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This sparse activation pattern reduces computational cost and memory bandwidth compared to dense models while maintaining parameter capacity for diverse reasoning tasks.","intents":["Deploy a capable language model with lower inference latency and memory footprint than dense 20B+ parameter models","Run inference on resource-constrained hardware while maintaining reasoning quality across diverse domains","Reduce per-token inference cost by leveraging sparse computation patterns in MoE routing"],"best_for":["Teams building cost-sensitive production chatbots and assistants","Developers optimizing inference for edge deployment or high-throughput serving","Organizations seeking open-weight alternatives to proprietary dense models with similar capability"],"limitations":["MoE routing adds ~5-15ms latency overhead per forward pass due to gating computation and expert selection","Sparse activation patterns may reduce performance on tasks requiring dense cross-expert knowledge fusion","Load balancing across experts can create uneven GPU utilization if token distribution skews toward fewer experts","Fine-tuning MoE models requires careful handling of expert collapse (all tokens routing to same expert)"],"requires":["OpenRouter API key or direct model access via compatible inference framework","CUDA 11.8+ for GPU inference, or sufficient CPU memory (>40GB) for CPU-only inference","Inference framework supporting MoE routing (vLLM, TensorRT-LLM, or similar with expert parallelism support)"],"input_types":["text (natural language prompts, code snippets, structured queries)"],"output_types":["text (natural language responses, code generation, reasoning chains)"],"categories":["text-generation-language","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.context.window.management","name":"multi-turn conversational reasoning with context window management","description":"Maintains coherent multi-turn dialogue by processing conversation history within a fixed context window, using attention mechanisms to weight recent and relevant prior messages while discarding or summarizing older context when token limits are approached. The model learns to extract key information from conversation history to maintain semantic continuity across turns.","intents":["Build stateful chatbots that remember and reference earlier conversation turns without external memory systems","Implement conversational agents that reason over dialogue history to provide contextually appropriate responses","Create interactive assistants where users expect natural back-and-forth without explicit context resets"],"best_for":["Developers building customer support chatbots and conversational interfaces","Teams creating interactive coding assistants that reference previous code exchanges","Builders of multi-turn reasoning systems where conversation history is essential to task completion"],"limitations":["Fixed context window (typically 4K-8K tokens) limits conversation length before older turns are lost or must be summarized","No persistent memory across sessions — each new conversation starts without prior context","Long conversation histories increase per-token latency due to larger attention computation","Context window exhaustion requires manual conversation pruning or external memory integration"],"requires":["OpenRouter API key or compatible inference endpoint","Client-side conversation history management (array of message objects with role and content)","Token counting utility to track context window usage (e.g., tiktoken for OpenAI-compatible models)"],"input_types":["text (user messages, system prompts, conversation history)"],"output_types":["text (assistant responses, reasoning traces)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_2","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Generates syntactically valid code across multiple programming languages by learning patterns from training data that includes code repositories, technical documentation, and problem-solution pairs. The model applies language-specific reasoning to produce working implementations, debug explanations, and architectural suggestions for technical problems.","intents":["Generate code snippets and complete functions from natural language descriptions or partial implementations","Explain and debug existing code by analyzing syntax, logic errors, and suggesting fixes","Provide technical solutions to programming problems across Python, JavaScript, SQL, and other languages"],"best_for":["Solo developers and small teams using AI-assisted coding workflows","Technical support teams automating code review and debugging assistance","Educators building interactive coding tutors and automated grading systems"],"limitations":["Code generation quality varies by language — performs better on Python/JavaScript than niche languages","No real-time code execution or validation — generated code may have runtime errors not caught by syntax analysis","Cannot access external libraries or package documentation beyond training data cutoff","Struggles with complex multi-file refactoring or architectural decisions requiring deep codebase knowledge"],"requires":["OpenRouter API key","Code context (file snippets, error messages, or problem descriptions) as text input","Optional: syntax highlighter or IDE integration for formatting generated code"],"input_types":["text (natural language problem descriptions, code snippets, error messages, technical questions)"],"output_types":["text (code blocks, explanations, debugging suggestions, architectural recommendations)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_3","uri":"capability://text.generation.language.knowledge.synthesis.and.question.answering.across.domains","name":"knowledge synthesis and question-answering across domains","description":"Answers factual and conceptual questions by retrieving and synthesizing relevant knowledge from training data, applying reasoning to connect concepts across domains. The model generates coherent explanations that cite reasoning steps and provide context-appropriate detail levels based on question complexity.","intents":["Answer user questions on diverse topics (science, history, technology, business) with accurate, sourced explanations","Synthesize information across multiple domains to answer complex 'how' and 'why' questions","Provide educational explanations that break down complex concepts into understandable components"],"best_for":["Knowledge workers and researchers seeking quick synthesis of information across domains","Educational platforms building AI tutors and homework assistance tools","Customer support systems providing product and domain knowledge to end users"],"limitations":["Knowledge cutoff at training time — cannot answer questions about