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The model is fine-tuned to understand search intent signals in user prompts and automatically determine when web search is necessary versus when cached knowledge suffices, reducing unnecessary API calls while maintaining factual accuracy on time-sensitive queries.","intents":["I need the model to answer questions about current events without hallucinating outdated information","I want to build a chatbot that can cite recent news, stock prices, or live data without manual search integration","I need to reduce hallucinations on factual queries by grounding responses in real-time web results"],"best_for":["teams building customer-facing chatbots requiring current information","developers creating research assistants or news aggregators","builders needing fact-grounded responses without implementing custom search infrastructure"],"limitations":["Search latency adds 500ms-2s per request depending on query complexity and result availability","Web search results are limited to publicly indexed content; cannot search private databases or paywalled sources","Model's search query generation may miss nuanced information needs requiring multi-step search strategies","No control over search result ranking or filtering — uses OpenAI's internal search provider","Context window injection of search results reduces available tokens for user history and long-form responses"],"requires":["OpenAI API key with access to gpt-4o-mini-search-preview model","Chat Completions API endpoint (https://api.openai.com/v1/chat/completions)","Network connectivity for real-time web search execution","Appropriate rate limits and quota allocation for search-augmented requests"],"input_types":["text (natural language queries)","structured chat messages with system/user/assistant roles"],"output_types":["text (natural language response)","structured JSON with optional search metadata and citations"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-search-preview__cap_1","uri":"capability://planning.reasoning.search.intent.recognition.and.routing","name":"search-intent-recognition-and-routing","description":"The model internally classifies incoming queries to determine whether web search is required or if existing knowledge is sufficient, using learned patterns from training data to identify temporal signals (dates, 'latest', 'current'), factual domains (news, prices, events), and explicit search indicators. This routing decision happens before search execution, allowing the model to skip unnecessary searches and preserve context window tokens for queries answerable from training data.","intents":["I want the model to intelligently decide when to search the web versus use its training knowledge","I need to minimize search API calls and latency for queries that don't require current information","I want responses that cite whether information comes from web search or training data"],"best_for":["cost-conscious teams wanting to reduce search overhead per request","applications requiring low-latency responses where search is only triggered for time-sensitive queries","builders implementing transparent AI systems that distinguish between real-time and historical knowledge"],"limitations":["Intent recognition is probabilistic and may incorrectly classify edge cases (e.g., 'what was the latest version' vs. 'what is the latest version')","No explicit control over search routing — developers cannot override the model's search/no-search decision","Model may fail to recognize domain-specific search needs if not represented in training data","Routing logic is opaque; no visibility into why a particular query triggered or skipped search"],"requires":["OpenAI API key with gpt-4o-mini-search-preview access","Chat Completions API endpoint","Queries formatted as standard chat messages"],"input_types":["text (natural language queries with implicit or explicit temporal/factual signals)"],"output_types":["text (response with implicit search execution)","optional metadata indicating search was performed"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-search-preview__cap_2","uri":"capability://memory.knowledge.context.window.aware.search.result.injection","name":"context-window-aware-search-result-injection","description":"Integrates web search results into the model's context window by formatting retrieved pages, snippets, and metadata into structured chunks that fit within token limits while preserving relevance ranking. The injection mechanism prioritizes high-relevance results and compresses verbose content to maximize space for user history and multi-turn conversation context, using a learned compression strategy to balance result fidelity with context availability.","intents":["I need search results to be seamlessly integrated into the model's reasoning without losing conversation history","I want the model to cite specific sources from search results in its response","I need to maintain multi-turn conversations while augmenting with real-time web data"],"best_for":["conversational AI systems requiring both context history and real-time information","applications where source attribution and transparency are critical","teams building research assistants that need to balance conversation depth with factual grounding"],"limitations":["Search result injection reduces available tokens for user message history — long conversations may lose earlier context","Result compression may lose nuance or detail from original sources","No explicit control over which results are injected or how they are prioritized","Token counting for search results is opaque — developers cannot predict exact context consumption","Search results are injected as-is without deduplication or conflict resolution if multiple results contradict"],"requires":["OpenAI API key with gpt-4o-mini-search-preview access","Chat Completions API with sufficient context window (128k tokens)","Queries that trigger search intent recognition"],"input_types":["web search results (URLs, snippets, metadata from OpenAI's search provider)"],"output_types":["text (response incorporating search results)","implicit citations or source references in generated text"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-search-preview__cap_3","uri":"capability://search.retrieval.real.time.factual.grounding.with.citation.support","name":"real-time-factual-grounding-with-citation-support","description":"Grounds model responses in real-time web data by retrieving current facts and enabling the model to cite sources directly from search results, reducing hallucinations on time-sensitive queries. The model is trained to recognize when citations are appropriate and to reference specific URLs, publication dates, or snippet