{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-3.5-turbo-instruct","slug":"openai-gpt-3.5-turbo-instruct","name":"OpenAI: GPT-3.5 Turbo Instruct","type":"model","url":"https://openrouter.ai/models/openai~gpt-3.5-turbo-instruct","page_url":"https://unfragile.ai/openai-gpt-3.5-turbo-instruct","categories":["chatbots-assistants"],"tags":["openai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.50e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-3.5-turbo-instruct__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.with.completion.api","name":"instruction-following text generation with completion api","description":"Generates coherent text continuations from arbitrary prompts using a completion-based API (not chat-optimized). The model processes raw text input through a transformer decoder architecture trained on instruction-following tasks, returning logit-sampled or beam-searched completions without enforcing message-role formatting. This differs from GPT-3.5 Turbo's chat variant by omitting conversation-specific fine-tuning, making it suitable for raw prompt completion, code generation from docstrings, and creative writing tasks.","intents":["Generate code from natural language docstrings or comments without chat formatting overhead","Complete creative writing or narrative text from arbitrary starting prompts","Build custom instruction-following pipelines without message role constraints","Perform few-shot learning by embedding examples directly in the prompt text"],"best_for":["developers building custom completion pipelines that don't fit chat semantics","researchers prototyping instruction-following behavior without conversation overhead","teams migrating from legacy GPT-3 completion APIs to newer models"],"limitations":["No built-in conversation memory or multi-turn context management — each request is stateless","Training data cutoff at September 2021 means no knowledge of events, APIs, or libraries after that date","Completion API lacks structured output guarantees — requires post-processing to extract JSON or code","No native function calling or tool use integration — must be implemented via prompt engineering","Slower inference than GPT-4 Turbo for complex reasoning tasks requiring chain-of-thought"],"requires":["OpenAI API key or OpenRouter API key with OpenAI provider configured","HTTP client library (curl, requests, axios, etc.) or OpenAI SDK (Python 1.0+, Node.js 4.0+)","Understanding of completion API semantics (prompt, max_tokens, temperature, top_p parameters)"],"input_types":["text (raw prompt string, no message role formatting)","code (as text within prompt for few-shot examples)"],"output_types":["text (raw completion string)","code (if prompted to generate, returned as unstructured text)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-instruct__cap_1","uri":"capability://text.generation.language.few.shot.prompt.engineering.with.in.context.examples","name":"few-shot prompt engineering with in-context examples","description":"Enables in-context learning by embedding multiple input-output examples directly in the prompt text, allowing the model to infer task patterns without fine-tuning. The model's transformer attention mechanism learns from these examples during inference, adapting behavior to match the demonstrated pattern. This is a zero-cost adaptation mechanism compared to fine-tuning, relying on the model's ability to recognize and generalize from textual demonstrations.","intents":["Teach the model a custom task (e.g., sentiment classification, entity extraction) by showing 3-5 labeled examples","Adapt the model to domain-specific terminology or formatting without retraining","Perform one-shot or zero-shot learning by structuring prompts to guide behavior through examples"],"best_for":["teams prototyping NLP tasks without access to fine-tuning infrastructure","developers building rapid MVPs that need task adaptation without model retraining","researchers experimenting with prompt-based learning and emergent abilities"],"limitations":["Performance degrades with very long prompts (>2000 tokens) due to context window limits and attention dilution","Few-shot learning is less reliable than fine-tuning for complex or nuanced tasks","Examples must be manually crafted and validated — no automated example selection","Model may overfit to example formatting, failing on slightly different input structures"],"requires":["Understanding of prompt engineering best practices (clear instructions, consistent formatting)","Domain knowledge to select representative examples","API access with sufficient context window (4,096 tokens for GPT-3.5 Turbo Instruct)"],"input_types":["text (prompt with embedded examples in natural language or structured format)"],"output_types":["text (model-generated output following the pattern of examples)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-instruct__cap_2","uri":"capability://code.generation.editing.code.generation.from.natural.language.specifications","name":"code generation from natural language specifications","description":"Generates syntactically valid code in multiple programming languages (Python, JavaScript, SQL, etc.) from natural language descriptions, docstrings, or comments. The model uses its pre-training on code corpora to map semantic intent to implementation patterns, supporting both standalone function generation and multi-file code scaffolding. Output is raw text without syntax validation, requiring post-processing to verify correctness.","intents":["Generate boilerplate code (API endpoints, database queries, utility functions) from English descriptions","Complete code from docstrings or function signatures","Translate algorithms or pseudocode into executable implementations"],"best_for":["developers accelerating routine coding tasks (CRUD operations, API handlers)","teams building code generation tools or IDE plugins","non-expert programmers prototyping