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The model applies learned patterns from code repositories and documentation to produce syntactically valid and contextually appropriate code blocks, API examples, and technical explanations. Supports inline code generation within conversational responses and can generate complete functions, classes, or multi-file projects when provided sufficient context.","intents":["I need to generate boilerplate code or function implementations from natural language descriptions","I want to create code examples or API documentation snippets programmatically","I need to refactor or explain existing code in a conversational context"],"best_for":["developers using LLM-assisted coding workflows for rapid prototyping","technical writers automating code example generation for documentation","teams building code generation pipelines or IDE integrations"],"limitations":["no syntax validation or compilation; generated code may contain logical errors or syntax mistakes requiring manual review","limited to languages in training data; obscure or very new languages may produce lower-quality output","no built-in testing or execution environment; developers must validate generated code independently","context-dependent quality; code quality degrades significantly when requirements are ambiguous or when context window is nearly full"],"requires":["OpenAI API key with GPT-3.5 Turbo 16k access","Clear code requirements or examples in the prompt","Manual code review and testing infrastructure"],"input_types":["natural language code requirements","existing code snippets for refactoring or explanation","technical specifications or API documentation"],"output_types":["code (Python, JavaScript, Java, C++, etc.)","code snippets and examples","technical documentation and explanations"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-16k__cap_3","uri":"capability://text.generation.language.semantic.understanding.and.reasoning.over.long.documents","name":"semantic understanding and reasoning over long documents","description":"Analyzes and reasons about extended text documents (up to 16k tokens) by computing semantic representations across the full input and applying learned reasoning patterns to answer questions, extract information, and synthesize insights. The model's attention mechanism enables it to identify relationships between distant parts of a document and perform multi-step reasoning without requiring external knowledge retrieval or summarization preprocessing.","intents":["I need to ask questions about a long document and get answers that reference specific sections","I want to extract structured information from a lengthy text without manual parsing","I need to identify contradictions or inconsistencies across a long document"],"best_for":["legal and compliance teams analyzing lengthy contracts or regulatory documents","researchers extracting insights from academic papers or technical specifications","customer support teams analyzing long transcripts or support tickets for issue resolution"],"limitations":["reasoning quality depends on document clarity and structure; poorly formatted or ambiguous documents may produce unreliable outputs","no external knowledge integration; cannot fact-check against real-world data or current information","reasoning errors can occur on complex multi-step inferences; no built-in verification or confidence scoring","performance degrades when reasoning requires understanding of very specialized domain knowledge not well-represented in training data"],"requires":["OpenAI API key with GPT-3.5 Turbo 16k access","Document text in plain text, markdown, or structured format","Clear questions or analysis prompts"],"input_types":["long-form text documents","structured documents (markdown, HTML, JSON)","natural language questions or analysis prompts"],"output_types":["natural language answers with document citations","extracted structured data","analytical summaries and insights"],"categories":["text-generation-language","planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-16k__cap_4","uri":"capability://text.generation.language.instruction.following.with.system.prompt.behavioral.steering","name":"instruction-following with system prompt behavioral steering","description":"Implements behavioral control through system prompts that establish role, tone, constraints, and output format expectations. The system message is processed as a special token sequence that influences the model's attention and generation patterns across all subsequent user messages in the conversation. This enables reliable behavioral steering without fine-tuning, allowing developers to specify custom personas, response styles, and operational constraints that persist across multiple turns.","intents":["I want to create a chatbot with a specific personality or role (e.g., technical expert, creative writer, customer service agent)","I need to enforce output format constraints (e.g., JSON, markdown, specific structure) across all responses","I want to set safety or content boundaries that apply to the entire conversation"],"best_for":["developers building specialized chatbots with consistent personas or roles","teams implementing domain-specific assistants (legal, medical, technical support)","builders creating structured output pipelines where format consistency is