific category definitions — may misclassify edge cases","Limited to 4,096 token context — cannot classify very long documents without chunking","Multi-label classification may assign overlapping or contradictory labels without explicit constraints"],"requires":["OpenAI API key or OpenRouter API key","Category labels or taxonomy definition","Text content to classify"],"input_types":["support tickets or emails","product feedback or reviews","user-generated content","documents or articles"],"output_types":["single category label","multiple category labels (multi-label)","category with confidence score","hierarchical category path"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_7","uri":"capability://text.generation.language.question.answering.from.context","name":"question answering from context","description":"Answers questions about provided context or documents by extracting and synthesizing relevant information. Uses transformer-based reading comprehension to locate answer spans in text and generate natural language responses. Supports both extractive QA (returning exact text from source) and abstractive QA (generating new text that answers the question).","intents":["Build a document-based Q&A system for FAQs or knowledge bases","Answer questions about uploaded documents or articles","Extract specific information from long documents","Create interactive tutorials or learning systems"],"best_for":["Teams building internal knowledge base Q&A systems","Customer support automating FAQ responses","Educational platforms creating interactive learning experiences"],"limitations":["Answers are limited to information present in provided context — cannot reason beyond source material","May hallucinate answers if context is ambiguous or contradictory","Limited to 4,096 token context — cannot process very long documents without chunking or summarization","No persistent memory of previous questions — each query is independent"],"requires":["OpenAI API key or OpenRouter API key","Context document or text to answer questions about","Question in natural language"],"input_types":["context document (article, FAQ, knowledge base entry)","natural language question","multiple documents (requires concatenation within token limit)"],"output_types":["natural language answer","extracted text span with source reference","structured data (when context is formatted as structured text)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_8","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Generates original creative content (stories, poems, marketing copy, social media posts) from prompts or outlines. Uses transformer-based text generation with instruction-tuning for creative tasks, enabling it to adapt tone, style, and length to specifications. Supports multiple genres, writing styles, and can generate variations or alternatives on demand.","intents":["Generate marketing copy, product descriptions, or ad headlines","Create social media content or blog post outlines","Generate creative writing samples or story ideas","Produce email templates or customer communication drafts"],"best_for":["Content creators and copywriters accelerating draft generation","Marketing teams producing content at scale","Non-technical founders creating marketing materials for MVPs"],"limitations":["Generated content may be generic, clichéd, or lack authentic voice — requires human editing for brand consistency","No awareness of brand guidelines, tone, or company voice — generates generic content","Limited to 4,096 token context — cannot generate very long documents without chunking","May produce repetitive or formulaic content, especially for common genres"],"requires":["OpenAI API key or OpenRouter API key","Creative prompt or outline"],"input_types":["creative prompt (e.g., 'write a funny tweet about coffee')","outline or structure","style or tone specification","example content for style matching"],"output_types":["creative text (stories, poems, copy)","multiple variations or alternatives","structured content (outlines, bullet points)"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo__cap_9","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.text","name":"structured data extraction from unstructured text","description":"Extracts structured information (entities, relationships, key-value pairs) from unstructured text and returns it in JSON or other structured formats. Uses transformer-based named entity recognition and relation extraction, combined with instruction-tuning to map natural language to structured schemas. Supports custom extraction schemas and can handle complex nested structures.","intents":["Extract contact information, dates, or amounts from documents or emails","Parse product information from descriptions or reviews","Extract entities and relationships from research papers or articles","Convert unstructured customer data into structured database records"],"best_for":["Data teams converting unstructured text into structured databases","Automation engineers building document processing pipelines","Researchers extracting information from large text corpora"],"limitations":["Extraction accuracy depends on text clarity and schema definition — ambiguous text may produce incorrect extractions","No awareness of domain-specific entity types or relationships — may miss context-specific information","Limited to 4,096 token context — cannot process very long documents without chunking","Requires clear schema definition — vague extraction instructions produce inconsistent results"],"requires":["OpenAI API key or OpenRouter API key","Extraction schema or field definitions","Unstructured text to extract from"],"input_types":["unstructured text (documents, emails, articles)","extraction schema (JSON schema or field list)","examples of desired extraction format"],"output_types":["JSON with extracted fields","CSV or structured table format","nested JSON for complex relationships"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key or OpenRouter API key with GPT-3.5 Turbo access","HTTP client library (curl, axios, requests, etc.)","Network connectivity to OpenAI or OpenRouter endpoints","OpenAI API key or OpenRouter API key","Programming language runtime or IDE to execute generated code","Basic understanding of the target language syntax to validate output","Problem statement or question","Clear, specific instructions","Code snippet or file to analyze (as text input)","Text content to summarize (as string input)"],"failure_modes":["Context window limited to 4,096 tokens — long conversations require summarization or pruning","No persistent memory across sessions — each conversation starts fresh without prior interaction history","Training data cutoff at September 2021 — lacks knowledge of events, products, or APIs released after that date","No native support for structured conversation state — requires external session management for complex workflows","Generated code may contain logical errors or security vulnerabilities — requires human review before production use","No awareness of project-specific conventions, internal libraries, or custom APIs — generates generic code that may not integrate seamlessly","Limited to 4,096 token context — cannot generate large multi-file codebases or refactor entire projects","Training data cutoff at September 2021 — unfamiliar with recent language versions, frameworks, or best practices released after that date","Reasoning is probabilistic — may make logical errors or take incorrect reasoning paths","Limited to 4,096 token context — cannot reason about very large problems or codebases","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"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-3.5-turbo","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-3.5-turbo"}},"signature":"h4IqFyu/IRyT7jGLd9hu6rEq54Q3GPeY94yvLd8NfLa3hHT2tH/ZehBIXI4cuFrIJyN2mMQ9ePqzOgj3rMooDw==","signedAt":"2026-06-20T01:13:23.370Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-3.5-turbo","artifact":"https://unfragile.ai/openai-gpt-3.5-turbo","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-3.5-turbo","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"}}