{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-3.5-turbo-0613","slug":"openai-gpt-3.5-turbo-0613","name":"OpenAI: GPT-3.5 Turbo (older v0613)","type":"model","url":"https://openrouter.ai/models/openai~gpt-3.5-turbo-0613","page_url":"https://unfragile.ai/openai-gpt-3.5-turbo-0613","categories":["chatbots-assistants"],"tags":["openai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-3.5-turbo-0613__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 trained on diverse conversational data, maintaining semantic coherence across message exchanges. Implements sliding-window context management to handle conversation threads up to 4,096 tokens, with attention mechanisms that weight recent messages more heavily. The model uses byte-pair encoding (BPE) tokenization to convert natural language into token sequences for processing.","intents":["Build a chatbot that maintains conversation history and context across multiple user messages","Create an interactive assistant that understands follow-up questions and references to prior statements","Implement a customer support agent that can handle multi-turn problem-solving dialogues"],"best_for":["Teams building conversational AI applications with limited latency budgets","Developers prototyping chatbots who need fast iteration and broad language understanding","Non-technical founders building MVP chat interfaces without ML expertise"],"limitations":["Context window limited to 4,096 tokens (~3,000 words), requiring conversation pruning for long sessions","Training data cutoff at September 2021 means no knowledge of events, products, or API changes after that date","No native memory persistence — each API call is stateless and requires explicit context passing","Occasional hallucinations or factual errors, especially on specialized or recent topics"],"requires":["OpenAI API key with GPT-3.5 Turbo access","HTTP client library (curl, requests, axios, etc.)","Network connectivity to OpenAI endpoints","Understanding of message format: role (system/user/assistant) + content structure"],"input_types":["text (natural language)","code snippets (inline in messages)","structured conversation arrays with role/content pairs"],"output_types":["text (natural language response)","code (when prompted to generate)","structured JSON (when explicitly requested)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.from.natural.language","name":"code generation and completion from natural language","description":"Generates executable code in multiple programming languages (Python, JavaScript, Java, C++, SQL, etc.) from natural language descriptions using transformer-based sequence-to-sequence patterns. The model was trained on code-heavy datasets and fine-tuned to understand programming intent, producing syntactically valid code with proper indentation, imports, and error handling. Supports both full function generation and inline code completion within existing codebases.","intents":["Generate boilerplate code or utility functions from English descriptions","Complete partial code snippets with context-aware suggestions","Translate algorithms or logic described in natural language into working code","Generate SQL queries, API calls, or configuration files from requirements"],"best_for":["Solo developers and small teams accelerating routine coding tasks","Developers learning new languages or frameworks who need syntax help","Teams prototyping features quickly without writing every line manually"],"limitations":["Generated code may contain logical errors or edge-case bugs requiring manual review","No real-time syntax validation — output is not guaranteed to be runnable without testing","Limited understanding of large codebases; works best with isolated functions or small modules","Training data cutoff (Sep 2021) means unfamiliarity with recent language features or library versions","Cannot access or analyze your actual codebase for context-aware suggestions"],"requires":["OpenAI API key","Ability to parse and execute generated code","Code editor or IDE to test and refine output","Understanding of the target programming language"],"input_types":["text (natural language description of desired code)","code (partial code snippets for completion)","pseudocode or algorithm descriptions"],"output_types":["code (functions, classes, scripts)","SQL queries","configuration files (JSON, YAML, etc.)","code comments and documentation"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_10","uri":"capability://code.generation.editing.error.diagnosis.and.debugging.assistance","name":"error diagnosis and debugging assistance","description":"Analyzes error messages, stack traces, and code snippets to diagnose root causes and suggest fixes. Uses learned patterns from debugging scenarios to map error symptoms to likely causes and generates targeted solutions. Supports multiple programming languages and frameworks, with attention mechanisms that trace error propagation through code.","intents":["Debug code errors by analyzing stack traces and error messages","Suggest fixes for common programming mistakes or edge cases","Explain why code is failing and how to resolve the issue","Troubleshoot configuration or deployment errors"],"best_for":["Developers debugging code during development","Teams reducing time spent on troubleshooting and support","Developers learning new languages or frameworks"],"limitations":["Diagnosis is based on error messages and code snippets; may miss context-dependent issues","Suggested fixes may not address root cause if error symptoms are misleading","No access to runtime state or environment; cannot diagnose issues requiring live debugging","Struggles with complex, multi-component failures or race conditions","Training data cutoff limits familiarity with recent framework versions or