solutions quickly"],"limitations":["Generated code is not guaranteed to be syntactically correct or semantically sound — requires human review and testing","Knowledge cutoff at September 2021 means no awareness of recent language features, frameworks, or libraries released after that date","Struggles with complex multi-file refactoring or architectural decisions requiring global codebase understanding","No built-in linting, type checking, or security analysis — vulnerable code may be generated without warnings"],"requires":["API access to OpenAI or OpenRouter","Understanding of the target programming language and desired code structure","Code review and testing infrastructure to validate generated output"],"input_types":["text (natural language description, docstring, or pseudocode)"],"output_types":["code (raw text in target language, unvalidated)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-instruct__cap_3","uri":"capability://text.generation.language.creative.text.generation.with.temperature.controlled.sampling","name":"creative text generation with temperature-controlled sampling","description":"Generates diverse, creative text outputs (stories, poetry, marketing copy) using temperature and top-p sampling parameters to control randomness and diversity. Lower temperatures (0.0-0.5) produce deterministic, focused outputs; higher temperatures (0.7-1.0) introduce variability and creative divergence. The model samples from the probability distribution over tokens, with top-p (nucleus sampling) filtering to exclude low-probability tokens and reduce incoherence.","intents":["Generate multiple creative variations of marketing copy, product descriptions, or social media posts","Write narrative fiction or poetry with controlled randomness to avoid repetition","Produce brainstorming outputs for ideation tasks where diversity is valued"],"best_for":["content creators and copywriters using AI as a brainstorming tool","marketing teams generating product descriptions at scale","creative professionals exploring multiple narrative directions"],"limitations":["Higher temperatures increase hallucination risk — factual accuracy degrades with temperature > 0.8","Sampling is non-deterministic, making outputs unreproducible without fixed random seeds","No built-in fact-checking or consistency validation across generated variations","Creative quality is subjective and highly dependent on prompt engineering"],"requires":["API access with temperature and top_p parameter support","Understanding of temperature/top_p tuning for desired creativity level","Human editorial review to filter low-quality or off-brand outputs"],"input_types":["text (prompt with creative direction and constraints)"],"output_types":["text (creative content with variable diversity based on temperature)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-instruct__cap_4","uri":"capability://text.generation.language.summarization.and.text.condensation","name":"summarization and text condensation","description":"Condenses long-form text (articles, documents, transcripts) into shorter summaries while preserving key information. The model uses attention mechanisms to identify salient content and generates abstractive summaries (paraphrased, not extracted). Summarization quality depends on prompt clarity (e.g., 'Summarize in 100 words') and source text structure.","intents":["Extract key points from long documents, meeting transcripts, or research papers","Generate executive summaries for reports or articles","Condense customer feedback or support tickets into actionable insights"],"best_for":["knowledge workers processing large volumes of text daily","teams building document processing pipelines","researchers synthesizing literature reviews"],"limitations":["Abstractive summaries may introduce subtle inaccuracies or misinterpretations of source material","Context window limits (4,096 tokens) restrict input document length — longer documents must be chunked","No built-in multi-document summarization — requires separate API calls per document","Summary length is not strictly enforced; model may exceed requested token limits"],"requires":["API access to OpenAI or OpenRouter","Source text within context window limits","Clear summarization instructions (length, style, focus areas)"],"input_types":["text (long-form document, article, or transcript)"],"output_types":["text (abstractive summary)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-instruct__cap_5","uri":"capability://text.generation.language.question.answering.from.provided.context","name":"question-answering from provided context","description":"Answers questions based on provided context text (documents, knowledge bases, or reference material) by retrieving relevant information and generating natural language responses. The model uses attention over the context to identify answer-bearing passages and synthesizes responses without external retrieval. This is a closed-book QA approach where all information must be in the prompt.","intents":["Build customer support chatbots that answer questions from a knowledge base","Create document-based Q&A systems for internal documentation","Extract answers from research papers or technical specifications"],"best_for":["teams building internal knowledge base Q&A systems","customer support teams automating FAQ responses","developers prototyping RAG systems without vector databases"],"limitations":["Requires all relevant context in the prompt — no external retrieval, so context must be pre-selected","Performance degrades with very long context (>2000 tokens) due to attention dilution","May generate answers not supported by provided context (hallucination)","No ranking or confidence scoring — cannot distinguish high-confidence from low-confidence answers"],"requires":["API access to OpenAI or OpenRouter","Pre-selected context documents or knowledge base chunks","Clear question-answering instructions