events after model training","No access to real-time information, current prices, or live data sources","May hallucinate plausible-sounding but incorrect facts, especially on niche or recent topics","Cannot verify claims against external sources without external RAG integration"],"requires":["OpenRouter API key","Question or topic as natural language text input","Optional: external knowledge base or RAG system for fact-checking and current information"],"input_types":["text (questions, topics, prompts requesting explanation or synthesis)"],"output_types":["text (explanations, answers, reasoning chains, educational content)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_4","uri":"capability://text.generation.language.instruction.following.and.task.decomposition","name":"instruction-following and task decomposition","description":"Interprets complex, multi-step instructions and decomposes them into executable sub-tasks, then generates outputs following specified constraints (format, length, tone, structure). The model learns to parse instruction syntax, identify priorities, and handle edge cases like conflicting constraints or ambiguous requirements.","intents":["Execute complex workflows where users specify detailed requirements (e.g., 'write a 500-word blog post in technical tone with 3 sections')","Build agents that break down high-level goals into concrete steps and execute them sequentially","Create systems that follow strict output formatting requirements (JSON, CSV, markdown, etc.)"],"best_for":["Automation engineers building instruction-driven workflows and agents","Content teams using AI for structured content generation with specific requirements","Developers building AI systems that must follow precise output specifications"],"limitations":["Complex nested instructions with many constraints may be misinterpreted or partially ignored","No persistent state between instruction steps — each step is computed independently","Struggles with instructions requiring external tool calls or real-time information lookup","May prioritize early constraints over later ones if they conflict"],"requires":["OpenRouter API key","Well-structured, clear instructions as text input","Optional: system prompt to set context and constraint priorities"],"input_types":["text (instructions, requirements, constraints, examples)"],"output_types":["text (structured output, formatted responses, task results)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_5","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Generates original creative content (stories, poetry, marketing copy, dialogue) by learning stylistic patterns, narrative structures, and genre conventions from training data. The model applies learned constraints (rhyme schemes, character consistency, tone) to produce coherent creative outputs that match specified requirements.","intents":["Generate creative writing (short stories, poetry, scripts) with specified themes, genres, or constraints","Create marketing copy, product descriptions, and promotional content with brand voice consistency","Write dialogue for characters, games, or interactive fiction with personality consistency"],"best_for":["Content creators and marketing teams using AI for ideation and draft generation","Game developers generating NPC dialogue and narrative content","Authors using AI for brainstorming and draft expansion"],"limitations":["Generated content may lack originality or repeat common tropes from training data","Long-form creative works (novels, screenplays) may lose coherence or character consistency beyond context window","Struggles with highly specialized genres or niche writing styles with limited training examples","No built-in fact-checking — creative content may contain factual errors if based on false premises"],"requires":["OpenRouter API key","Creative prompt or brief (theme, genre, style, constraints) as text input","Optional: examples of desired style or tone for few-shot prompting"],"input_types":["text (creative prompts, themes, style descriptions, character briefs)"],"output_types":["text (creative writing, dialogue, marketing copy, narrative content)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_6","uri":"capability://text.generation.language.summarization.and.information.extraction","name":"summarization and information extraction","description":"Condenses long-form text into concise summaries by identifying key information, removing redundancy, and preserving essential meaning. The model learns to extract structured information (entities, relationships, facts) from unstructured text and present it in specified formats (bullet points, JSON, tables).","intents":["Summarize long documents, articles, or conversations into key points or executive summaries","Extract structured data (names, dates, amounts, relationships) from unstructured text","Generate table-of-contents or outline from long-form content"],"best_for":["Knowledge workers processing large volumes of documents or research papers","Legal and compliance teams extracting key terms and obligations from contracts","Data teams building information extraction pipelines for unstructured text"],"limitations":["Summarization quality degrades for very long documents (>8K tokens) due to context window limits","May miss important details if they appear late in source material or are implicit rather than explicit","Extraction accuracy depends on text clarity — ambiguous or poorly formatted source text leads to errors","No multi-document summarization without external aggregation logic"],"requires":["OpenRouter API key","Source text (document, article, conversation) as input","Optional: summary length or format specification (bullet points, JSON, etc.)"],"input_types":["text (documents, articles, conversations, transcripts)"],"output_types":["text (summaries, extracted entities, structured data, outlines)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_7","uri":"capability://text.generation.language.translation.and.multilingual.text.generation","name":"translation and multilingual text generation","description":"Translates text between languages and generates content in non-English languages by learning multilingual patterns from training data. The model preserves meaning, tone, and context-appropriate phrasing across language pairs, and can switch between languages within a single response.","intents":["Translate content between major languages (English, Spanish, French, German, Chinese, etc.)","Generate multilingual responses or content in specified non-English languages","Assist with language learning by providing translations with explanations"],"best_for":["Global teams and organizations requiring multilingual content generation","Localization teams translating products and documentation","Language learners and