{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemma-2-27b-it","slug":"google-gemma-2-27b-it","name":"Google: Gemma 2 27B","type":"model","url":"https://openrouter.ai/models/google~gemma-2-27b-it","page_url":"https://unfragile.ai/google-gemma-2-27b-it","categories":["chatbots-assistants"],"tags":["google","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$6.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemma-2-27b-it__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.instruction.following","name":"multi-turn conversational reasoning with instruction-following","description":"Gemma 2 27B implements a transformer-based architecture trained on instruction-tuned data to maintain context across multi-turn conversations while following explicit user directives. The model uses standard transformer attention mechanisms with optimized inference patterns to process conversation history and generate contextually appropriate responses, leveraging Google's research into alignment and instruction-following from Gemini model development.","intents":["Build a chatbot that understands multi-turn context and remembers previous exchanges","Create an assistant that follows complex, multi-step instructions across conversation turns","Develop an interactive Q&A system that maintains coherent reasoning across dialogue","Implement a customer support agent that tracks conversation state and user intent"],"best_for":["Teams building conversational AI products with moderate computational budgets","Developers deploying open-source chatbots on self-hosted infrastructure","Builders prototyping multi-turn dialogue systems before scaling to larger models"],"limitations":["Context window limited to model's training sequence length (typically 8K-16K tokens), requiring conversation pruning for very long dialogues","No native memory persistence — conversation history must be managed externally between API calls","Instruction-following quality degrades on highly specialized domain tasks without fine-tuning","Inference latency scales linearly with context length; longer conversations incur proportional latency penalties"],"requires":["API access via OpenRouter or compatible inference provider","Valid API key for authentication","HTTP client library for REST API integration","Conversation state management system (in-memory or external database)"],"input_types":["text (natural language queries and instructions)","conversation history (array of message objects with role and content)"],"output_types":["text (natural language responses)","structured reasoning traces (when prompted for step-by-step thinking)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_1","uri":"capability://code.generation.editing.code.understanding.and.generation.with.language.agnostic.patterns","name":"code understanding and generation with language-agnostic patterns","description":"Gemma 2 27B can analyze and generate code across multiple programming languages by leveraging transformer-based pattern recognition trained on diverse code corpora. The model identifies syntactic and semantic patterns in code snippets, understands variable scope and control flow, and generates syntactically valid code completions or refactorings without language-specific parsing rules, relying instead on learned representations of programming constructs.","intents":["Generate boilerplate code or function implementations from natural language descriptions","Analyze code snippets to explain logic, identify bugs, or suggest optimizations","Translate code between programming languages while preserving intent","Complete partial code with context-aware suggestions for multiple languages"],"best_for":["Solo developers seeking code generation assistance without specialized IDE plugins","Teams building polyglot systems needing cross-language code understanding","Educators creating programming tutorials with AI-assisted code examples"],"limitations":["No AST-based structural awareness — relies on token-level patterns, leading to occasional syntax errors in complex nested structures","Limited to code patterns present in training data; novel or domain-specific languages may generate lower-quality output","No built-in type checking or semantic validation; generated code requires manual review for correctness","Context window constraints limit analysis to code snippets under ~4K tokens; large files require chunking"],"requires":["API access via OpenRouter","Valid API key","Code context provided as text input (no binary or compiled code support)"],"input_types":["text (code snippets, natural language descriptions of desired code)","code comments and docstrings for context"],"output_types":["text (generated code in requested language)","explanations of code logic","refactoring suggestions"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_10","uri":"capability://text.generation.language.constraint.based.text.generation.with.format.enforcement","name":"constraint-based text generation with format enforcement","description":"Gemma 2 27B generates text that adheres to specified constraints (length limits, format requirements, structural patterns) by learning to respect constraints through prompting and guided generation. The model uses attention mechanisms to track constraint satisfaction during generation, enabling production of structured outputs like JSON, lists, or formatted documents without explicit constraint solvers or grammar-based generation.","intents":["Generate JSON or structured data from natural language specifications","Create formatted documents (emails, reports, invoices) with consistent structure","Produce lists, tables, or other structured outputs with specified column counts or row limits","Generate code snippets with specific function signatures or API contracts"],"best_for":["Systems requiring structured output from language models","Automation pipelines that need consistent formatting","Data generation for testing or training purposes"],"limitations":["Constraint satisfaction is not guaranteed; model may violate length limits, format requirements, or structural patterns","Complex constraints (e.g., 'JSON with exactly 5 fields of specific types') may