in the prompt"],"input_types":["text (context document + question)"],"output_types":["text (natural language answer)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-instruct__cap_6","uri":"capability://text.generation.language.text.classification.and.sentiment.analysis","name":"text classification and sentiment analysis","description":"Classifies text into predefined categories (sentiment, intent, topic, toxicity) by analyzing semantic content and returning category labels or confidence scores. The model uses learned representations to map input text to output classes, supporting both binary classification (positive/negative) and multi-class scenarios (5-star ratings, intent types). Classification is performed via prompt engineering (e.g., 'Classify as positive, negative, or neutral') without fine-tuning.","intents":["Classify customer reviews or feedback into sentiment categories","Detect intent from user messages (e.g., complaint, question, request)","Categorize support tickets by topic for routing"],"best_for":["teams building content moderation or sentiment analysis pipelines","customer support teams automating ticket classification","product teams analyzing user feedback at scale"],"limitations":["Classification accuracy depends heavily on prompt clarity and example quality","No built-in confidence scoring — cannot distinguish borderline cases","Struggles with sarcasm, cultural context, and domain-specific sentiment","Output format is unstructured text — requires post-processing to extract category labels"],"requires":["API access to OpenAI or OpenRouter","Clear classification instructions and category definitions in the prompt","Optional: few-shot examples for improved accuracy"],"input_types":["text (document or message to classify)"],"output_types":["text (category label or classification explanation)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-instruct__cap_7","uri":"capability://text.generation.language.language.translation.with.instruction.based.control","name":"language translation with instruction-based control","description":"Translates text between languages using instruction-based prompting (e.g., 'Translate to Spanish') without fine-tuning. The model leverages multilingual pre-training to map source language tokens to target language equivalents, preserving semantic meaning and tone. Translation quality varies by language pair and domain; common languages (English-Spanish, English-French) perform better than rare pairs.","intents":["Translate user-generated content (reviews, comments, support tickets) into English for analysis","Localize product documentation or marketing copy into multiple languages","Build multilingual chatbots that respond in the user's language"],"best_for":["teams building multilingual products or content platforms","companies localizing documentation for international markets","support teams handling multilingual customer inquiries"],"limitations":["Translation quality is lower than specialized translation models (e.g., DeepL, Google Translate) for technical or domain-specific content","Struggles with idioms, cultural references, and context-dependent meaning","No built-in quality scoring — cannot flag low-confidence translations","Rare language pairs (e.g., English-Icelandic) may produce poor results"],"requires":["API access to OpenAI or OpenRouter","Source text in a language the model understands","Clear translation instructions (target language, tone, domain)"],"input_types":["text (source language content)"],"output_types":["text (target language translation)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key or OpenRouter API key with OpenAI provider configured","HTTP client library (curl, requests, axios, etc.) or OpenAI SDK (Python 1.0+, Node.js 4.0+)","Understanding of completion API semantics (prompt, max_tokens, temperature, top_p parameters)","Understanding of prompt engineering best practices (clear instructions, consistent formatting)","Domain knowledge to select representative examples","API access with sufficient context window (4,096 tokens for GPT-3.5 Turbo Instruct)","API access to OpenAI or OpenRouter","Understanding of the target programming language and desired code structure","Code review and testing infrastructure to validate generated output","API access with temperature and top_p parameter support"],"failure_modes":["No built-in conversation memory or multi-turn context management — each request is stateless","Training data cutoff at September 2021 means no knowledge of events, APIs, or libraries after that date","Completion API lacks structured output guarantees — requires post-processing to extract JSON or code","No native function calling or tool use integration — must be implemented via prompt engineering","Slower inference than GPT-4 Turbo for complex reasoning tasks requiring chain-of-thought","Performance degrades with very long prompts (>2000 tokens) due to context window limits and attention dilution","Few-shot learning is less reliable than fine-tuning for complex or nuanced tasks","Examples must be manually crafted and validated — no automated example selection","Model may overfit to example formatting, failing on slightly different input structures","Generated code is not guaranteed to be syntactically correct or semantically sound — requires human review and testing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"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-instruct","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-3.5-turbo-instruct"}},"signature":"+tVUU7J9y167M98V5A41XhH8kVKNbj02I/mzDarhnaD0hXD+yBVSkutKxtF2KL4r93tUPHxS7rO6TUbXxTD+BQ==","signedAt":"2026-06-21T18:52:53.155Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-3.5-turbo-instruct","artifact":"https://unfragile.ai/openai-gpt-3.5-turbo-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-3.5-turbo-instruct","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"}}