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When activated, the model produces verbose step-by-step explanations with intermediate conclusions, implemented via training on synthetic reasoning datasets and reinforced through process-reward modeling to prefer logically sound intermediate steps.","intents":["I need the model to show its work and explain complex reasoning step-by-step","I want to verify model reasoning by inspecting intermediate conclusions before final answers","I need to debug why a model reached a particular conclusion on a complex problem"],"best_for":["educational applications where reasoning transparency is critical","safety-critical domains requiring auditable decision chains","developers building interpretability tools or model evaluation frameworks"],"limitations":["reasoning chains increase output token count by 3-5x, significantly raising inference costs","extended reasoning mode has ~40% higher latency than direct generation","reasoning quality degrades on tasks outside training distribution (e.g., highly specialized domains)","intermediate steps may contain plausible-sounding but incorrect reasoning that requires human validation"],"requires":["OpenRouter API key with sufficient token quota for extended outputs","client supporting streaming to handle longer response times gracefully","context window of at least 16K tokens recommended for complex multi-step problems"],"input_types":["text (natural language questions)","code (debugging, optimization analysis)","mathematical problems","logical reasoning tasks"],"output_types":["text with explicit reasoning steps","structured reasoning traces with step labels","code with inline explanation comments"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nousresearch-hermes-4-70b__cap_10","uri":"capability://planning.reasoning.question.answering.with.reasoning","name":"question-answering-with-reasoning","description":"Answers factual and reasoning-based questions by retrieving relevant knowledge and applying logical deduction. The model combines pattern matching from training data with reasoning chains to synthesize answers, particularly effective when questions require multi-step inference or combining information from multiple domains.","intents":["I need accurate answers to factual questions across diverse domains","I want the model to explain its reasoning for complex questions","I need to build a Q&A system that handles both factual lookup and reasoning questions"],"best_for":["developers building Q&A systems or knowledge bases","teams creating customer support chatbots with knowledge integration","builders implementing educational tutoring systems"],"limitations":["factual accuracy is limited to training data cutoff (knowledge cutoff ~early 2024); no real-time information","reasoning questions have ~85% accuracy; complex multi-step inference often contains errors","no built-in fact verification; answers may sound confident but be factually incorrect","domain-specific questions (specialized medicine, cutting-edge research) have lower accuracy"],"requires":["OpenRouter API key","context window of 8K+ tokens","optional: external knowledge base or RAG system for real-time information"],"input_types":["text (natural language questions)","structured queries (JSON with question type and context)","context documents (for Q&A over specific texts)"],"output_types":["text (answers with or without reasoning)","structured answers (JSON with answer + confidence)","reasoning chains (step-by-step explanation)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nousresearch-hermes-4-70b__cap_11","uri":"capability://text.generation.language.sentiment.analysis.and.opinion.extraction","name":"sentiment-analysis-and-opinion-extraction","description":"Analyzes sentiment and extracts opinions from text, classifying emotional tone and identifying specific viewpoints or attitudes. 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The model applies learned safety patterns to classify content risk levels and flag problematic material, implemented through instruction-tuning on safety datasets and reinforcement learning from human feedback on safety preferences.","intents":["I need to filter user-generated content for policy violations","I want to identify potentially harmful content before it's published","I need to classify content by risk level (safe/warning/blocked)"],"best_for":["platforms moderating user-generated content","teams building safety-critical applications","developers implementing content governance systems"],"limitations":["moderation accuracy is ~90%; false positives and false negatives both occur","cultural and contextual nuances are often missed; sarcasm or irony may be misclassified","no support for emerging harms or novel attack patterns; relies on training data","moderation decisions are not explainable; model provides classification but not reasoning"],"requires":["OpenRouter API key","context window of 4K+ tokens","human review process for edge cases and appeals"],"input_types":["text (user posts, comments, messages)","structured content (JSON with text field)"],"output_types":["safety classification (safe/warning/blocked)","risk scores","violation categories (hate speech, violence, etc.)","structured moderation decisions (JSON)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nousresearch-hermes-4-70b__cap_2","uri":"capability://text.generation.language.instruction.following.with.format.control","name":"instruction-following-with-format-control","description":"Executes complex multi-part instructions with precise output formatting, using instruction-tuning techniques to reliably parse structured prompts and generate outputs matching specified schemas. 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The model uses symbolic reasoning patterns learned from mathematical datasets, showing work through explicit equation manipulation and logical deduction steps rather than direct answer generation.","intents":["I need the model to solve math problems and show all steps","I want to verify mathematical reasoning by inspecting intermediate calculations","I need symbolic manipulation (simplification, factoring, solving equations)"],"best_for":["educational platforms providing tutoring or homework assistance","researchers using LLMs for mathematical exploration and discovery","developers building math-heavy applications (finance, engineering, physics simulations)"],"limitations":["mathematical reasoning is reliable for high-school and early undergraduate level; graduate-level proofs often contain errors","symbolic computation is limited to algebraic manipulation; numerical methods and advanced calculus are weaker","no integration with computer algebra systems (Mathematica, SymPy); purely text-based reasoning","probability and statistics problems have ~70% accuracy; combinatorics is