educators using AI for translation assistance"],"limitations":["Translation quality varies by language pair — performs better for high-resource languages (Spanish, French) than low-resource languages","Struggles with idioms, cultural references, and context-dependent phrasing that don't translate literally","No access to domain-specific terminology databases — may use generic translations for specialized terms","Cannot maintain consistent terminology across multiple documents without external glossary"],"requires":["OpenRouter API key","Source text and target language specification","Optional: domain context or terminology glossary for specialized translation"],"input_types":["text (content to translate, language pair specification)"],"output_types":["text (translated content, multilingual responses)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_8","uri":"capability://text.generation.language.logical.reasoning.and.mathematical.problem.solving","name":"logical reasoning and mathematical problem-solving","description":"Solves mathematical problems and performs logical reasoning by learning to apply mathematical rules, algebraic manipulation, and logical inference patterns from training data. The model generates step-by-step solutions, explains reasoning, and handles problems ranging from arithmetic to calculus and symbolic logic.","intents":["Solve math problems (algebra, geometry, calculus) with step-by-step explanations","Perform logical reasoning and formal proof generation","Debug mathematical or logical errors in student work or code"],"best_for":["Educational platforms providing math tutoring and homework assistance","Researchers and engineers solving mathematical problems as part of larger workflows","Students learning mathematics with AI-assisted explanation and verification"],"limitations":["Struggles with very complex multi-step problems requiring deep mathematical insight","May make algebraic errors or lose track of variables in long derivations","Cannot verify solutions against external mathematical databases or symbolic solvers","Performance degrades for problems requiring specialized mathematical knowledge (advanced topology, abstract algebra)"],"requires":["OpenRouter API key","Mathematical problem or logical statement as text input","Optional: problem context, constraints, or hints for improved accuracy"],"input_types":["text (mathematical problems, logical statements, equations)"],"output_types":["text (step-by-step solutions, proofs, explanations, verified answers)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-20b__cap_9","uri":"capability://tool.use.integration.api.compatible.inference.with.openrouter.integration","name":"api-compatible inference with openrouter integration","description":"Exposes model inference through OpenRouter's API, providing OpenAI-compatible endpoints that accept standard chat completion requests and return structured responses. Integration handles authentication, rate limiting, request routing, and response formatting without requiring direct model deployment.","intents":["Access gpt-oss-20b through standard OpenAI API clients without modifying application code","Integrate the model into existing LLM applications that expect OpenAI-compatible endpoints","Switch between different models (OpenAI, Anthropic, open-source) using the same API interface"],"best_for":["Developers with existing OpenAI-based applications seeking cost-effective alternatives","Teams evaluating multiple models without refactoring application code","Organizations requiring vendor-agnostic LLM integration"],"limitations":["OpenRouter API adds ~50-200ms latency overhead compared to direct model inference","Rate limiting and quota management depend on OpenRouter's infrastructure and pricing tier","No streaming response support (if OpenRouter doesn't expose it for this model)","Requires internet connectivity — cannot run offline or in air-gapped environments"],"requires":["OpenRouter API key (obtain from https://openrouter.ai)","HTTP client library (curl, requests, axios, etc.) or OpenAI SDK configured for OpenRouter endpoint","Network connectivity to OpenRouter infrastructure"],"input_types":["JSON (OpenAI-compatible chat completion request format)"],"output_types":["JSON (OpenAI-compatible chat completion response format)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key or direct model access via compatible inference framework","CUDA 11.8+ for GPU inference, or sufficient CPU memory (>40GB) for CPU-only inference","Inference framework supporting MoE routing (vLLM, TensorRT-LLM, or similar with expert parallelism support)","OpenRouter API key or compatible inference endpoint","Client-side conversation history management (array of message objects with role and content)","Token counting utility to track context window usage (e.g., tiktoken for OpenAI-compatible models)","OpenRouter API key","Code context (file snippets, error messages, or problem descriptions) as text input","Optional: syntax highlighter or IDE integration for formatting generated code","Question or topic as natural language text input"],"failure_modes":["MoE routing adds ~5-15ms latency overhead per forward pass due to gating computation and expert selection","Sparse activation patterns may reduce performance on tasks requiring dense cross-expert knowledge fusion","Load balancing across experts can create uneven GPU utilization if token distribution skews toward fewer experts","Fine-tuning MoE models requires careful handling of expert collapse (all tokens routing to same expert)","Fixed context window (typically 4K-8K tokens) limits conversation length before older turns are lost or must be summarized","No persistent memory across sessions — each new conversation starts without prior context","Long conversation histories increase per-token latency due to larger attention computation","Context window exhaustion requires manual conversation pruning or external memory integration","Code generation quality varies by language — performs better on Python/JavaScript than niche languages","No real-time code execution or validation — generated code may have runtime errors not caught by syntax analysis","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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":"2026-05-03T15:20:45.776Z","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-oss-20b","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-oss-20b"}},"signature":"+QwCHv0ym4RJ8Auc/CFrapHpQnDC60hLmfBqNwzYjt5HM5joLTdkI6iQ9ekJYCsei7jcD44aDbL6IQoXLHDJAg==","signedAt":"2026-06-21T04:14:16.865Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-oss-20b","artifact":"https://unfragile.ai/openai-gpt-oss-20b","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-oss-20b","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"}}