text from search results, providing transparency about information provenance and allowing users to verify claims.","intents":["I need responses about current events, prices, or breaking news with proper source attribution","I want to reduce hallucinations by grounding answers in verifiable web sources","I need to build systems where users can click through to original sources"],"best_for":["news aggregators, financial advisors, or research tools requiring current data","customer support systems handling time-sensitive inquiries","applications where user trust depends on transparent source attribution"],"limitations":["Citation accuracy depends on search result quality — if search returns incorrect or outdated results, citations may be misleading","Model may hallucinate citations to non-existent sources or misattribute information","No guarantee that cited sources remain accessible or unchanged after response generation","Search results are limited to publicly indexed content — cannot cite proprietary or paywalled sources","Citation format and structure are not explicitly controlled — developers cannot enforce specific citation standards"],"requires":["OpenAI API key with gpt-4o-mini-search-preview access","Chat Completions API endpoint","Queries that trigger web search intent"],"input_types":["text (natural language queries about current events, facts, or time-sensitive information)"],"output_types":["text (response with embedded citations)","structured citations with URLs and metadata"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-search-preview__cap_4","uri":"capability://memory.knowledge.multi.turn.conversation.with.search.augmentation","name":"multi-turn-conversation-with-search-augmentation","description":"Maintains conversation history across multiple turns while selectively augmenting individual user messages with web search results, allowing the model to reference earlier context and build on previous responses while incorporating real-time data. The model tracks conversation state and determines which turns require search augmentation, avoiding redundant searches for follow-up questions that can be answered from earlier search results or training knowledge.","intents":["I want to have a multi-turn conversation where some messages are augmented with web search and others use training knowledge","I need the model to remember earlier context while answering follow-up questions about current events","I want to avoid re-searching for information already retrieved in earlier conversation turns"],"best_for":["conversational chatbots and research assistants requiring sustained context","applications where users ask follow-up questions about search results","teams building interactive tools where conversation depth matters"],"limitations":["Context window limits mean very long conversations will lose earlier messages even with search augmentation","Search results from earlier turns are not automatically reused — the model may re-search for similar information","No explicit conversation state management — developers cannot inspect or modify which turns were searched","Multi-turn search routing is opaque — no visibility into why certain turns triggered search while others didn't","Search result freshness may vary across turns if conversation spans extended time periods"],"requires":["OpenAI API key with gpt-4o-mini-search-preview access","Chat Completions API with conversation history support","Properly formatted message arrays with role and content fields"],"input_types":["text (chat messages with user, assistant, and system roles)"],"output_types":["text (response incorporating both conversation history and search results)"],"categories":["memory-knowledge","search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-search-preview__cap_5","uri":"capability://tool.use.integration.openai.chat.completions.api.integration","name":"openai-chat-completions-api-integration","description":"Integrates with OpenAI's Chat Completions API using standard request/response formats, supporting all Chat Completions parameters (temperature, max_tokens, top_p, etc.) while transparently handling search augmentation in the backend. The model accepts standard chat message arrays and returns responses in the same format as other GPT models, with optional metadata indicating search was performed, enabling drop-in replacement for existing Chat Completions workflows.","intents":["I want to use a search-augmented model without changing my existing Chat Completions integration","I need to migrate from standard GPT-4o-mini to the search preview with minimal code changes","I want to control model behavior using standard Chat Completions parameters"],"best_for":["teams with existing Chat Completions implementations wanting to add search capability","developers building multi-model systems where API consistency is important","applications requiring standard OpenAI API compatibility"],"limitations":["Search behavior cannot be explicitly controlled via API parameters — search routing is entirely model-driven","No explicit search configuration options (e.g., result count, domain filters, freshness constraints)","Response format does not include explicit search metadata by default — search execution is opaque","Model is only available through OpenAI's API — no local or self-hosted deployment option","Search functionality is tied to OpenAI's internal search provider — no option to use alternative search engines"],"requires":["OpenAI API key with gpt-4o-mini-search-preview model access","HTTPS connectivity to https://api.openai.com/v1/chat/completions","Standard Chat Completions request format (messages array with role/content)","Python, JavaScript, or other language with OpenAI SDK support"],"input_types":["JSON (Chat Completions request with messages, model, temperature, etc.)"],"output_types":["JSON (Chat Completions response with choices, usage, finish_reason)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with access to gpt-4o-mini-search-preview model","Chat Completions API endpoint (https://api.openai.com/v1/chat/completions)","Network connectivity for real-time web search execution","Appropriate rate limits and quota allocation for search-augmented requests","OpenAI API key with gpt-4o-mini-search-preview access","Chat Completions API endpoint","Queries formatted as standard chat messages","Chat Completions API with sufficient context window (128k tokens)","Queries that trigger search intent recognition","Queries that trigger web search intent"],"failure_modes":["Search latency adds 500ms-2s per request depending on query complexity and result availability","Web search results are limited to publicly indexed content; 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