particularly weak"],"requires":["OpenRouter API key","context window of 8K+ tokens for multi-step problems","optional: symbolic math library (SymPy) for validation of generated solutions"],"input_types":["text (natural language math problems)","mathematical notation (LaTeX, ASCII math)","equations and formulas","word problems with implicit mathematical structure"],"output_types":["step-by-step solutions with intermediate steps","mathematical notation and equations","numerical answers with derivations","multiple solution approaches"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nousresearch-hermes-4-70b__cap_5","uri":"capability://text.generation.language.multi.turn.conversation.with.context.retention","name":"multi-turn-conversation-with-context-retention","description":"Maintains coherent multi-turn conversations by tracking conversation history and building context across exchanges. The model uses standard transformer attention mechanisms to weight recent messages more heavily while retaining key facts from earlier turns, implemented through careful prompt formatting that preserves conversation structure within the context window.","intents":["I need the model to remember facts and context from earlier in the conversation","I want natural back-and-forth dialogue without re-explaining context each turn","I need the model to correct itself or refine answers based on user feedback across turns"],"best_for":["developers building chatbot applications with multi-turn interactions","teams creating conversational AI for customer support or tutoring","builders implementing dialogue systems where context accumulation is critical"],"limitations":["context retention degrades after 20-30 turns due to context window limits (8K-16K tokens)","model may lose track of facts introduced early in conversation if later turns are verbose","no explicit memory mechanism; all context must fit within the context window","conversation history must be managed by the client; the model has no persistent memory across sessions"],"requires":["OpenRouter API key","client-side conversation history management (list of messages with roles)","context window of at least 8K tokens; 16K+ recommended for long conversations","message formatting following OpenAI chat API conventions (system/user/assistant roles)"],"input_types":["text (user messages in multi-turn format)","conversation history (prior messages with roles)","system prompts defining conversation behavior"],"output_types":["text (assistant responses)","structured conversation turns with metadata","streaming responses for real-time interaction"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nousresearch-hermes-4-70b__cap_6","uri":"capability://tool.use.integration.function.calling.and.tool.use","name":"function-calling-and-tool-use","description":"Generates structured function calls in JSON format to invoke external tools and APIs, parsing natural language requests into executable tool invocations. The model learns to map user intents to appropriate functions by recognizing function signatures provided in the prompt, generating valid JSON that downstream systems can parse and execute.","intents":["I need the model to decide which API to call based on user requests","I want to build an agent that uses tools to answer questions (search, calculator, database queries)","I need the model to generate function calls that my application can execute"],"best_for":["developers building LLM agents with external tool integration","teams creating AI-powered applications that need to interact with APIs and databases","builders implementing autonomous workflows where the model decides which tools to use"],"limitations":["function calling reliability is ~90%; the model occasionally generates malformed JSON or calls non-existent functions","no native support for complex nested function calls; sequential tool use requires multiple model invocations","function signature understanding is limited to simple parameters; complex type systems (generics, unions) may confuse the model","no built-in error handling; if a tool call fails, the model doesn't automatically retry or adjust"],"requires":["OpenRouter API key","function definitions provided in prompt (JSON schema format)","client-side function registry to execute generated calls","error handling logic to manage failed tool invocations"],"input_types":["text (natural language requests)","function definitions (JSON schema)","tool descriptions with parameter specifications"],"output_types":["JSON (function calls with parameters)","text (reasoning about which tool to use)","mixed (reasoning + function calls)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nousresearch-hermes-4-70b__cap_7","uri":"capability://text.generation.language.summarization.and.content.condensation","name":"summarization-and-content-condensation","description":"Condenses long documents, articles, or conversations into concise summaries while preserving key information. The model learns to identify salient facts and main ideas through training on summarization datasets, generating summaries at configurable length (bullet points, paragraphs, or single-sentence abstracts) while maintaining factual accuracy.","intents":["I need to summarize long documents quickly without reading the full text","I want bullet-point summaries of articles for quick scanning","I need to extract key takeaways from meeting transcripts or research papers"],"best_for":["knowledge workers processing large volumes of documents","teams building document management systems with AI-powered summaries","developers creating content curation or news aggregation applications"],"limitations":["summaries may omit important nuances or context from source material","factual accuracy is ~95%; occasional hallucinations or misrepresentations occur","summarization quality degrades on highly technical or domain-specific content","very long documents (>50K tokens) may exceed context window; requires chunking and multi-pass summarization"],"requires":["OpenRouter API key","context window of 8K+ tokens; 16K+ for documents longer than 5K tokens","optional: source text chunking logic for documents exceeding context limits"],"input_types":["text (articles, documents, transcripts)","structured content (markdown, HTML)","conversation logs"],"output_types":["text (paragraph summaries)","bullet points","single-sentence abstracts","structured summaries (JSON with key sections)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nousresearch-hermes-4-70b__cap_8","uri":"capability://text.generation.language.translation.and.multilingual.generation","name":"translation-and-multilingual-generation","description":"Translates text between 50+ languages and generates content in non-English languages with cultural and linguistic appropriateness. 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