critical"],"limitations":["system prompt effectiveness varies with prompt quality; poorly written system prompts may be ignored or inconsistently applied","no guarantee of format compliance; model may deviate from specified output formats, especially under conflicting user instructions","system prompt tokens consume part of the 16k context window; very long system prompts reduce available space for conversation history","behavioral steering is probabilistic; same system prompt may produce slightly different behaviors across multiple requests"],"requires":["OpenAI API key with GPT-3.5 Turbo 16k access","Well-crafted system prompt with clear instructions","Output validation logic to verify format compliance"],"input_types":["system prompt (text with role and behavioral instructions)","user messages"],"output_types":["text responses adhering to system prompt constraints","structured output (JSON, markdown, etc.) if specified in system prompt"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-3.5-turbo-16k__cap_5","uri":"capability://tool.use.integration.cost.optimized.api.access.with.token.based.billing","name":"cost-optimized api access with token-based billing","description":"Provides API access to GPT-3.5 Turbo 16k through OpenAI's token-based pricing model, where costs scale linearly with input and output token consumption. Developers pay only for tokens used, with separate rates for input tokens (cheaper) and output tokens (more expensive), enabling cost-predictable inference at scale. The 16k variant costs approximately 4x more than the base 4k model but provides proportional context expansion.","intents":["I need to estimate and control API costs for my LLM application","I want to choose between context window sizes based on cost-benefit tradeoffs","I need to implement token counting and budget management in my application"],"best_for":["startups and small teams with limited budgets optimizing for cost per inference","developers building high-volume applications where token efficiency directly impacts margins","teams evaluating LLM model choices based on cost-performance tradeoffs"],"limitations":["16k variant is 4x more expensive than base GPT-3.5 Turbo (4k), making it unsuitable for cost-sensitive applications with short contexts","no volume discounts or reserved capacity pricing; costs scale linearly regardless of usage volume","token counting requires external library (tiktoken) or manual estimation; no built-in cost prediction in API responses","no cost controls or spending limits in the API itself; developers must implement application-level budget management"],"requires":["OpenAI API key with billing enabled","Credit card or billing account with OpenAI","Token counting library (tiktoken) for cost estimation","Application-level budget tracking and rate limiting"],"input_types":["API requests with token counts"],"output_types":["billing data (tokens used, cost per request)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-3.5 Turbo 16k access","HTTP client library (Python requests, Node.js axios, etc.)","Token counting library to stay within 16k limit (tiktoken or equivalent)","JSON serialization for message objects","Application-level conversation history storage (in-memory, database, or cache)","Clear code requirements or examples in the prompt","Manual code review and testing infrastructure","Document text in plain text, markdown, or structured format","Clear questions or analysis prompts","Well-crafted system prompt with clear instructions"],"failure_modes":["16k token limit still insufficient for very large documents (>50 pages); requires chunking or summarization for larger inputs","latency increases non-linearly with context length due to quadratic attention complexity; ~2-3x slower than 4k-token variant at max capacity","pricing scales with token usage; processing full 16k window costs ~4x more than equivalent 4k-token request","no built-in document chunking or sliding-window management; developers must implement their own context management strategy","no built-in conversation persistence; developers must implement external storage (database, file system) to save/restore message history","message history grows linearly with conversation length; old messages consume tokens even if irrelevant, requiring manual pruning or summarization","role-based formatting is rigid (system/user/assistant only); no native support for custom roles or multi-agent message types","no automatic context window overflow handling; developers must manually truncate or summarize when approaching 16k limit","no syntax validation or compilation; generated code may contain logical errors or syntax mistakes requiring manual review","limited to languages in training data; obscure or very new languages may produce lower-quality output","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"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-16k","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-3.5-turbo-16k"}},"signature":"MPEGPAYYXlZeVVwtuqKPDCU9R9mtMzT1jpv1Rqf7LX0sTRybiPFKN0xC/OGeVY96EgpJCUEn82MoXS2bqhVBAA==","signedAt":"2026-06-20T11:09:44.798Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-3.5-turbo-16k","artifact":"https://unfragile.ai/openai-gpt-3.5-turbo-16k","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-3.5-turbo-16k","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"}}