be partially satisfied or ignored","No validation of generated output; produced structures may be syntactically invalid (e.g., malformed JSON)","Constraint enforcement adds latency; longer constraints or more complex requirements increase generation time"],"requires":["API access via OpenRouter","Valid API key","Clear specification of desired format and constraints in prompt"],"input_types":["text (natural language description of desired output)","text (format specification or example output)"],"output_types":["text (generated output in specified format)","structured data (JSON, CSV, or other formats, with explicit prompting)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_2","uri":"capability://text.generation.language.summarization.and.information.extraction.from.long.form.text","name":"summarization and information extraction from long-form text","description":"Gemma 2 27B performs abstractive and extractive summarization by processing long text sequences through its transformer encoder-decoder architecture, identifying salient information patterns, and generating condensed representations. The model learns to compress information by recognizing key entities, relationships, and concepts, then reconstructing them in shorter form while preserving semantic meaning and factual accuracy.","intents":["Summarize research papers, articles, or documentation into concise overviews","Extract key facts, dates, and entities from unstructured text documents","Generate executive summaries of meeting transcripts or reports","Condense long customer feedback into actionable insights"],"best_for":["Content teams processing high volumes of text for knowledge management","Researchers synthesizing information from multiple sources","Business analysts extracting insights from unstructured data"],"limitations":["Abstractive summarization may hallucinate details not present in source text, requiring fact-checking","Performance degrades on highly technical or domain-specific jargon without domain-specific fine-tuning","Context window limits summarization to documents under ~8K tokens; longer documents require chunking and multi-pass processing","No structured output format enforcement — extracted entities may be inconsistently formatted across requests"],"requires":["API access via OpenRouter","Valid API key","Text input in UTF-8 encoding"],"input_types":["text (articles, documents, transcripts, reports)","optional instructions specifying summary length or focus areas"],"output_types":["text (abstractive summaries)","extracted entities and facts (unstructured text)","structured outlines or bullet points (with explicit prompting)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_3","uri":"capability://text.generation.language.semantic.question.answering.over.unstructured.text","name":"semantic question-answering over unstructured text","description":"Gemma 2 27B performs reading comprehension by encoding question and document context through transformer self-attention, identifying relevant passages, and generating answers grounded in source material. The model learns to map question semantics to document content through cross-attention mechanisms, enabling it to answer questions that require reasoning over multiple sentences or paragraphs without explicit retrieval or ranking components.","intents":["Answer user questions about document content without requiring structured databases","Build FAQ systems that retrieve and synthesize answers from knowledge bases","Implement document-based search where answers are generated rather than ranked","Create interactive documentation systems that explain concepts from provided materials"],"best_for":["Teams building knowledge management systems with unstructured text sources","Support teams implementing AI-assisted customer service with documentation","Researchers analyzing text corpora for specific information patterns"],"limitations":["Answers are generated rather than retrieved, risking hallucination if question requires information not in provided context","No explicit confidence scoring — model may generate plausible-sounding but incorrect answers with high confidence","Context window limits document length to ~8K tokens; requires chunking and multi-pass retrieval for larger documents","Performance degrades on questions requiring numerical reasoning or precise calculations"],"requires":["API access via OpenRouter","Valid API key","Document text provided as context in prompt"],"input_types":["text (questions in natural language)","text (reference documents or passages to answer from)"],"output_types":["text (generated answers grounded in source material)","optional source citations (with explicit prompting)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_4","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.style.adaptation","name":"creative writing and content generation with style adaptation","description":"Gemma 2 27B generates original text content by learning stylistic patterns from training data and applying them to user-specified prompts. The model uses transformer-based language modeling to predict coherent token sequences that match specified tones, genres, or formats, enabling generation of marketing copy, creative fiction, technical documentation, and other content types through learned style representations.","intents":["Generate marketing copy, product descriptions, or ad headlines in specified tones","Create creative fiction, poetry, or storytelling content with consistent voice","Draft technical documentation or API descriptions from specifications","Produce social media content, email templates, or other formatted text"],"best_for":["Content creators and marketers seeking AI-assisted writing tools","Small teams without dedicated copywriting resources","Developers generating documentation or technical content at scale"],"limitations":["Generated content may lack originality or contain clichéd phrasing common in training data","Style adaptation is implicit and may not precisely match complex or niche style requirements","No fact-checking — generated content may contain false claims or outdated information","Longer content (>2K