libraries"],"requires":["OpenAI API key","Error message or stack trace","Relevant code snippet (optional but helpful)"],"input_types":["text (error messages, stack traces)","code (relevant code snippets)","context (framework, language, environment info)"],"output_types":["text (diagnosis and explanation)","code (suggested fixes or patches)","structured debugging steps"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_2","uri":"capability://text.generation.language.text.summarization.and.abstraction","name":"text summarization and abstraction","description":"Condenses long-form text (articles, documents, transcripts, code comments) into concise summaries while preserving key information. Uses transformer attention mechanisms to identify salient content and abstractive summarization patterns to rephrase rather than extract. Supports variable compression ratios and style preferences (bullet points, paragraphs, executive summary format).","intents":["Summarize long documents, articles, or research papers into key takeaways","Extract main points from meeting transcripts or video captions","Create executive summaries of technical documentation or code reviews","Condense verbose logs or error messages into actionable insights"],"best_for":["Knowledge workers processing large volumes of text daily","Teams managing documentation and need quick reference summaries","Developers reviewing code changes or pull request descriptions"],"limitations":["May omit nuanced details or context important for specialized domains","Abstractive approach can occasionally introduce subtle inaccuracies or misrepresentations","Context window limits summarization to ~4,000 tokens of input text","No awareness of document structure or metadata; treats all text equally"],"requires":["OpenAI API key","Text input (plain text, markdown, or HTML)","Clear instructions on desired summary length or format"],"input_types":["text (articles, documents, transcripts)","code (with comments for abstraction)","structured text (lists, tables)"],"output_types":["text (paragraph summaries)","bullet points","structured summaries (JSON with key sections)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_3","uri":"capability://text.generation.language.natural.language.translation.across.100.languages","name":"natural language translation across 100+ languages","description":"Translates text between natural languages using a multilingual transformer model trained on parallel corpora. Supports both direct translation and pivot-language translation for low-resource language pairs. Preserves formatting, tone, and context through attention mechanisms that track semantic relationships across languages. Handles idiomatic expressions and cultural references through learned translation patterns.","intents":["Translate user-generated content or customer support tickets across languages","Localize product documentation, marketing copy, or UI strings","Enable cross-language communication in global teams or platforms","Translate code comments or technical documentation for international audiences"],"best_for":["Global teams and platforms serving multilingual user bases","Developers building internationalization (i18n) features","Content creators and publishers reaching international audiences"],"limitations":["Quality varies significantly by language pair; high-resource pairs (English-Spanish) are more accurate than low-resource pairs","Struggles with domain-specific terminology, jargon, or technical terms not well-represented in training data","May not preserve tone or cultural nuances perfectly; requires human review for marketing or sensitive content","Context window limits translation to ~4,000 tokens per request","No awareness of previous translations; each request is independent"],"requires":["OpenAI API key","Source language and target language specification","Text input in supported language"],"input_types":["text (any natural language)","code comments","structured text (lists, tables)"],"output_types":["text (translated natural language)","formatted text (preserving original structure)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_4","uri":"capability://text.generation.language.semantic.question.answering.over.text","name":"semantic question-answering over text","description":"Answers factual and inferential questions about provided text by using transformer attention to locate relevant passages and generate answers grounded in the source material. Implements reading comprehension patterns learned during training, enabling the model to synthesize information across multiple sentences and paragraphs. Supports both extractive answers (direct quotes) and abstractive answers (paraphrased or inferred).","intents":["Build a FAQ or knowledge base chatbot that answers questions about specific documents","Extract answers from long documents without manual reading","Implement a customer support system that retrieves and answers questions from documentation","Create an interactive tutorial or learning system that explains concepts on demand"],"best_for":["Teams building document-based Q&A systems or knowledge bases","Customer support teams automating FAQ responses","Educational platforms creating interactive learning experiences"],"limitations":["Answers are only as good as the provided text; cannot retrieve external knowledge beyond training data","May hallucinate or infer answers not explicitly stated in the source material","Context window limits the amount of text that can be analyzed per request (~4,000 tokens)","No ranking or confidence scoring for answer quality; all answers presented equally","Struggles with multi-hop reasoning requiring information synthesis across distant parts of text"],"requires":["OpenAI API key","Source text or document to query","Clear question phrased in natural language"],"input_types":["text (documents, articles, knowledge base entries)","questions (natural language queries)"],"output_types":["text (natural language answers)","structured answers (JSON with answer + source passage)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_5","uri":"capability://planning.reasoning.instruction.following.and.task.decomposition","name":"instruction-following and task decomposition","description":"Interprets complex, multi-step instructions and breaks them into executable subtasks using learned reasoning patterns. The model uses chain-of-thought-like internal representations to plan task sequences, handle conditional logic, and adapt to ambiguous or underspecified instructions. Supports both explicit step-by-step guidance and implicit task inference from context.","intents":["Build agents that can execute multi-step workflows from natural language instructions","Create systems that adapt task execution based on dynamic conditions or user feedback","Implement flexible automation that handles edge cases and variations in input","Enable non-technical users to specify complex processes without programming"],"best_for":["Teams building autonomous agents or workflow automation systems","Developers creating flexible task execution engines","Organizations automating knowledge work with variable requirements"],"limitations":["Task decomposition is implicit and not always transparent; difficult to debug or modify execution plans","May misinterpret ambiguous instructions or make incorrect assumptions about intent","No persistent state or memory between requests; each task execution is independent","Cannot guarantee task completion or handle failures gracefully without explicit error handling instructions","Performance degrades with very complex or novel task types not well-represented in training data"],"requires":["OpenAI API key","Clear, detailed instructions or task descriptions","Ability to parse and execute generated subtasks"],"input_types":["text (natural language instructions)","structured task specifications","context or environmental information"],"output_types":["text (task decomposition or execution plan)","structured task lists (JSON with subtasks)","execution results or summaries"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_6","uri":"capability://text.generation.language.content.classification.and.sentiment.analysis","name":"content classification and sentiment analysis","description":"Categorizes text into predefined or open-ended classes (sentiment, topic, intent, toxicity, etc.) using transformer-based sequence classification patterns. The model learns decision boundaries during training and applies them to new text through attention-weighted feature extraction. Supports both binary classification (positive/negative) and multi-class scenarios (multiple topics or intents).","intents":["Classify customer feedback or reviews by sentiment or topic","Route support tickets to appropriate teams based on intent or issue type","Detect toxic, spam, or inappropriate content in user-generated content","Categorize incoming messages or emails by priority or type"],"best_for":["Teams managing large volumes of user feedback or support tickets","Content moderation platforms screening user-generated content","Customer service teams automating ticket routing and prioritization"],"limitations":["Classification is probabilistic; edge cases or ambiguous content may be misclassified","Requires clear definition of classes; performance degrades with vague or overlapping categories","No explanation of classification reasoning; difficult to debug incorrect classifications","Biases in training data may propagate to classifications, especially for sensitive categories","Context window limits analysis to ~4,000 tokens per request"],"requires":["OpenAI API key","Clear definition of classification categories or examples","Text input to classify"],"input_types":["text (reviews, feedback, messages)","structured text (lists, tables)"],"output_types":["text (category labels)","structured output (JSON with category + confidence scores)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_7","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Generates original text in various styles and formats (stories, poems, marketing copy, social media posts, etc.) using learned patterns from diverse writing corpora. The model uses attention mechanisms to maintain coherence and style consistency across generated text, adapting tone and vocabulary based on context or explicit instructions. Supports both constrained generation (within specified parameters) and open-ended creative output.","intents":["Generate marketing copy, product descriptions, or advertising content","Create social media posts or email subject lines","Write creative fiction, poetry, or storytelling content","Generate variations of existing content for A/B testing or personalization"],"best_for":["Content creators and marketing teams accelerating content production","Small businesses without dedicated copywriting resources","Developers building content generation features into applications"],"limitations":["Generated content may lack originality or contain clichés from training data","Quality and creativity vary; may require multiple iterations or manual refinement","No awareness of brand voice or style guidelines; requires explicit instructions for consistency","May inadvertently plagiarize or closely paraphrase training data","Struggles with very niche or specialized writing styles not well-represented in training"],"requires":["OpenAI API key","Clear instructions or examples of desired style and tone","Context or topic for content generation"],"input_types":["text (prompts, style examples, topic descriptions)","structured parameters (length, tone, format)"],"output_types":["text (generated content in various formats)","multiple variations (for A/B testing)"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_8","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 formats it as JSON, CSV, or other structured formats. Uses transformer attention to identify relevant information and learned patterns to map text to structured schemas. Supports both predefined schemas (with explicit field definitions) and open-ended extraction (inferring structure from content).","intents":["Extract key information from documents (names, dates, amounts, addresses)","Parse unstructured logs or error messages into structured data for analysis","Convert natural language requirements into structured specifications","Extract entities and relationships from text for knowledge graph construction"],"best_for":["Teams processing documents or unstructured data at scale","Data engineering teams preparing data for downstream analysis","Developers building data extraction pipelines or ETL workflows"],"limitations":["Extraction accuracy depends on text clarity and schema definition; ambiguous text may produce incomplete or incorrect results","No validation against external data sources; extracted data may be factually incorrect","Context window limits extraction to ~4,000 tokens per request","Struggles with complex nested structures or highly domain-specific formats","May hallucinate or infer fields not explicitly present in source text"],"requires":["OpenAI API key","Clear schema definition or examples of desired structure","Unstructured text input"],"input_types":["text (documents, logs, descriptions)","schema definitions (JSON schema or examples)"],"output_types":["structured data (JSON, CSV)","key-value pairs","entity lists with attributes"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-0613__cap_9","uri":"capability://text.generation.language.explanation.and.educational.content.generation","name":"explanation and educational content generation","description":"Generates clear, pedagogically-sound explanations of complex concepts, techniques, or systems in accessible language. Uses learned patterns to break down topics into digestible components, provide analogies, and scaffold understanding progressively. Adapts explanation depth and style based on audience level (beginner, intermediate, expert) and learning context.","intents":["Create tutorial content or learning materials for technical topics","Generate explanations of code, algorithms, or system architecture","Build interactive tutoring systems that explain concepts on demand","Create documentation that is accessible to non-expert audiences"],"best_for":["Educators and content creators building learning materials","Technical teams documenting complex systems for diverse audiences","Developers building educational or tutoring applications"],"limitations":["Explanations may oversimplify or omit important nuances for advanced learners","Quality depends on clarity of input question or topic; vague requests produce generic explanations","No awareness of learner's prior knowledge; may assume too much or too little background","Analogies may not resonate with all learners or cultural contexts","Training data cutoff limits explanations of very recent developments or technologies"],"requires":["OpenAI API key","Clear topic or concept to explain","Optional: target audience level or learning context"],"input_types":["text (topic descriptions, questions, code snippets)","structured parameters (audience level, format preference)"],"output_types":["text (explanations in various formats)","structured explanations (with sections, examples, analogies)","educational content (lessons, tutorials, FAQs)"],"categories":["text-generation-language","education"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-3.5 Turbo access","HTTP client library (curl, requests, axios, etc.)","Network connectivity to OpenAI endpoints","Understanding of message format: role (system/user/assistant) + content structure","OpenAI API key","Ability to parse and execute generated code","Code editor or IDE to test and refine output","Understanding of the target programming language","Error message or stack trace","Relevant code snippet (optional but helpful)"],"failure_modes":["Context window limited to 4,096 tokens (~3,000 words), requiring conversation pruning for long sessions","Training data cutoff at September 2021 means no knowledge of events, products, or API changes after that date","No native memory persistence — each API call is stateless and requires explicit context passing","Occasional hallucinations or factual errors, especially on specialized or recent topics","Generated code may contain logical errors or edge-case bugs requiring manual review","No real-time syntax validation — output is not guaranteed to be runnable without testing","Limited understanding of large codebases; works best with isolated functions or small modules","Training data cutoff (Sep 2021) means unfamiliarity with recent language features or library versions","Cannot access or analyze your actual codebase for context-aware suggestions","Diagnosis is based on error messages and code snippets; may miss context-dependent issues","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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-0613","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-3.5-turbo-0613"}},"signature":"KhRYN3pevx9SfntrRW3eEZIK6UiyKxQBFn4d+REWjlPffXdr+SQRcDyoBtoIrXLEFHXd7B9mL2b7ufAZ0akTAw==","signedAt":"2026-06-22T20:57:46.705Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-3.5-turbo-0613","artifact":"https://unfragile.ai/openai-gpt-3.5-turbo-0613","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-3.5-turbo-0613","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"}}