tokens) may lose coherence or consistency in voice across sections"],"requires":["API access via OpenRouter","Valid API key","Clear prompts specifying desired content type, tone, and format"],"input_types":["text (prompts describing desired content, style, and constraints)","optional examples of desired style or format"],"output_types":["text (generated content in specified format and style)","multiple variations (with explicit prompting for alternatives)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_5","uri":"capability://text.generation.language.translation.between.natural.languages.with.context.preservation","name":"translation between natural languages with context preservation","description":"Gemma 2 27B performs neural machine translation by encoding source language text through transformer layers and decoding into target language while preserving semantic meaning and context. The model learns language-pair mappings from multilingual training data, enabling translation across 50+ language pairs without language-specific translation modules, using shared transformer representations to bridge linguistic differences.","intents":["Translate user-generated content or documentation between languages at scale","Build multilingual chatbots that respond in user's preferred language","Localize software interfaces, marketing materials, or help documentation","Enable cross-language communication in global teams or customer support"],"best_for":["Global teams needing real-time translation without external translation services","Platforms serving multilingual user bases","Content teams localizing materials for international markets"],"limitations":["Translation quality varies significantly across language pairs; low-resource languages (e.g., Swahili, Icelandic) produce lower-quality output than high-resource pairs (e.g., English-French)","Idioms, cultural references, and context-dependent phrasing may be translated literally rather than semantically","No domain-specific terminology handling without fine-tuning; technical or specialized vocabulary may be mistranslated","Context window limits translation to documents under ~8K tokens; longer documents require chunking"],"requires":["API access via OpenRouter","Valid API key","Source language text in UTF-8 encoding","Explicit specification of target language"],"input_types":["text (source language content)","language pair specification (e.g., 'English to Spanish')"],"output_types":["text (translated content in target language)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_6","uri":"capability://planning.reasoning.logical.reasoning.and.step.by.step.problem.decomposition","name":"logical reasoning and step-by-step problem decomposition","description":"Gemma 2 27B performs multi-step reasoning by generating intermediate reasoning steps before producing final answers, using chain-of-thought prompting patterns learned during training. The model learns to decompose complex problems into simpler sub-problems, track state across reasoning steps, and validate intermediate conclusions, enabling it to solve problems requiring multiple logical inferences without explicit symbolic reasoning engines.","intents":["Solve math word problems by breaking them into calculation steps","Debug code by systematically tracing execution and identifying error sources","Answer complex questions requiring multi-hop reasoning across facts","Plan multi-step solutions to open-ended problems"],"best_for":["Educational applications requiring step-by-step problem solving","Debugging and troubleshooting systems that explain reasoning","Complex query systems requiring multi-hop inference"],"limitations":["Reasoning quality degrades on problems requiring >10 steps; longer chains accumulate errors","No formal verification of reasoning steps — intermediate conclusions may be logically invalid","Arithmetic and precise calculations remain error-prone; model may make computational mistakes despite correct reasoning structure","Reasoning steps are generated text, not executable logic; no guarantee of consistency with final answer"],"requires":["API access via OpenRouter","Valid API key","Prompts explicitly requesting step-by-step reasoning (e.g., 'Let's think step by step')"],"input_types":["text (problems or questions requiring reasoning)","optional context or constraints"],"output_types":["text (intermediate reasoning steps)","text (final answer or solution)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_7","uri":"capability://text.generation.language.sentiment.analysis.and.emotional.tone.classification","name":"sentiment analysis and emotional tone classification","description":"Gemma 2 27B classifies emotional tone and sentiment by learning to recognize linguistic patterns associated with positive, negative, or neutral sentiment. The model uses transformer attention to identify sentiment-bearing words, phrases, and contextual cues, then generates sentiment classifications or detailed emotional analysis without requiring explicit sentiment lexicons or rule-based classifiers.","intents":["Analyze customer feedback or reviews to identify satisfaction levels","Monitor social media sentiment for brand reputation tracking","Classify support tickets by emotional urgency or customer frustration","Detect sarcasm, irony, or nuanced emotional content in text"],"best_for":["Customer experience teams analyzing feedback at scale","Social media monitoring and brand reputation management","Support teams prioritizing tickets by emotional urgency"],"limitations":["Sarcasm and irony detection remains unreliable; model may classify sarcastic negative statements as positive","Cultural and linguistic nuances affect accuracy; sentiment expressions vary across languages and regions","No confidence scoring — model may classify ambiguous text with high confidence despite genuine uncertainty","Context window limits analysis to single messages; multi-message conversations require separate analysis per message"],"requires":["API access via OpenRouter","Valid API key","Text input in UTF-8 encoding"],"input_types":["text (reviews, feedback, social media posts, support messages)"],"output_types":["text (sentiment classification: positive/negative/neutral)","text (detailed emotional analysis with reasoning)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_8","uri":"capability://data.processing.analysis.entity.recognition.and.named.entity.extraction.from.unstructured.text","name":"entity recognition and named entity extraction from unstructured text","description":"Gemma 2 27B identifies and extracts named entities (persons, organizations, locations, dates, products) from unstructured text by learning entity patterns through transformer attention mechanisms. The model recognizes entity boundaries and types through learned representations without explicit entity gazetteers or rule-based pattern matching, enabling flexible entity extraction across diverse text domains.","intents":["Extract company names, people, and locations from news articles or documents","Identify product mentions and brand references in customer feedback","Parse dates, times, and temporal references from unstructured text","Build knowledge graphs by extracting entities and relationships from documents"],"best_for":["Information extraction pipelines processing unstructured documents","Knowledge graph construction from text sources","Content analysis and metadata extraction at scale"],"limitations":["Entity boundaries may be incorrectly identified; model may include extra words or miss parts of multi-word entities","Entity type classification is implicit and may be ambiguous (e.g., 'Apple' as company vs. fruit)","No confidence scoring per entity; model may extract non-entities with high confidence","Rare or domain-specific entities may be missed without domain-specific fine-tuning"],"requires":["API access via OpenRouter","Valid API key","Text input in UTF-8 encoding"],"input_types":["text (articles, documents, feedback, social media posts)"],"output_types":["text (extracted entities with types)","structured data (JSON with entity lists and types, with explicit prompting)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-2-27b-it__cap_9","uri":"capability://text.generation.language.semantic.similarity.and.paraphrase.detection","name":"semantic similarity and paraphrase detection","description":"Gemma 2 27B assesses semantic similarity between text pairs by encoding both inputs through transformer layers and comparing their learned representations. The model learns to recognize paraphrases, synonymous expressions, and semantically equivalent statements despite surface-level differences, enabling similarity scoring and paraphrase detection without explicit similarity metrics or hand-crafted features.","intents":["Detect duplicate or near-duplicate content in document collections","Identify paraphrased plagiarism or content reuse","Match user queries to similar historical questions in FAQ systems","Find semantically equivalent code snippets or documentation sections"],"best_for":["Content moderation and plagiarism detection systems","Deduplication pipelines for document collections","FAQ and knowledge base systems with semantic matching"],"limitations":["Similarity scoring is implicit and not calibrated to standard metrics (e.g., cosine similarity); threshold selection requires empirical tuning","No explicit confidence bounds; model may rate dissimilar texts as similar if they share surface-level vocabulary","Context window limits comparison to text pairs under ~8K tokens combined","Similarity assessment may be biased by training data; uncommon language patterns may be rated as dissimilar despite semantic equivalence"],"requires":["API access via OpenRouter","Valid API key","Two text inputs for comparison"],"input_types":["text (first text for comparison)","text (second text for comparison)"],"output_types":["text (similarity assessment: similar/dissimilar/paraphrase)","text (explanation of similarity reasoning)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or compatible inference provider","Valid API key for authentication","HTTP client library for REST API integration","Conversation state management system (in-memory or external database)","API access via OpenRouter","Valid API key","Code context provided as text input (no binary or compiled code support)","Clear specification of desired format and constraints in prompt","Text input in UTF-8 encoding","Document text provided as context in prompt"],"failure_modes":["Context window limited to model's training sequence length (typically 8K-16K tokens), requiring conversation pruning for very long dialogues","No native memory persistence — conversation history must be managed externally between API calls","Instruction-following quality degrades on highly specialized domain tasks without fine-tuning","Inference latency scales linearly with context length; longer conversations incur proportional latency penalties","No AST-based structural awareness — relies on token-level patterns, leading to occasional syntax errors in complex nested structures","Limited to code patterns present in training data; novel or domain-specific languages may generate lower-quality output","No built-in type checking or semantic validation; generated code requires manual review for correctness","Context window constraints limit analysis to code snippets under ~4K tokens; large files require chunking","Constraint satisfaction is not guaranteed; model may violate length limits, format requirements, or structural patterns","Complex constraints (e.g., 'JSON with exactly 5 fields of specific types') may be partially satisfied or ignored","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.24,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:24.484Z","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=google-gemma-2-27b-it","compare_url":"https://unfragile.ai/compare?artifact=google-gemma-2-27b-it"}},"signature":"8sQcHiODs7YJJ9ngHqNOIVnBZoqaAur2Y0lumZO+07db2VqLqyRpjlaghDykkpQZ4IEQFG06Powlpe+LlEdOBw==","signedAt":"2026-06-15T17:25:32.707Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemma-2-27b-it","artifact":"https://unfragile.ai/google-gemma-2-27b-it","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemma-2-27b-it","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"}}